Cognitive Preference and Spelling Difficulties

Cognitive Preference and Spelling Difficulties

A thesis submitted to the University of Manchester in part fulfilment of the requirements for the degree

of Doctorate in Educational Psychology in the Faculty of Education

2003

Garry Squires

Department of Education

List of Contents List of Contents..............................................................................................................2 List of Figures ................................................................................................................5 List of Tables .................................................................................................................6 Abstract ..........................................................................................................................7 Declarations ...................................................................................................................8 Acknowledgements........................................................................................................9 The Author .....................................................................................................................9 Chapter 1 Introduction to the project ...........................................................................10

1.1 Introduction........................................................................................................10 1.2 The contribution made by this project ...............................................................12 1.3 Cognitive modelling...........................................................................................15 1.4 Cognitive preference ..........................................................................................16 1.5 Limits of the current study.................................................................................18 1.6 Overview of the methodology used ...................................................................20 1.7 Organisation of the thesis...................................................................................22

Chapter 2 The contribution of neuropsychology to understanding cognitive preference......................................................................................................................................24

2.1 Introduction........................................................................................................24 2.2 Methods and techniques for studying brains .....................................................25 2.3 Brain differentiation – the link between morphology and function...................28 2.4 Areas of brain considered to be involved in dyslexia ........................................35 2.5 Difficulties with previous studies ......................................................................40 2.6 Conclusions and implications for the current study...........................................45

Chapter 3 Sensory processing......................................................................................47 3.1 Introduction........................................................................................................47 3.2 The auditory channel and the phonological deficit hypothesis..........................48 3.3 The visual channel and the visual deficit hypothesis.........................................55 3.4 The magnocellular and parvocellular systems...................................................58 3.5 Transient magnocellular deficit hypothesis .......................................................63 3.6 Implications for the current study ......................................................................66

Chapter 4: Cognitive profiles and preferences derived from psychometric analyses..67 4.1 Introduction........................................................................................................67 4.2 Profiles with the WISC ......................................................................................69 4.3 The BAS and DAS.............................................................................................71 4.4 Examples of cognitive profile studies using the BAS and DAS........................76 4.5 Recent studies using the BAS and DAS ............................................................81 4.6 Profiles in pupils referred to the author .............................................................85 4.7 Other psychometric approaches .........................................................................92 4.8 Critique of methodology used by different researchers .....................................92 4.9 Implications for the current study ......................................................................95

Chapter 5 Summary of literature review and research questions ................................97 5.1 Introduction........................................................................................................97 5.2 The focus of the investigations in this study....................................................100 5.3 Cognitive preferences ......................................................................................102 5.4 The research questions .....................................................................................103

Chapter 6: Methodology ............................................................................................105 6.1 Introduction and overview ...............................................................................105

6.2 Selection of the participants.............................................................................106 6.3 Teacher questionnaire methodology ................................................................109

6.3.1 Design of the questionnaire ......................................................................109 6.3.2 Analysis of the pilot questionnaire ...........................................................114

6.4 Q-sort methodology .........................................................................................117 6.4.1 Design of the Q-sort..................................................................................117 6.4.2 Administration of Q-sort...........................................................................119 6.4.3 Scoring procedure .....................................................................................119 6.4.4 Piloting the Q-sort.....................................................................................121

6.5 The learning experiment ..................................................................................122 6.5.1 Selection of words to be used ...................................................................123 6.5.2 Procedure for administration during the pilot study .................................126 6.5.3 General instructions ..................................................................................127 6.5.4 Testing.......................................................................................................128 6.5.5 Scoring ......................................................................................................128 6.5.6 Changes after the pilot study.....................................................................130

6.6 Analysis of spelling errors in naturalistic writing............................................131 6.7 Summary of the methodology..........................................................................133 6.8 Choice of data analysis ....................................................................................134

Chapter 7 Results .......................................................................................................136 7.1 Introduction......................................................................................................136 7.2 Inter measure reliability ...................................................................................138 7.3 BAS-II compared to pupil choices...................................................................140 7.4 BAS-II compared to teacher ratings ................................................................142 7.5 BAS-II compared to learning experiment........................................................148 7.6 Case Studies .....................................................................................................151

7.6.1 CM ............................................................................................................151 7.6.2 AW............................................................................................................152 7.6.3 JS...............................................................................................................153 7.6.4 JG ..............................................................................................................153 7.6.4 JM .............................................................................................................154 7.6.5 DS .............................................................................................................154 7.6.6 RH.............................................................................................................155 7.6.7 AK.............................................................................................................156 7.6.8 SH .............................................................................................................157 7.6.9 SJ...............................................................................................................157

7.7 Ecological Validity of BAS-II cognitive preferences ......................................158 Chapter 8 Discussion .................................................................................................161

8.1 Introduction......................................................................................................161 8.2 Summary of the results ....................................................................................163 8.3 Methodological issues......................................................................................167

8.3.1 Development of the main part of the project and pilot studies .................167 8.3.2 Sample recruited .......................................................................................172 8.3.3 Laboratory type learning experiment versus longer term teaching ..........176 8.3.4 Cued learning and recall and actual processing........................................178 8.3.5 Different types of measures used ..............................................................179 8.3.6 Modality based assessment .......................................................................184

8.4 Implications for assessment .............................................................................185 8.5 Implications for teaching .................................................................................191 8.6 Conclusions and implications for further research ..........................................199

References..................................................................................................................202 Appendix 1: Pilot Teacher Questionnaire..................................................................212 Appendix 2: Modified Teacher Questionnaire ..........................................................216 Appendix 3: Q-sort brainstorm questionnaire............................................................219 Appendix 4: Q-sort cards ...........................................................................................220 Appendix 5: Q-Sort Scoring sheet .............................................................................223 Appendix 6: Word cards for learning experiment .....................................................224 Appendix 7: S-Cue Card............................................................................................226 Appendix 8: V-Cue Card ...........................................................................................226 Appendix 9: Learning Experiment Record Sheet (version 1)....................................227 Appendix 10: Learning Experiment Record Sheet (version 2)..................................228 Appendix 11: Sentences for dictation ........................................................................229 Appendix 12: Summary of measures .........................................................................230 Appendix 13 Categories of spelling errors made by 7 pupils ....................................232

List of Figures Figure 1.1 Focus population for this study ..................................................................12 Figure 1.2 Major components used in cognitive models .............................................15 Figure 1.3 Possible links between literacy and cognitive preference ..........................17 Figure 2.1 Drawing to show the two hemispheres in relation to the head...................28 Figure 2.2 Left Hemisphere divided into lobes ...........................................................29 Figure 2.3 Left hemisphere showing the position of the sylvian fissure .....................30 Figure 3.1: Sensory processing channels .....................................................................47 Figure 3.2: Generalised developmental model of literacy acquisition. .......................54 Figure 3.3 Pathway of neurones from the retina to the visual cortex ..........................56 Figure 3.4 Neural Pathways in the visual system ........................................................59 Figure 3.5 Effects of dysfunctional magnocellular region...........................................64 Figure 4.1 Sample population for initial BAS-II profile analysis................................86 Figure 4.2 Cognitive profiles for children with spelling levels as predicted by their

GCA .....................................................................................................................88 Figure 4.3 Cognitive profiles for children with spelling levels significantly below

levels predicted from GCA ..................................................................................89 Figure 4.4 Cognitive profiles for children with reading levels commensurate with

predicted levels ....................................................................................................89 Figure 4.5 Cognitive profiles for children with reading levels significantly below

predicted levels ....................................................................................................90 Figure 5.1 Ellis' model of cognitive processes involved in recoding language when

reading or spelling..............................................................................................101 Figure 5.2 The link between neurology, cognitive preference and possibilities for

assessment..........................................................................................................103 Figure 6.1 Sample of pupils for the main study.........................................................108 Figure 7.1 Construct Map for dyslexia .....................................................................145 Figure 7.2 Construct Map for literacy difficulties ....................................................146 Figure 7.3 The proportion of spelling errors made by BAS-II preference ................159 Figure 8.1: Different types of measures used ............................................................181 Figure 8.2: Systematic approach to assessment of spelling.......................................191 Figure 8.3: Factors influencing selection of cognitive processing ............................194 Figure 8.4: Extending the developmental model .......................................................196

T

List of Tables Table 2.1: Summary of techniques used to explore brain function and anatomy........27 Table 2.2 Hemisphere with speech centres and handedness........................................29 Table 2.3 Areas of brain implicated in dyslexia. ........................................................38 Table 3.1 Developmental model derived from Treiman..............................................52 Table 3.2 Development of ocular stability...................................................................57 Table 3.3 Gain in reading ability with ocular occlusion..............................................57 Table 3.4 Effect of coloured filters on ocular control..................................................58 Table 4.1: Cognitive profiles derived in studies using the BAS-R..............................76 Table 4.2 Cognitive profiles found using the DAS .....................................................78 Table 4.3 Correlations for attainment and ability for the DAS....................................79 Table 4.4 Mean correlations for attainment and ability for the BAS-II.......................80 Table 4.5 DAS/BAS-II profiles identified by Elliott ...................................................82 Table 4.6 Profiles found in different populations .......................................................83 Table 4.7 Mathematical representation of Elliott’s subgroups used by the author......85 Table 4.8 Composition of SEN sample referred to author...........................................87 Table 4.9 Percentages of each group of children assessed by the author ...................87 Table 4.10 Percentages of each group of children assessed by the author ..................88 Table 4.11 Variation in decision points for defining cognitive profiles ......................94 Table 6.1 Composition of sample used in main study ...............................................106 Table 6.2 Question items contributing towards each area of cognitive processing...113 Table 6.3 Question items contributing towards each area of cognitive processing...115 Table 6.4 responses received from practising educational psychologists..................117 Table 6.5 Items with the highest consensus chosen for the q-sort.............................118 Table 6.6 The number of cards selected for each layer .............................................119 Table 6.7 Scores allocated to each layer ....................................................................120 Table 6.8 Words available on the Boder reading test ................................................124 Table 6.9 Words selected for the learning experiment ..............................................125 Table 6.10 Alternative word lists considered ............................................................126 Table 6.11 Composition of sample used for naturalistic spelling analysis................131 Table 6.12 Summary of writing types by developmental sequence ..........................132 Table 7.1 Percentage of agreements between categories of pupil identified .............139 Table 7.2 Distribution of cognitive preferences identified by different measures ....139 Table 7.3 Relationship between BAS-II and Q-sort category identification .............140 Table 7.4 Agreement between BAS-II and teacher identified cognitive categories..142 Table 7.5 Teacher comments related to cognitive processing ...................................144 Table 7.6 Constructs used by teachers .......................................................................147 Table 7.7 Relationship between BAS-II profile and performance when learning.....149 Table 7.8 Recruitment ages for each condition .........................................................149 Table 7.9 Ages for each condition based on performance.........................................150 Table 7.10 Mean improvement during learning across the sample ...........................150 Table 7.11 Summary of error types in handwriting samples .....................................159 Table 8.1 Ratio of cognitive preference found in other studies .................................173

Abstract This study draws upon research from a number of fields to investigate the role of cognitive preference on spelling remediation. A cognitive preference is considered to be a relative strength in processing one type of information compared to processing another type of information. In this study a preference for auditory-verbal processing or visuo-spatial processing is explored. A preliminary study involving a sample of 99 pupils referred for Statutory Assessment was analysed with approximately 20% being found to have one preference or the other. A small-scale study involving 17 pupils aged between 8 and 11 years was conducted to explore the possibility of identifying cognitive preference through a diagnostic questionnaire, q-sort methodology of choices of pastime activities and using the BAS-II. Some agreement was found between the three approaches. A learning experiment was conducted with the same pupils to see whether a verbal cue or a visuo-spatial cue would lead to improved learning of letter position in graded spellings. This indicated that the BAS-II was the most predictive of the 3 assessment methods and correctly identified the best learning method for 12 pupils (Fischer’s Exact Test, p=0.032). A measurable difference was found after only five learning trials that suggests that the use of modality based learning improves performance when matched to cognitive preference (Wilcoxon, p=0.038). Observational data collected suggests that other factors also influenced learning during the learning experiment (attentional focus, motivation, social priming). Further study involving the use of naturalistic samples of handwriting seemed to indicate that cognitive preference was identifiable in the types of errors made for a further sample of 7 pupils (Chi Squared, p=0.013).

Declarations No part of the work referred to in this thesis has been submitted in support of the application for another degree or qualification of this or any other university or institute of learning.

(1) Copyright in text of this thesis rests with the Author. Copies (by any process) either in full, or of extracts, may be made only in accordance with instructions given by the Author and lodged in the John Rylands University Library of Manchester. Details may be obtained from the Librarian. This page must form part of any such copies made. Further copies (by any process) of copies made in accordance with such instructions may not be made without the permission (in writing) of the Author.

(2) The ownership of any intellectual property rights which may be described in this thesis is vested in the University of Manchester, subject to any prior agreement to the contrary (excepting use by the Author), and may not be made available to use by third parties without the written permission of the University, which will prescribe the terms and conditions of any such agreement.

Further information on the conditions under which disclosures and exploitation may take place is available from the Head of the Department of Education.

Acknowledgements The writer would like to thank all those who have given their time and support in the completion of this project. The participating pupils remain anonymous, as do their schools and teachers, but without their co-operation, the collection of data for this study would not have been possible. I would like to acknowledge the support of Colin Elliot through e-mail and discussion regarding the use of the BAS-II as a psychometric test capable of detecting reliable cognitive profiles. I am grateful for his support and provision of data from his own research into the executive control function deficit hypothesis. Special thanks are given to Rea Reason. As my supervisor, she has presented alternative viewpoints, challenges and reflective questions that have helped to clarify my thinking. The writer would also like to thank Staffordshire local education authority for funding half of the doctorate and providing an opportunity to study at the University of Manchester.

The Author The author works as a chartered educational psychologist for Staffordshire LEA and is an associate fellow of the British Psychological Society (AFBPsS, CPsychol). Previous qualifications include:

• B.Ed (Hons) awarded from Lancaster University in 1982. This was a joint honours degree in Educational Studies, Biological Sciences, Science Education and the Theory and Practice of Teaching.

• B.Sc (Hons) awarded by the Open University in 1994 in Psychology. • Dip Psych – a post graduate diploma in psychology awarded by the Open

University in 1994. • M.Sc. in Educational Psychology awarded by Manchester University in 1996.

This course was the professional training course required to practice as an educational psychologist.

Chapter 1 Introduction to the project

1.1 Introduction

Reading and spelling are skills that are highly valued in our society and a great deal of

effort is expended to make sure that children develop these skills to a high standard.

For some children, these efforts are not rewarded and they fail to develop the skills or

develop them to an incomplete level. These children find that reading and spelling

remain hard and this reduces their ability to participate in the curriculum and may

limit opportunities for the rest of their life. Attempts to mitigate against this involves a

graduated response from teachers to provide support and remediation in line with the

Special Educational Needs Code of Practice (DfES, 2001).

Good remediation requires a good understanding of the nature of literacy difficulties

and the purpose of this project is to explore how an understanding of within-child

factors can contribute to selecting approaches to remediation. It starts with an

assumption that children are different in the way in which they process information

and this might influence the way in which they learn.

The need to understand reading and spelling has led to research being carried out in

several diverse areas. Several theoretical perspectives influence this thesis, each with

its own distinct area of research and its own specialist vocabulary. These include:

information processing theory, neuropsychology, experimental psychology,

constructivist approaches, psychometric approaches, developmental models and,

reading and spelling research.

The attempt to link together the different perspectives is made possible by thinking

about how information is processed and using cognitive models as explanatory tools.

1.2 The contribution made by this project

This project is concerned with investigating how the assessment of pupils who find

the acquisition of spelling skills difficult can lead to understanding how the way that

they process information can inform choice of remediation. The project is not

concerned with the wider ‘normal’ population, though comparisons are made with this

population whenever possible. It will be concerned with those pupils who have a

special educational need involving spelling and who have an identifiable cognitive

preference. This will affect the choice of pupils used in the sample:

Figure 1.1 Focus population for this study (not to scale)

Normal population (100% of pupils)

Pupils with Special Educational Needs (20%)

Spelling difficulty

Pupils with a cognitive preference

The project is unique in that it attempts to bring together background research from

different theoretical backgrounds to provide possible explanations of cognitive

processing and cognitive preference. This is then linked to different methods of

assessment that could be used to identify cognitive preference amongst poor spellers.

This includes the use of:

• Psychometric data from the British Ability Scales (BAS-II).

• Pupil perceptions of preferences of choice of past-time activities.

• Teacher perceptions of performance of different skills in literacy based

activities.

• Pupil performance when different types of cues are used to aid recall of

learning of spelling.

• Handwriting samples to analyse the types of errors made when writing.

This study is concerned with trying to identify two particular subtypes of poor readers

or poor spellers using different assessment methods. This is then used to see if this

helps to select a method of cueing recall to help the pupil improve spelling accuracy.

In principle this could be used as the basis for devising interventions to support

remedial teaching.

This leads to a number of questions:

• How pervasive are cognitive preferences and to what extent do the different

assessment methods agree with each other? Does the categorisation of pupils

on the basis of BAS-II cognitive profiles into subgroups that have either verbal

strengths or spatial strengths reflect:

o Pupil choices of activities from a limited selection of 20 activities

presented that roughly corresponds to activities that involve spoken

language or visuospatial activities?

o Teacher perceptions of approaches to literacy measured through a

questionnaire that asks for ratings of performance on reading and

writing subtasks, which on face validity, depend on either verbal skills

or visuospatial features?

o Subsequent performance on a learning experiment in which recall of

spellings is cued by sound or by visual features of the target letters?

• Which of the assessment strategies is the best predictor of pupil performance

on the learning experiment?

• Do pupils with particular profiles identified on the BAS-II tend to make less

spelling errors consistent with their cognitive preferences?

1.3 Cognitive modelling

The basic components of any cognitive model include a mechanism for inputting

information from the senses, a method of manipulating and storing the information

and some form of output or way of responding (see Figure 1.2). It is possible to create

theoretical models of how reading and spelling might proceed and this methodology is

useful for testing out how complex processes might be carried out.

Figure 1.2 Major components used in cognitive models

Cognitive models are limited because they are simply representations of what might

be happening and other representations could also explain the same facts. However,

they are useful in allowing the relationships between different facts to be explored. In

this study, they are used to help explore the ways in which textual information might

be decoded and how this might help with spelling.

INPUT PROCESSES Information from senses

STORAGE PROCESSES Manipulation of information

OUTPUT PROCESSES Responses made to

information

1.4 Cognitive preference

A cognitive model for reading or spelling must take into account the nature of the

task. Reading requires that a pattern of light reflected off text on the page is

adequately detected and somehow linked to word or letter sounds. These are used to

generate speech (if the text is being read out loud) and to access meaning (for the

reader to make sense of what is read). Similarly in spelling, the word is heard (either

by dictation or from the ‘inner voice’) and appropriate letters found and written.

These are complex tasks that experienced readers and writers take for granted.

Human evolution has allowed the brain to become specialised and to develop different

components to process different types of information. Reading and writing are

relatively new developments in evolutionary terms and it is unlikely that specific

‘reading centres’ or ‘spelling centres’ have evolved in the brain. This means that

existing processing units must take on the tasks of reading and writing. Two major

sensory processing routes are involved in both reading and writing (see Figure 1.3).

The visual route deals with visuo-spatial components (i.e. the sight, shape, colour, size

and orientation of letters on the page) and the verbal route deals with auditory-verbal

components (i.e. sound, phonemes, word meanings and definitions).

It could be argued that if one route works more efficiently than another route then it is

possible that the person will prefer to make sense of the world around them using this

type of processing. In this thesis this will be referred to as a cognitive preference. For

convenience the visuo-spatial processing route will be abbreviated to visual

processing while the auditory-verbal route will be abbreviated to verbal processing.

These information processing routes will be considered more fully through reference

to the existing literature in Chapters 2, 3 and 4.

Figure 1.3 Possible links between literacy and cognitive preference

The idea of cognitive preference is contrary to the monoaetiological perspective (Van

der Wissel, 1987) in that it proposes a number of reasons for failure to acquire literacy

skills at an adequate pace. This debate has a research history that has focussed on the

question of whether or not dyslexic children are fundamentally different from

‘common-garden poor readers’ (Elliott 1989, Stanovich 1991). Understanding of

cognitive preference allows a consideration of processing strengths and weaknesses

TEXT Grapheme

SOUND Phoneme

General visuo-spatial processing components

General phonological processing components

Reading and Spelling

These components help the child make sense of their auditory world

These components help the child make sense of their visual world

that can be applied to the assessment of children’s difficulties with reading and

spelling.

The child’s cognitive preference should arise out of common processing units in one

sensory modality and as such impinge upon all areas of their life. It should therefore

be detectable using a range of approaches. If this is true, then different approaches

used to assess cognitive preference should agree with each other. One of the aims of

this thesis is to explore whether different sources of evidence can be used in the

generation of hypotheses about a child’s cognitive preference that could lead to

interventions to include in Individual Education Plans.

1.5 Limits of the current study

For experienced readers, decoding textual information is seemingly effortless and

automatic. Somehow they are able to combine visual and verbal information and

relate this to previously stored knowledge to allow them to comprehend what is being

read. This study will not consider how competent readers or spellers are able to

complete this task. The focus of the background research and the studies is on those

children who have difficulty with reading and spelling.

The study will draw upon research (mainly concerned with literacy) to explore

spelling difficulties. The focus will be on identifying verbal and visual cognitive

preferences. It is recognised that other models can also explain literacy difficulties.

For instance, Nicholson and Fawcett’s hypothesis suggests that fluent reading does

not occur because of more general problems with automaticity and cerebellar function

(Nicholson, Fawcett and Dean, 1995; Nicholson et al, 1999; Fawcett and Nicholson,

1999).

The level of exploration used in the project is at the word level and in particular at the

level of letters and letter sequences. Written English is non-transparent in that

individual letters do not match onto individual sounds (there is not a common

grapheme-phoneme correspondence). For instance, the sounds of individual letters are

changed according to the surrounding letters (Dombey, 1999). The same letter

combination can have different sounds and, the same sounds can be represented by

different letters. In the more extreme cases, two differently pronounced words may

have the same spelling but the reader can only determine which to use by making

sense of the whole sentence. The reader and speller have to learn to deal with this

added complexity.

Some authors (e.g. see Stanovich and Stanovich, 1997), suggest that the phonological

deficit model applies to all children with reading and spelling difficulties. Yet this

does not seem to match the present author’s observations from clinical practice.

Although the BAS-II does not measure phonological ability directly, there is an

assumption that higher order language skills are reliant on lower order auditory skills

or phonological processing (Elliot et al, 1997). When the BAS-II is used some

children do appear to have deficits in verbal skills consistent with a phonological

weakness. But other children with reading difficulties do not seem to have a

phonological deficit – their difficulties might be with visual processing or spatial

reasoning. These two groups are explored in this study. However there are other

cognitive profiles that are not explored, for example children who have difficulty with

integrating information form visual and verbal modalities (i.e. they have low scores

on the BAS-II non-verbal scales).

This study is in essence an exploratory study. It uses small samples of pupils to see

whether an understanding of cognitive processes is useful in considering how to help

children with difficulties in learning to spell. These children are unusual in that they

find the acquisition of literacy skills difficult. This means that any findings from the

study cannot be generalised to the wider population of pupils participating in Key

Stage 2 of the National Literacy Strategy.

1.6 Overview of the methodology used

Three different samples of pupil data have contributed to different parts of this study.

An archival sample of BAS-II scores is examined from 99 pupils who have been

assessed previously to see whether there are different cognitive profiles within the

population of pupils referred to the County Psychological Service. These pupils are

drawn from the 20% of pupils with Special Educational Needs shown in Figure 1.1.

This project then sets out to explore four assessment techniques with 17 pupils aged

between 8:0 and 11:0. These pupils are from a different sample and represent those

with SEN, spelling difficulties and identifiable cognitive preferences (see Figure 1.1)

This part of the study includes:

• The use of psychometric assessment to identify types of cognitive preference

based on whether verbal reasoning is significantly better then spatial reasoning

or vice versa.

• The use of a teacher questionnaire to explore whether teachers notice the types

of errors that children make and whether this can be used to categorise

cognitive preferences.

• The use of q-sort methodology to ask pupils about the types of pastime

activities that they like or dislike to see if this falls into the two types of


• The use of a learning experiment to investigate whether one type of perceptual

cueing is better for particular pupils while a different cue is better for others.

Comparisons of the categories of cognitive preference identified by each assessment

technique should allow the merits of each of the first three methods to be explored

with regard to utility in selecting learning style for pupils.

The extent to which the results of the assessments might have ecological validity is

explored through the use of analysis of spelling mistakes made by 7 further pupils in

naturalistic writing and categorised as being either verbal or visual errors. These

pupils are an opportunistic sample but from the same type of pupils used in the main

study (i.e. they have SEN, spelling difficulties and identifiable cognitive preferences –

see Figure 1.1).

1.7 Organisation of the thesis

The remainder of this thesis is divided into 3 chapters that cover the literature review

and considers existing research and the contribution made to understanding how

information is processed to enable the child to spell. This is then extended into the

research questions and methodology. An overview is shown below:

• Chapter 2 considers neuropsychological evidence and discusses how different

areas of the brain might be involved in processing information required for

reading and spelling.

• Chapter 3 considers sensory processing with an emphasis on auditory-verbal

and visuo-spatial information.

• Chapter 4 reviews the development of psychometric assessment tools and

considers how well tests might be used to identify different cognitive

preferences that could underlie processes used in spelling. A sample of 99

pupils is considered to explore the existence of different profiles in children

with difficulties in learning to read and spell.

• Chapter 5 summarises the previous chapters through six major hypotheses that

can explain failure to acquire literacy skills. Two of these hypotheses are then

considered for exploration and this gives rise to the research questions

addressed by this thesis.

• Chapter 6 outlines the research design and describes the methodology. It

considers whether the assumption justified in Chapter 2 that cognitive

preferences are part of the nature of the child and are wide ranging in effect is

valid. This leads to an exploration of the potential methods of assessment and

detection of cognitive preference with a sample of 17 primary school children:

• The use of psychometrics to identify pupils as having either a

verbal cognitive preference or a spatial cognitive preference.

• The use of a teacher questionnaire to see whether teachers notice

the effects of cognitive preferences at the diagnostic level with

children referred as having literacy difficulties.

• Whether there are cognitive preferences in the choices that children

make for pastime activities are explored using a q-sort

methodology.

• This then leads to the development of a learning experiment to

explore whether one method of learning suits some children better

than a different method of learning and whether the opposite is true

for other children. In particular, whether the type of learning is

related to cognitive preference identified through each of the 3

assessment methods.

• Observations made at the time of the learning experiment are

considered as well as actual performance on the tasks presented.

This leads into considering the value of naturalistic samples of

writing as a method of providing qualitative data to see how

classroom performance might be indicative of cognitive preference.

• Chapter 7 outlines the results of these studies.

• Chapter 8 discusses the results and is divided into a summary of the findings;

consideration of methodological issues; consideration of implications for

assessment; consideration of implications for teaching; and considerations for

further research.

Chapter 2 The contribution of neuropsychology to understanding cognitive preference

2.1 Introduction

Although literacy skills seem automatic for a competent reader and speller they are

complex and involve many sub-processes. Cognitive modelling involves breaking

down complex processes into simpler information processing steps. Failure of a

sub-process results in poor performance of the skill or cognitive process being carried

out. The accuracy of the cognitive model is tested by how well it can predict or

explain the difficulties observed in human performance of the task modelled. In the

case of reading or writing this will involve trying to break the task down into smaller

steps for investigation and trying to identify single cognitive processes involved.

The extent to which different cognitive processes are involved is referred to as

dissociation. Two tasks are said to be dissociated if a person performs badly on one

task but well on the other. This could be because different cognitive processes are

involved or because one task is more demanding than the other and the difference is a

measure of performance of the same cognitive process. Double dissociation of

processes occurs when one person does well on one task and badly on another while

another person has the reverse pattern of performance. This implies that different

cognitive processes are involved and must be accounted for in the cognitive model.

(Springer and Deutsch, 1993; McCarthy and Warrington, 1990). In the current study

the assumption is being made that visual processing and verbal processing are doubly

dissociated and pupils can be identified who perform well in one area but less well in

the other. This is used to identify cognitive preference.

Many different cognitive models can be constructed to explain the same processes and

understanding can progress further if the models can be mapped onto physiological

processes. There has been a move to identify the anatomical structures involved in

order to test out predictions made from different cognitive models. It can be assumed

that damage to a particular area of brain or a lack of development of a particular area

has implications for the type of information that can be processed. Such a

neurological basis for developmental dyslexia has been argued by some authors (e.g.

Beardsworth and Harding; 1996). According to neurological theories an area of poor

neurological function results in deficient cognitive processing of information when

reading or spelling and leads to inadequate performance or learning.

This chapter outlines:

• the methods and techniques used for exploring anatomy and function;

• how recent studies are contributing to a general understanding of brain

differentiation;

• how this helps us to understand dyslexia and;

• the implications for the current study.

2.2 Methods and techniques for studying brains

Tony Buzan (2001) sets out the complexity of the human brain by comparing the

number of possible connections that neurones could make when engaging in

information processing to the number of atoms in the universe. He concludes that the

10800 possible connections could easily allow each of the 10100 atoms to be

represented individually. His metaphor immediately communicates the potential of

the brain to make sense of information reaching the senses.

Unfortunately, this is a rather simplistic way of thinking about the brain. The brain is

not an amorphous mass of undifferentiated tissue in which any of the thousand billion

or so neurones can communicate with any other. It is organised into a number of

centres, each with a set processing task.

The advantage of organising the brain in this way has been described by David Marr

(1976). He compared brains to computers and argued that there is a need to introduce

modularity as an aid to error correction. Large systems involving many connections

would require the whole system to be debugged and corrections made at many points

to improve performance whereas improvement can be made by making a smaller

number of corrections in modules that process part of the information. This means

that a differentiated brain is better for learning because small processes can be

tweaked easier.

A number of techniques are used to provide insight into how the brain is structured

and how it functions when given set processing tasks to do (see Springer and Deutsch,

1993; Fischbach, 1993, Rose, 1992) A summary of the techniques is shown in

Table 2.1.

Technique Brief Description Post Mortem Studies

The brains of people who behave differently from others are compared after death to see what structural differences are present in their brains compared to controls

Electroencephalogram (EEG) Electrodes are placed on the scalp and detect activity in the brain. EEG correlates of personality, intelligence and behaviour have been found (Springer and Deutsch, 1993)

Evoked Potentials (EP) In a normal EEG the activity of the brain in response to a stimulus is masked by background activity. A computer is used to measure the sequence of positive and negative changes from a baseline for 500 milliseconds after the specific stimulus ends. Parameters measured include:

• amplitude (voltage change) • latency (delay from stimulus onset to activity

onset) Magnetoencephalography (MEG)

A three dimensional image of neural activity is made by detecting the magnetic fields generated using a superconducting quantum interface device (SQUID)

Cerebral Angiography A dye is injected into the internal carotid artery and this allows blood flow through the brain to be detected and imaged using X-rays. The flow of blood gives an indication of the brain structure.

Computerised Tomography (CT Scanning)

X-rays are passed through the brain and absorption measured by an array of detectors while the source is revolved around a plane of the head. A computer is then used to re-construct an image of the slice of brain.

Magnetic Resonance Imaging (MRI scanning)

A fine grain cross sectional image of brain is produced without the use of penetrating radiation. The Nuclear Magnetic Resonance (NMR) uses a combination of radio waves and a strong electromagnet to detect the movement of water molecules in the brain.

Positron Emission Tomography (PET scanning)

Cerebral blood flow and metabolism are measured during specific tasks. This enables the areas of brain that are utilising energy during a particular cognitive task to be displayed.

Split brain studies A surgical solution for severe epilepsy involves cutting through the fibres that connect the two cerebral hemispheres. This prevents information from passing from one hemisphere to the other and allows careful presentation of information to one half of the brain at a time. The behaviour of the patient is then observed.

Table 2.1: Summary of techniques used to explore brain function and anatomy

2.3 Brain differentiation – the link between morphology and function

The possibility that there is a relationship between behaviour and morphology has

been demonstrated in many studies. A recent example found that musicians with

perfect tone perception tended to have larger planum temporale regions (upper surface

of the temporal lobe behind the auditory cortex) detected using PET scans (Schlaug et

al, 1995).

The most obvious structural division occurs in the cerebral cortex (see Figure 2.1).

The cortex is anatomically divided into two halves or hemispheres, linked by a bundle

of fibres (the corpus callosum).

Figure 2.1 Drawing to show the two hemispheres in relation to the head

2.3.1 Main differences between left and right hemispheres

Different areas of the two hemispheres have been identified as having specific

localised functions. A number of studies have shown that the majority of people have

speech controlled on the left side of the brain (see Springer and Deutsch, 1993). This

Left hemisphere

Right hemisphere

Cerebellum

Spinal Cord

distribution of speech centres on each hemisphere is shown by dominant handedness

in Table 2.2.

Handedness Left

Hemisphere Right Hemisphere

Bilateral

Right 95% 5% Left 70% 15% 15%

Table 2.2 Hemisphere with speech centres and handedness

The left hemisphere can be further subdivided into frontal lobes, temporal lobes

parietal lobes and occipital lobes (Figure 2.2). A noticeable split runs upwards along

the side and is called the sylvian fissure (Figure 2.3). Around this area are the speech

centres (particularly, the Broca’s area). The rest of the region is then named according

to location (anterior, central, posterior, lateral, inferior and superior) or divided

according to proximity with other structures.

Frontal Lobe

Pa

Figure 2.2 Left Hemi

Temporal Lobe

ge 29

sphere divided into lobe

Occipital Lobe

Parietal Lobe

s

Figure 2.3 Left hemisphere s

In one study, Geschwind and Levitsky

temporale in 100 post-mortem brains.

the left in most people (65 compared t

on the right hemisphere and 24 having

Other large-scale studies have shown

reduction in verbal ability - this sugge

areas are directly involved in speech.

reduction in non-verbal abilities invol

Deutsch, 1993).

This leads to the possibility of the two

In most people, the left hemisphere ha

understanding and describing in word

discrimination. The reverse is true for

Speelocat

th

Sylvian Fissure

howing the position of the sylvian fissure

(1968) measured the length of the planum

They found the planum temporale is longer on

o 11 having longer planum temporale structures

equal sized structures on each hemisphere).

that damage to the left hemisphere produces a

sts that the planum temporale and associated

Damage to the right hemisphere produces a

ving space, form and distance (Springer and

hemispheres having different processing styles.

s a language-processing role. It is used for

s but has some difficulty with visual

the right hemisphere. The left hemisphere is

ch centres ed around is area

capable of sequential processing e.g. sounds used in speech and of analytic processing

e.g. the features in visual information. The right hemisphere seems to be specialised in

non-linguistic functions involving complex visual and spatial processes. It seems to be

superior to the left hemisphere at perceiving part-whole relations. The right

hemisphere is more holistic and synthetic in processing information.

Marshall (1996) points out that damage to the left hemisphere usually leads to

dyslexia or dysgraphia while right hemispheric damage leads to neglect dyslexia with

more widespread visuo-spatial difficulties. However, there is some plasticity in brain

differentiation and specialisation with people with left hemisphere damage early in

life showing a higher incidence of right hemisphere or bilateral speech (70% of

left-handers and 19% of right-handers).

The brain does not automatically process information on the basis of the sensory

modality through which it is presented. There is some evidence that despite the nature

of the stimulus material, people are able to decide for themselves what to do with the

material in terms of information processing strategies. Sensory information presented

to the right side of the body generally goes to the left side of the brain for processing.

Light falling on the right visual field of each eye goes to the left hemisphere while the

light falling on the left visual field goes to the right hemisphere. This means that it is

possible to present visual information to one hemisphere and compare how long it

takes the participant to say the word with when the stimulus is presented to the other

hemisphere. If told to remember word pairs by subvocalisation the response times are

greater if presented to the right visual field (linking to the left hemisphere and speech

centres). If told to use visualisation strategies then the converse is true (Springer and

Deutsch, 1993).

2.3.2 Theories to explain specialisation of the two hemispheres

Geschwind and Galaburda (1987) have developed a theory of lateralisation to explain

how the two hemispheres have become differentiated. A common factor is responsible

for both left-handedness and susceptibility to immune disorders. This is male related

since the incidence of left-handedness is higher in males than females and so too is the

incidence of immune disorders. The factor must however, have the potential to affect

females and this led Geschwind and Galaburda to suspect that testosterone may be

responsible.

The effects of prenatal factors on the disposition to reading difficulties is currently

being investigated. Galaburda et al (1985) carried out post mortem studies on the

brains of 4 dyslexics and found neuropathological abnormalities consistent with

changes that occur at 24 weeks of foetal development. A study of 181 sibling pairs in

nuclear families by Fisher et al (1999) has found evidence to support the existence of

a genetic locus on chromosome 6 affecting orthographic and phonological processing.

This gene is located next to others which control histocompatability and it has been

suggested that this might be why there is a higher incidence of eczema, hay fever,

asthma and auto-immune diseases in families with dyslexics than without (Stein and

Talcott, 1999). Work by Sanchez et al (1999) suggests that the levels of oestrogens

during early pregnancy might influence development of the magnocellular region of

the brain (the importance of the magnocellular region is considered in Chapter 3).

Geschwind and Galaburda (1987) argue that testosterone might slow the growth of

parts of the left hemisphere during foetal development leading to the corresponding

parts of the right hemisphere developing faster. They thought that this could account

for a shift to right hemisphere participation in handedness and language skills.

Reduction in left hemisphere development leads to a permanent learning disorder.

They believed that the levels of testosterone would also contribute to the development

of immune disorders. Increased development of the right hemisphere could also

contribute to the development of spatial skills e.g. autistic children showing very

superior artistic skills. Studies have shown that mathematically gifted children had

five times the incidence of allergies than normal counterparts (Springer and Deutsch,

1993).

The right shift theory (Annett, 1996) suggests that the tendency to have a preference

for the right forelimb (compared with mixed tendency or tendency for the left)

produces the same distribution curve in other animals as in humans. In humans the

curve is shifted towards the right (increasing the proportion of right:mixed:left

preferences). The increase in right-handedness is seen as being due to an increase in

left hemisphere function. The risks proposed for those with a right hand advantage are

a reduction in functions controlled by the right hemisphere, leading to weaker

visuo-spatial skills and poorer mathematic ability. The risks for those with a left hand

advantage are reduced left hemisphere function including poorer phonological skills.

This was found to match difficulties in poor readers in a dyslexia clinic sample

(Annett, 1996).

Further work by Waldie and Mosley (2000) has shown that both hemispheres might

be involved in decoding text. The right hemisphere was able to have direct access to

word meaning using the word gestalt. They concluded that there is intrahemispheric

co-operation with the left hemisphere taking a dominant role during reading with a

dynamically involved right hemisphere. In essence this means that the left hemisphere

can process text using phonology and language processing while the right hemisphere

can process text using the gestalt of the word shape. Each hemisphere can access its

own lexicon directly but the left hemisphere is likely to have a bigger lexicon because

it is also used in speech. Co-operation between the hemispheres prevents the kinds of

errors noted by Annett.

An extension of these theories is called hemisphericity. This is the concept that an

individual relies more on one hemisphere than the other and is reflected in their

cognitive style and person’s preferences and approaches to problem solving.

2.4 Areas of brain considered to be involved in dyslexia

A large number of studies have been conducted to identify differences between people

diagnosed as being dyslexic and people thought to be normal.

Habib (2000) cites a number of post mortem studies that report microscopic cortical

malformations and an absence of asymmetry between the two hemispheres in the

region of the planum temporale in dyslexic subjects. The post mortem studies can tell

us what a brain looks like but does not tell us how the anatomical structure is related

to function. We are left to infer that anatomical differences in the brains of dyslexic

children and in normal children have resulted in differences in the way that the brain

processes information and consequently makes sense of text. However, in many of

these studies there was a history of oral language delay in the subjects that could mean

that observed differences were either correlated with spoken language difficulties or

with developmental dyslexia, or both.

Habib (2000) reviews a wide range of other studies that look at the brain when it is

functioning. A selection has been summarised below with the data re-arranged to

show the areas of brain investigated.

Area of brain MRI Study cited

by Habib (2000) Dyslexics Others

Lateral Ventricles Rumsey et al, 1986 10 male dyslexics

R<L 20% L<R 40%

No controls

Temporal Ventricles Rumsey et al, 1986 10 male dyslexics

R=L 90% No controls

Hynd et al, 1990 10 dyslexics 10 controls 10 ADHD (8M + 2F of each)

Length R<L 10%

Length R<L 70% of controls and 70% of ADHD

Shultz et al, 1994 17 dyslexics (10M, 7F) 14 controls (7M, 7F)

Surface R<L 76%

Surface R<L 71%

Larsen et al, 1990 19 dyslexics 17 controls (4M:1F)

Surface R<L 31.5% R=L 68.5%

Surface R<L 70.5% R=L 29.5%

Planum Temporal (PT)

Leonard et al, 1993 9 dyslexics (7M, 2F) 10 unaffected siblings (4M, 6F) 12 controls (5M, 7F)

Total Length No significant differences Temporal length R<L Parietal length No significant differences

Total Length No significant differences Temporal length R<L Parietal length R>L in siblings and controls

Frontal Lobes Hynd et al, 1990 10 dyslexics 10 controls 10 ADHD (8M + 2F of each)

Width R=L

Width R=L ADHD R>L Controls

Insula Hynd et al, 1990 10 dyslexics 10 controls 10 ADHD (8M + 2F of each)

R<L R<L ADHD R=L Controls

Postcentral surface Axial slice involving 6 areas: anterior polar (prefrontal regions) anterior (premotor regions and Broca’s area) anterior central (anterior part of the superior temporale gyrus) posterior central (posterior part of the central gyrus including PT) posterior (including the angular gyrus) posterior polar (lateral occipital cortex and the cuneus of the occipital lobe)

Duara et al, 1991 21 dyslexics (12M, 9F) 29 controls (15M, 9F)

No significant differences


Posterior Surface Duara et al, 1991 21 dyslexics (12M, 9F) 29 controls (15M, 9F)

R>L (p<0.001)

No significant difference

Inferior anterior (prefrontal region below the frontal operculum, including the orbifrontal lobe bilaterally)

Jernigan et al, 1991 20 language/learning impaired (13M, 7F) 12 controls (8M, 4F)

R>L L/Li children

Superior posterior (including the superior parietal lobe above the parietal operculum)


R<L (p=0.01)

Inferior posterior (includes the PT)


R<L 45% R>L 50% R=L 50%

Perisylvian Region Plante et al, 1991 8 specific language impaired and 8 normal MRI from data base

R<L 25% R>L 37.5% R=L 37.5%

Results not reported

Superior Surface of the Temporal Lobe (SSTL) extends from the end of the sylvian fissure to the anterior border of the temporale lobe and divided into two equal anterior and posterior surfaces

Kushch et al, 1993 17 dyslexics (9M, 8F) 21 controls (8M, 13F)


Anterior R<L Posterior R<L Total R<L

Intrahemispheric asymmetry (comparison of the length of the temporale border and length of the parietal border)


Left hemisphere T<P 22% T>P 78%

Right hemisphere T<P 55.5% T>P 44.5%

Left hemisphere T>P 100% in siblings and controls Right hemisphere T>P 100% in controls But T<P 40% T>P 50% T=P 10% in siblings

Parietal Operculum (there are four types)


Type 3 Left hemisphere 67%

Type 4 Right hemisphere 11%

Type 3 Right hemisphere 8% controls 40% siblings Type 4 Left hemisphere 10% siblings

Table 2.3 Areas of brain implicated in dyslexia. Rearranged from source data by author

When presented in this way, the studies indicate:

• While there are differences between left and right hemispheric organisation for

some dyslexics when compared to controls or siblings, this is not universal.

• There are similarities between dyslexics and other populations e.g. ADHD.

This may be because there are associated factors in each condition (difficulty

with organisation, or sustaining concentration). Alternatively this may simply

reveal co-morbidity between some of the subjects used in the studies

• Leonard’s results for siblings compared to dyslexics suggest that left

hemispheric differences are more important in contributing to dyslexia than

right hemispheric differences. However, many of the other studies point to

smaller structures on the right hemisphere. This suggests that phonological

difficulties might not be responsible for all types of dyslexia. It may be that

different types of processing deficit can be accounted for at the neurological

level and then linked with cognitive models that explain the different roles of

the left and right hemisphere in reading and spelling.

• Although the planum temporale has been a focus of many studies, Shultz’s

work casts doubt on the role of the planum temporale in contributing to

dyslexia with almost the same number of controls and dyslexics showing the

same asymmetry.

• Some areas of the hemispheres are not differentiated in dyslexics to the same

extent as in controls (e.g. Superior Surface of the Temporal Lobe) supporting

Geschwind and Galaburda’s theory of laterilisation, Annett’s right shift theory

and Waldie and Mosley’s dynamic intrahemispheric theory.

In adults, damage to the right posterior parietal cortex has been shown to lead to ‘left

neglect’. This is the inability to direct attention to the left side of space or the left side

of objects. There is often a misjudgement of direction, distance and spatial relations of

objects, particularly on the left. Stein (1996) reports similar difficulties for some

dyslexic children as those found in adults with parietal damage. They are prone to

make more errors on the left when copying the Rey Figure; make more errors on the

left when judging the angle of lines; make more errors on the left in cancellation

tasks; and clocks tend to have more spaces on the left and more figures on the right.

Marshall (1996) describes this type of dyslexia as ‘neglect dyslexia’.

2.5 Difficulties with previous studies

One of the difficulties of the previous studies is that in order to look for anatomical

differences between dyslexics and control groups there is a need to identify who the

dyslexics are. This is a matter for contention with different definitions used. The

argument can become circular, for example, defining dyslexia as having a

phonological weakness and then showing that it is caused by poor development of

language centres in the brain. Two examples have been selected to illustrate this type

of difficulty:

• Rumsey et al (1999) defined dyslexia on the basis of a mid-average or better

IQ (WAIS>99), average sight vocabulary on the WRAT-3 (Standard score of

>90) for half of the men involved in the study and, slow fluency measured on

the Gray Oral Reading Test (-0.67σ). They found that blood flow through the

angular gyrus changed with greater blood flow correlating with better reading

in fluent readers and with worse reading in dysfluent readers.

• Heim et al (2000) defined dyslexia as requiring 1 standard deviation between

predicted and actual scores for spelling and poor pseudo word reading. Since

the latter are not real words, the participants in the study have to use

phonological skills to attempt to decode them. In this study the researchers

found that in dyslexic subjects the left hemisphere auditory cortex was

organised differently to controls.

These two completely different ways of defining dyslexia lead to different subject

populations – would dyslexics in the Heim study have been included in the

dyslexic sample in the Rumsey study? Or are the two studies typical of many

others that show that if we can define some behavioural feature of a reading or

spelling difficulty then we can track down bits of brain that might be involved?

This essentially reflects the underlying assumptions in cognitive neuropsychology:

•••• Neurological specificity or isomorphism – the assumption that there is a

correspondence between the organisation of the mind and the organisation of

the brain

•••• Transparency – the assumption that impaired performance following brain

injury will provide us with a basis for determining which mode of the system

is disrupted. However, there are complex interactions between different parts

of the brain to produce a given process. These may be inhibitory processes as

well as excitatory processes. This means that damage in small areas could lead

to disruption of a large number of processes. Secondly, most injuries are

widespread and therefore affect more than one information processing module

or process – such developmental differences could explain why several

sections of the left hemisphere have been investigated.

•••• Subtractivity – the assumption that performance following a brain injury

reflects the previous intact cognitive apparatus minus those systems that have

been impaired and that the mature brain does not produce new modules.

However, in brain damage studies individual variation in performance prior to

injury is not accounted for.

The last assumption is probably an oversimplification since the brain can re-

organise to some extent and will adjust some aspect of its normal operation. The

ability of the brain to compensate for injury in one part – either through general

plasticity found during development, or through modules adapting the processing

of information presented to them, can lead to processing being carried out

elsewhere. This may be through the development of specific strategies to

overcome lost performance from modules that are damaged or through the

development of neurological pathways to handle more data. Habib cites evidence

from Schlaug et al, 1995b that intensive training for musicians can change the size

of the corpus callosum. Habib suggests that intensive remedial teaching for

dyslexics would have the same effect. If he is right then this would suggest that

the remedial teaching involves utilising brain function from one hemisphere to

compensate for poor function on the other. If this is true then teaching to

hemispherical strengths should facilitate the learning of spelling as it will not

require the same degree of interhemispheric collaboration.

There are a number of critiques of neurological studies in general:

•••• Beaumont (1996) coins the term ‘emergent psychoneural monism’ to describe

the process by which mental properties somehow emerge out of neural events

and while they can be directly correlated to them they have an existence that is

independent of physiology. This could mean that it is only when there are lots

of different sub-processes involved in spelling and they are working

effectively that the child is able to perform the emergent skill of spelling.

•••• Beaumont (1996) provides a second critique based on interactionist theory.

This reminds us that higher mental abilities are built up by combining a

number of more basic functions. Damage to the brain at a more basic level of

component skills will have an effect on higher functions, depending on how

much they require the underlying skills. In terms of brain imaging this means

that if lower processing areas are damaged the higher processing area may not

appear to be working because it has no input to process rather than because it

is faulty or damaged. E.g. an area of brain that processes word meanings

cannot determine the meaning of text if the centres that detect letter shape are

not functioning.

•••• Fischbach (1993) describes the difficulties of producing PET scans and MRI

scans. These require the background resting activity of the brain to be

subtracted from the slight increase in blood flow detected (20 to 50 percent).

They often require the same mental task to be performed over and over again

in order to detect the change. Assignment of changes in blood flow to specific

structures requires accurate superimposition of computer generated images

onto anatomical maps.

•••• Rose (1992) explains that the resolution of neurological imaging is not very

refined. PET scans are slow to obtain, needing neurone activation and

subsequent blood flow to be maintained. They can resolve down to half a

cubic centre metre. This is a relatively large area, given that neurones are

small enough to have 146, 000 per square millimetre of cortical surface

(Changeux, 1985). The PET scan is effectively only detecting activity if

millions of brain cells are operating on the task for a sustained period.

•••• Rose (1992) MRI scans have poor spatial resolution compared to PET scans

but can detect short events of only a few milliseconds duration. This means

that they can follow the pattern of activation during a specific task. While

being quicker and able to catch transient activity, they need the involvement of

even more brain cells than PET scans.

The critiques by Rose and Fischbach mean that MRI scans and PET scans are still

very much hit and miss. An analogy might help – if the brain was the size of the Earth

and I was an observer looking down trying to detect if this thesis was being processed

by you (representing a single neurone). I would only know if it was night time and

Manchester was in darkness and for some reason, as you read this text you decide to

tell everyone in the city to turn on their lights. I can then only tell if it has been read in

Manchester by mapping the pattern of light over a map of the UK. A lot of inference

is needed to understand why someone in Manchester might be reading my thesis –

what kind of processing is being done? Most of the processing being detected may

have nothing to do with the work of essential neurones dealing with the presenting

task. Perhaps you are not actually reading the words – maybe you found a diagram

that you like or you are just doing an audit of who has handed in their work. Maybe

the centres activated in the speech centres of dyslexic brains detected in some studies

are not actually involved in decoding the text but are simply processing an inner voice

saying, “Oh no… I’ve got to read this set of words!” Perhaps the real task of decoding

the visual symbols is done somewhere else by a much smaller collection of neurones.

2.6 Conclusions and implications for the current study

The brain is highly differentiated and attempts have been made to link anatomy to

function in order to provide further understanding of the types of information

processing difficulties that might contribute to poor reading and poor spelling. At the

moment, it seems that no single area of brain can be identified as causing all dyslexia

type problems – there is no specialised ‘reading centre’ or ‘spelling centre’. Not all

dyslexics have been found to have the same anatomical differences. Equally, some

anatomical differences found in dyslexic populations are also found in other clinical

populations.

This is to be expected from an evolutionary perspective since reading and writing

have only been available as means of communication for the masses for a relatively

short time. Why should the brain have evolved a single spelling centre? What seems

to be happening is that lots of different areas of brain that are involved in other highly

evolved tasks such as speech and spatial processing are used to carry out a relatively

complex task. Reading and spelling are complex processes made up of several smaller

cognitive sub-processes; a breakdown in any one can lead to the behavioural

presentation of reading or spelling difficulties.

There will be slightly different effects produced depending on which parts of the brain

are dysfunctional and subsequent cognitive processes impaired. This provides a

theoretical basis for the possibility of dyslexic subtypes that might require different

types of remediation. The question of whether the remediation should start at the

perceptual end of the processing so as to provide stronger input signals that can be

passed onto later processing modules is explored further in the next chapter.

At the gross organisational level, the two hemispheres seem responsible for

specialising in either verbal-auditory processing or visuo-spatial processing. A

relative strength in one hemisphere’s ability to process information compared with the

other could lead to a preference in the way that an individual makes sense of the

world around them. The interaction of different parts of the brain in the processing of

reading and spelling should produce observable differences in other aspects of

behaviour and lead to cognitive processing preferences. Such a difference should be

detectable on broad measures such as those suggested by psychometric tests and this

will be explored in Chapter 4.

Chapter 3 Sensory processing

3.1 Introduction

The brain cannot act on the real world directly. It relies on information derived from

the senses to try to make sense of what is happening around us. The different senses

provide the brain with different types of information and these are separate from each

other and do not interfere readily. Such information is said to flow in discrete

channels after work on selective attention (Broadbent, 1954). A number of channels

could be postulated at the sensory input stage (see Figure 3.1).

Figure 3.1: Sensory processing channels

The kinds of information that we can manipulate, store and respond to are limited by

the types of information that our senses can respond to. For example, there are a range

of colours that most people can see, yet other animals can respond to a different range,

for instance bees respond to ultraviolet patterns on petals that are invisible to us. This

Gustatory Channel

Kinaesthetic Channel

Auditory Channel

Visual Channel

Olfactory Channel

Colour, hue, brightness, contrast, form, movement

Pitch, tone, loudness

Texture, warmth, pressure, feedback from muscles

Smell

Taste, sweet, sour, salt, bitter

External world – an environment full of potential information

means that we do not have access to full information about the real world and

consequently have to make sense of what is around us from limited information. Our

brain has to construct a version of reality based on available information and

experience.

In terms of reading and spelling the most important channels to consider are the

auditory channel and the visual channel. The kinaesthetic channel can also contribute

towards spelling (e.g. feeling how a word should be written) but will not be

considered in this project.

This chapter will explore how the verbal and spatial channels are thought to work in

order to provide an understanding of cognitive processing preferences and how

difficulties in learning to read and write arise and how they might be remediated.

3.2 The auditory channel and the phonological deficit hypothesis

Some writers define dyslexia as being a language disorder that overlaps with other

communication disorders such as Asperger syndrome, autism and aphasia

(Hornsby, 2002). Reading and writing are extensions of language – their purpose is to

enable one person to communicate their thoughts and ideas to another. It would seem

reasonable therefore to assume that fundamental difficulties with processing spoken

language would impact greatly on other forms of communication. In essence, this is

the phonological deficit hypothesis. Rack (1994) argues that poor information

processing of sound is at the root of most dyslexic’s reading difficulties.

Research has identified a number of phonological processing deficits:

• Difficulty repeating psuedowords (Snowling, 1981; Gathercole et al, 1994;

Hulme and Snowling, 1994, 1997)

• Greater difficulty than controls in repeating long polysyllabic words (Miles,

1983; Hulme and Snowling, 1994, 1997).

• Less sensitivity to rhyme and alliteration than reading level matched normal

readers (Bradley and Bryant, 1978).

• Difficulties with speech production and perception (Hulme and Snowling,

1992, 1994, 1997)

• Difficulty in recognising rhyme and alliteration and analysing words for their

constituting sounds e.g. /k/a/t/ (Hulme and Snowling, 1994, 1997)

• Metalinguistic difficulties are also identified (Hulme and Snowling, 1994,

1997):

• rapidly naming objects

• repeating unfamiliar words

• short term memory tasks that require repeating random sequences of words

in the correct order after hearing them

• Poor phonological decoding is thought to be the cause of reading difficulties

for dyslexics rather than poor linguistic comprehension (Snowling, Hulme and

Nation, 1997)

Some of these difficulties could be evidence of a weakness in the phonological loop

component of working memory (Baddeley and Hitch, 1974; Gathercole et al, 1994)

This theory is not quite so clear cut, however. Bradley and Bryant’s (1978) study

compared four groups of pupils in different remedial programmes:

• sound categorisation training

• sound categorisation training supported by plastic letters

• semantic categorisation training

• control group (no treatment)

They found that sound categorisation had a beneficial effect on reading and spelling,

supporting the phonological deficit hypothesis. However, it was only significantly

better then semantic training when the sound categorisation was supported by plastic

letters.

There are a number of dyslexic children that are able to deal with phonologically

regular words but have great difficulty with irregular words in reading and tend

towards phonological regularity in writing. This suggests that they have intact

phonological skills and that their reading and writing errors cannot be explained by

the phonological deficit model (Talcott et al, 1998).

The role of phonological processing in reading and spelling is explored in a number of

developmental models. Treiman (1997) describes the kinds of writing done by

children of different ages and compares this to the kinds of spelling errors made by

dyslexic children. She concludes that dyslexics make the same types of errors as

normal younger children. Table 3.1 is based on information abstracted from her paper:

Age of child Descriptor of writing and types of

errors made Knowledge or processing skills

Pre-school Make marks on paper that are qualitatively different from drawings. The marks are smaller and usually arranged in a line.

The children know that writing looks different to drawing but do not understand that letters represent sound. Written words are thought to have direct representation to meaning in a physical sense – like a drawing. This means that big objects are given big words.

Reception Each syllable is represented by a single letter.

Writing and speech are thought to match at the syllabic level rather than the letter level.

Year 1 Increasing consistency with letter use and 1 letter per sound becomes common. E.g. eat � it (/i/t/) Difficulties arise when a phoneme is represented by more than one letter, for example, final consonant clusters (drink � drik, warm � wom, cold �cod). This can happen with other consonant clusters and can lead to the missing out of consonants. A third difficulty is that vowels are not used by the children to represent phonemes and this can lead to omissions of vowels (e.g. frmmr for farmer) Young children may use different sounds in their speech than adults:

• /dr/ becomes /jr/ so spelling dragon becomes jragn

• /tr/ becomes /ch/ so truck becomes chuck

Some letter reversals are more likely than others e.g. hme for hem is less likely than hre for her because:

• hr sounds like her and the children then remember that it has a vowel so add it at the

Writing and speech are matched at the phonemic level

Consonant omissions

Vowel omissions

Immaturity of speech leads to substitutions

Letter reversals have a phonological basis

end (after the sounded word) • hm could produce the sound

/h/em/ or children could make the /em/ sound from a short /e/ and /m/

Year 1 and Year 2

Awareness of orthographic rules and that some letter combinations are not permitted in particular parts of a word e.g. ‘ck’ does not occur at the beginning of a word. Reading and spelling experience strengthens phoneme-grapheme correspondences and children tend to make phonologically plausible spelling errors.

Phonological correctness takes precedence over orthographic correctness

As orthographic knowledge increases then children start to spell words so that they look right

Increase use of how words look to check how they have been written. Orthographic processing

Older children

Knowledge of how words are linked by meaning is used spell related words (e.g. heal appears in health, magic appears in magician). Morphemic rules start to take precedence over phonological pronunciations

Morphemic knowledge is used to deal with changes in pronunciation

Table 3.1 Developmental model derived from Treiman (1997)

Treiman can be criticised on a number of points:

• Her argument that hme is less likely than hre because of phonological errors

rather than visual sequencing errors seems speculative and the logic does not

follow. A child making the phonological error hr could just as easily make the

error hm and in both cases visually remember the presence of a vowel and add

it to the end of the sounded word. Equally her could be produced by making

the sounds /h// and assembling a short /e/ and blending with /r/.

• Young children may represent the sounds in warm as /w/aur/m/ and only hear

three sounds and so write them as wom. If this is true then we might expect

spelling errors to vary and to depend on regional variations in pronunciation.

However, children could equally remember the visual pattern of a w followed

by a circular shaped letter (o or a, possibly even e). Then r and m could

become merged into a visual gestalt causing an m to be produced.

While there is interplay between different types of processing, Treiman seems to be

arguing that different types of processing take precedence at different stages of

spelling development:

• analogical processing from objects to words producing a logographic spelling

• phonological processing of sound

o syllabic representation

o phonemic representation

• orthographic processing – checking the word looks right according to rules

learnt about how letters are allowed to combine

• morphemic processing – using meaning to link the spelling of related words in

preference to how the words sound

This seems similar to a developmental model of reading and spelling devised by Frith

(1985)

• Logographic approach (partial visual cues) to read words

• Alphabetic phase to spelling – learning about letter sound relationships

• Transfer knowledge to reading to read words not previously encountered

• Increasing fluency leads to a more orthographic phase for reading (extends

beyond the simple grapheme-phoneme correspondences to the use of

morphological spelling patterns).

• Transfer to spelling

In both models, phonological processing and skills are preceding the use of either

visual processing (checking visual patterns in orthography) or processing dependent

on meaning (morphemic checking). The models can be combined into a generalised

developmental model shown in Figure 3.2.

Figure 3.2: Generalised developmental model of literacy acquisition. Phonology takes precedence in the early stages of reading and spelling.

Goswami and Bryant (1990) provide a discussion of the weaknesses of developmental

models. They point out that the mechanism for transfer between different stages is

often unclear. They argue that sequential movement through the stages is not

universal but is dependent upon the language the child learns.

Logographic

Syllabic

Phonemic

Orthographic

Morphemic

Random

3.3 The visual channel and the visual deficit hypothesis

In order to able to read text and writing, the visual image created from light reflecting

from the page to enter the eye has to be decoded. Deficits in processing at this level

mean that reading cannot proceed accurately. This is the essence of the visual deficit

hypothesis.

Evidence collected by Olson (1989) suggests that children’s visual skills do not

correlate with reading ability. Stein (1996) cites other authors who have found that

most people believe that visual perceptual deficits seldom, if ever, cause children’s

reading problems. He argues that this is mainly because of the impact of the

phonological deficit hypothesis postulated by Bryant and Bradley (1983). In studies

reported in 1993 and 1994, Stein has presented evidence to challenge this view, and

suggests that visual perceptual abilities do correlate with good and bad readers. Other

workers estimate that visual deficits are found in up to 75-80% of developmental

dyslexics (Talcott et al, 1998; Williams, 1999; Lovegrove and Williams, 1993;

Slaghuis and Lovegrove, 1985).

The neurological pathway involved in seeing words is gradually being uncovered and

has developed from the initial work of Hubel and Weisel in 1959. Light passes from

the retina of each eye along the optic nerve. At the optic chiasma bundles of fibres

from each optic nerve converge and then split again so that the signals from the left

visual field are taken to the right visual cortex and the right visual field is taken to the

left visual cortex (see Figure 3.3).

Figure 3.3 Diagram adapted from Zeki (1993) showing the pathway of neurones from the retina to the visual cortex

Difficulties with visual perception and with visual processing may occur at any point

beyond the retina. Assuming adequate distance sight and no identified visual

impairment, the first thing that must happen to allow for decoding of text is that there

must be a steady image formed. Both eyes must be co-ordinated to move together and

focus on the same point with a degree of stability and feedback so that the brain can

give the impression of a having a steady image. Stein (1994) has found that some

dyslexic children lack stable binocular control. It was found that children with stable

binocular control could read on average 6.3 months better at the end of each of the

first three years of primary school, than those with poor binocular control (Stein,

1996). It could be argued that the development of reading skill leads to greater

binocular control as muscle control develops through greater exposure to the reading

task. Indeed, Stein (1996) reports development of ocular stability through maturation

when tested using the Dunlop Test1 (Table 3.2).

Age of child

% with ocular stability

6 years old 54 7 years old 70 9 years old 85

Table 3.2 Development of ocular stability

However, Stein has found that when younger readers are matched for reading age

with older poor readers, the younger readers have better binocular control. Attempts

to correct this defect using ocular occlusion of the left eye for 6 months have led to

substantial improvements in reading ability in 51% of the children in Stein’s study

(Stein, 1996; Stein et al, 2000) and similar improvements have been reported by

Rennie (1996). On average reading gains of 20.6 months were made in 1 year. It has

been shown that a short period of monocular occlusion can lead to children

overcoming their binocular visual confusion permanently, possibly by allowing one

eye to learn to control its own direction (Stein et al, 2000).

Reading age in months (BAS WRT) Condition

Start 3 months 6 months 9 monthsNot occluded 81.3 86.1 89.1 93.0 Occluded 82.9 92.5 94.3 97.7

Table 3.3 Gain in reading ability with ocular occlusion

The results of Stein et al (2000) are shown in Table 3.3. It can be seen the greatest

overall gain in reading age was for pupils who received the occlusion treatment, with 1 The Dunlop test is a test of ocular stability in which the child views a post next to a door through a stereoscope or synotophore. The child starts with a fused image and then slowly moves the tubes apart. One image of the post will appear to move towards the door and the child is asked which one. This is repeated 10 times. If it is always the same image that moves then the child has a fixed reference. If there is a mixed response (more than 2/10), then the child has an unfixed reference and unstable binocular control.

most of the increase occurring in the first 3 months. In this study, the children were

also provided with a yellow tinted lens for the eye(s) not occluded because this had

led to some improvement in magnocellular function in previous studies. In this study

the children have made month on month progress between 3 months and 9 months in

both conditions, this may have been due to the use of the coloured lenses, but may

also have been due to maturation with more children developing a stable ocular

control spontaneously. Stein argues that about 20% of 8 year olds will learn to

become fixed within 9 months. He also found that more children achieved stable

binocular control in the non-occluded group when wearing yellow lenses compared to

a previous study with plain lenses (Table 3.4).

Stein study (ref Stein 2000)

Lens type Gain in binocular control

1986 Plain 20-24% 2000 Yellow 54%

Table 3.4 Effect of coloured filters on ocular control

3.4 The magnocellular and parvocellular systems

A number of authors describe two different channels within the visual system

(Livingstone and Hubel, 1987; Livingstone, 1997; Stein, 1994, 1996;

Lovegrove, 1994; Chase, 1996). Each channel responds to different characteristics of

visual stimuli and the neurological pathway for each has been mapped (Figure 3.4).

Figure 3.4 Neural Pathways in the visual system

When reading the eye can focus on a small amount of the text in one go consisting of

approximately 5 letters ahead and 3 letters behind the current tracking position. It has

to flick along the line of text to see what comes next in a series of saccades. Each flick

Visual Channel

TRANSIENT SYSTEM

“Where system”

SUSTAINED SYSTEM

“What system”Mutually Inhibitory

• Large contours rather than fine spatial detail

• Low contrast • High response to

temporal information and movement

• Achromatic with no differentiation of

l

• Highly sensitive to spatial information e.g. detecting letter shapes

• low response to temporal information and movement

• sensitive to colour

Dorsomedial magnocellular pathway through the visual and pre-striate cortex

Ventromedial magnocellular pathway through the infero-temporal cortex

Right posterior parietal cortex associates eye-movement with image movement. Signals are used to determine the location and movement of targets and are important in the control of eye-movements

takes about 30ms and the fixations last longer 250ms (Stein and Talcott, 1999). It is

thought that the magnocellular system is responsible for controlling saccadic

movements and therefore a dysfunctional magnocellular system would lead to

difficulties in achieving a stable image of text for reading. We are unaware of the

saccadic movements because visual processing is inhibited between movements and

preventing us from seeing the blur of visual images that must fall upon the retina at

this time. The mechanisms responsible for this must respond to two images, one from

each eye, in order for us to adequately control and co-ordinate the saccadic

movements of both eyes to achieve a consistently overlapping visual image. Failure to

achieve a stable binocular control may result in images appearing to move or change

position (mild oscillopsia).

Oscillopsia makes the world appear to be in continuous motion as the small

involuntary movements of the eye give rise to the apparent motion of objects in the

real world and leads to the words on the page appearing to move around (Stein, 1996).

This must be particularly difficult for a child who has reached the phonics stage of

reading and needs to link a sound to a small stable image of a letter.

Stein (1996) has carried out a number of studies to compare dyslexic children with

normal readers and has shown:

• they have lower fixation stability when trying to focus on a small object

• unstable binocular control leads to more visual errors when reading non-words

• error rates are decreased if print size is increased

• EEG measurements of activity in the magnocellular region are reduced and

abnormal when visual stimuli of low spatial frequency and low contrast is

presented.

Stein (1994) has noted that the spellings of children who fail the Dunlop test tend to

be spelled out as they sound leading to a greater phonological regularity effect. He

also suggests that binocular control difficulties cause visual confusion in dyslexic

children, leading to mis-sequencing words when they attempt to read them. Perhaps

causing the child to sound out the letters in the order that they ‘see’ them (Stein et al,

2000). Stein argues that confused visual input forces children to have a greater

reliance on phonological rules leading to increased phonological regularisation errors.

This could explain the developmentally younger spellings noted by Treiman (1997).

This has led to the hypothesis that dyslexic children may try to sound out the confused

visual images and this leads to the production of more nonsense words on tests such

as the British Ability Scale Word Reading Test (BAS WRT) than for normal readers.

The parvocellular pathway has been reported to be unimpaired in dyslexics with a

reported 20% reduction in magnocellular cells in the lateral geniculate body of

dyslexics but no difference in the number of parvocellular cells (Livingstone et al,

1991; Talcott et al, 2000). It has been suggested that transient deficits should lead to

more errors for dyslexics when reading continuous text then when reading single

words. This is because reading continuous text requires integration of peripheral

vision form one saccadic fixation with central visual information on the next fixation

(Lovegrove, 1994). Colour and wavelength affect the response characteristics of the

transient and sustained subsystems to the processing of a stimulus. A red background

light has been found to attenuate the response of the transient channels in primates.

Dyslexics have been found to comprehend text better when it is presented in a blue

light compared with either red or white light (Lovegrove, 1994; Lovegrove and

Williams, 1993; Williams, 1999). Williams (1999) claims that manipulating

characteristics of the physical stimulus can compensate for deficits in the

magnocellular system and lead to improvements for up to 80% of dyslexics.

In normal sight we are have to use feedback from the ocular muscles to match the

shifting image on the retina so that the brain can cancel out the apparent movement.

This is achieved in the right posterior cortex. This is more difficult with small letters

or words than with large words and also when the eye is focussed on a close small

object such as in reading. In such close tasks, the angle at which the eyes have to work

together is much larger than when objects are further away. Equally, the larger the

target, the more easily the child can compensate for movement. This suggests a

number of interventions for this perceptual difficulty:

• increase the distance from the child’s eye to the reading material (some

children sit so their head almost touches the book)

• use a photocopier to enlarge text to provide a more stable image on which the

child can fixate

• using cues to help stabilise focus e.g. masks to surround the target text, rulers

to aid tacking of line

• using lens to enlarge the target text e.g. Visual Tracking Magnifier (Jordan,

2000)

3.5 Transient magnocellular deficit hypothesis

Stein et al (2000) argue that although most children with developmental dyslexia have

a phonological deficit, many have impairment in the transient magnocellular function

as well.

Galaburda and Livingstone (1993) have shown that dyslexic brains have anomalies in

the both the auditory and visual nuclei of the thalamus. Talcott et al (1998) have

found impairments in both auditory and visual processing due to temporal deficits of

the magnocellular layer and postulate that this would extend to other modalities as

well.

Livingstone (1997) postulates that the magnocellular differences noted between poor

readers and normal readers may be due to the speed at which the different pathways

operate. She points out that dyslexic children do badly at a range of tasks in which

speed of discrimination is needed. Talcott et al (1998) describes the magnocellular

pathway as being the visual pathway responsible for fast temporal perception and

argues that impairments in this system contribute to dyslexic difficulties.

Witton et al (1998) argue against the theory that developmental dyslexia results exclusively

from a phonological deficit and that the difficulty is due to more fundamental sensory

processing difficulties related to the detection of rapidly changing stimuli. Phonological

difficulties have been suggested to occur as a result of not being able to discriminate between

linguistic events that last for only a few milliseconds (Tallal, 1980).

Witton et al (1998) found that dyslexic subjects were less sensitive than controls at detecting

visual coherent motion in random dot kinematograms (RDK) and low rates of auditory

frequency modulation. Both measures were found to correlate with phonological skill

(measured using non-word reading). They argue that this is because of a dysfunctional

magnocellular region. Difficulties with auditory temporal processing result from damage to the

medial geniculate nuclei responsible for auditory relay through the thalamus. Difficulties with

visual processing results from damage to the lateral geniculate nuclei responsible for visual relay

through the thalamus. It is argued that the consequence of an auditory temporal deficit is

difficulty in discriminating between the different phonemes used in speech.

Changing phonemes in

speech

Magnocellular region of medial geniculate body

Rapidly changing

visual stimuli

Magnocellular region of lateral geniculate body

Area of dysfunction in dyslexics

Impaired phonological

skills

Impaired ocular control

Figure 3.5 Effects of dysfunctional magnocellular region on temporal processing of auditory and visual information.

The Witton et al (1998) study suggests that the magnocellular region is responsible for

responding to temporal information in both the auditory and visual channel. Impairment in this

area (either through a lesion or through structural differences resulting from genetic influences)

affects both visual processing and auditory processing. It is possible to hypothesise that the

extent of the damage in the magnocellular region will determine whether both channels are

impaired or just one (Figure 3.5).

Galaburda et al (1994) found that the magnocellular cells in the medial geniculate body are

particularly disorganised in the brains of dyslexic subjects studied at post-mortem.

Talcott et al (1999) report strong correlations between normal children’s sensitivity to 2 Hz

frequency modulation (FM) of a 1kHz tone and their phonological skills. The 2 Hz FM

threshold was able to account for:

• 40% of variance in non-word naming performance

• 29% of variance in decoding spoonerisms

• 34% of the variance in BAS reading ability

Talcott et al (1999) suggest that this study shows that basic auditory skills can constrain

phonological development. This is thought to be crucial when listening to speech and

developing phonemic awareness at the syllabic level before learning to read and spell.

Stein and Talcott (1999) estimate that about 20% of dyslexic children have a predominantly

visual difficulty, 20% have a phonological difficulty and 60% have difficulties with both visual

and phonological processing. Mild generalised damage to all magnocellular neurones is also

argued to account for slightly impaired motor function in dyslexics.

3.6 Implications for the current study

The phonological deficit model suggests that most reading and spelling difficulties are

caused by an underdevelopment of phonological skills resulting from poor

phonological processing. The visual deficit hypothesis suggests that most reading

difficulties are caused by poor visual processing leading to weak binocular control

and a phonological regularity effect. The magnocellular deficit hypothesis suggests

that difficulties noted in phonological processing and visual processing can both be

accounted for by considering temporal processing difficulties. Developmental

processes lead to an increase in phonological skills, ocular control and temporal

processing. A number of children develop less quickly in these areas and

consequently develop difficulties in acquiring literacy skills.

This leads to a number the hypothesis that different types of developmental dyslexia exist due to

different underlying physiological causes that have pervasive effects on information processing.

• some children encountered will have a phonological deficit

• some children will have a visual deficit

• some children will have a magnocellular deficit affecting both visual and

phonological processing

• some children will have a more general developmental delay in acquiring

literacy skills

Chapter 4: Cognitive profiles and preferences derived from psychometric analyses

4.1 Introduction

This chapter will consider psychometric theory and the extent to which different tests

can be used to measure an individual’s ability to engage in different types of problem

solving and to what extent that helps to identify different cognitive profiles and

cognitive preferences. In doing so the question of how useful cognitive profiles are in

defining dyslexia will be considered.

Carroll (1993) differentiates between two types of tests. There are those that measure

aptitude and those that measure achievement. Aptitude is a cognitive ability that is

predictive of future learning success. Achievement refers to the degree of learning

following a course of teaching. He defines cognitive ability as an intervening variable

linking together a series of systematic observations made from individual

performances on defined classes of tasks. The cognitive processes are the types of

mental operations needed to act on the given tasks in order to produce some

measurable responses. When performance on a number of different tasks is highly

correlated they are thought to involve the same cognitive processes or traits. The traits

have a high weighting on the same factor in factor analysis terms (Carroll, 1993).

Some researchers refer to these traits simply as factors or in the case of the DAS and

BAS, as clusters (Elliott, Smith and McCulloch, 1997; Elliott, 2001).

In terms of designing tests to identify traits there are a number of important stages:

1. develop some task with a set of items of differing difficulty

2. administer to a group of people who represent the target population for test use

– the standardisation sample

3. remove items that do not show sufficient discrimination

4. see if similar tasks measure the same ability using Pearsonian correlation or

Factor Analysis.

Once the traits have been defined, it then is possible to see how other people compare

to the standardisation sample and to identify their strong and weak traits to produce an

ipsative analysis or cognitive profile.

The attempt to use psychometric data to identify patterns of strengths and weaknesses

in subtest scores is not new. The pattern of scores is used to generate hypotheses

about underlying cognitive processes. The aim is to be able to predict what types of

educational intervention will be most suitable for a particular child. An example of

this was the way in which dyslexia was defined by Boder and Jarrico (1982) using

deficits in either “visual gestalt function or auditory analytic function” as defining

factors and then suggesting what type of teaching should follow.

Another use for cognitive profiles has been the attempt to identify one particular

pattern of scores as being an indicator of a clinical population. The defining of

cognitive profiles should relate to established research, but often this is not the case

(Holland and McDermott, 1996; Flanagan, Andrews and Genshaft, 1997; Kamphaus,

1998; Youngstrom, Kogos and Glutting, 1999). However, there is a growing body of

research regarding the use of subtest results on a number of test batteries for profile

analyses. These use groups of children who meet criteria for different clinical labels to

look for discernable patterns in subtest scores or factor scores. The use of the WISC

as an ipsative test to produce cognitive profiles to identify clinical subgroups has been

promoted by Kaufman (1994).

4.2 Profiles with the WISC

Factor analysis of the scores that make up the subtests on the Wechsler Intelligence

Scale for Children (WISC) lead to two major factors – Performance and Verbal. The

significance of the traits for verbal reasoning and performance skills measured on the

WISC has been explored by Rourke (1998). He identifies the following subgroups for

children classified as Learning Disabled:

• High Performance—Low Verbal (PIQ at least 10 points higher than VIQ)

• Performance equal to Verbal (PIQ and VIQ within 4 points)

• High Verbal—Low Performance (VIQ at least 10 points higher than PIQ)

The identified groups performed differently on a range of other measures:

• High Verbal group did best on the Peabody Picture Vocabulary Test; Aphasic

Screening, Speech Sounds Perception and Seashore Rhythm Tests; Reading,

Spelling and Arithmetic subtests of the Wide Range Achievement Test. These

tests all require the use of verbal and auditory perception skills. Results for

reading and spelling were significantly higher than for arithmetic.

• High Performance group did best on the Trail Making Test and Target Test.

These require visual perceptual skills. Results on the arithmetic test were

significantly higher than for reading and spelling. In a second study reported in

the same article, children with this profile did better on 25 measures of

complex motor and psychomotor abilities compared with the High Verbal

group.

What the study suggests is that differences in cognitive processing influence

performance on a range of measures. The differences identified in studies such as

Rourke’s have been attributed to neurological differences (Hynd, Cohen, Riccio and

Arceneaux, 1998), with the cautionary reminder of the need to find collaborating

evidence, such as from neuro-imaging techniques. The hypothesis remains that the

ability of neurological structures to process information leads to similar performance

on tasks that utilise the same underlying cognitive processes.

However, other research is starting to question the use of the WISC for cognitive

profiling. For example, low scores on Arithmetic, Coding, Information and Digits (the

ACID profile) was thought to identify weaknesses in cognitive processes common in

dyslexic children. A number of studies have highlighted difficulties with this

approach using the WISC (see Flanagan, Andrews and Genshaft, 1997 for a full

discussion):

• WISC was not designed with profile analysis in mind. The WISC was

designed to measure overall intelligence and not based on any theory of

neurological functioning or cognitive processing.

• Children with learning disabilities tend to show less variation in their subtest

scores on the WISC than children without learning difficulties. In one study

cited, only 3.6% of children with special educational needs show unusual

profiles compared to profiles for the normal population.

• Reliable differences in WISC profiles can be found in clinical groups but these

also appear in the normal population. This means that the profiles do not

discriminate between the two populations. The null hypothesis that the

variation described for the clinical group also exists in the general population

is not refuted.

• There is an assumption that children in the clinical subgroup are homogenic.

4.3 The BAS and DAS

The WISC is essentially an old test that has undergone several re-standardisations. It

does not benefit from developments in test theory, neurology or intelligence theory

that form the basis of more modern tests such as the British Ability Scales (BAS-II),

Differential Ability Scales (DAS) and Woodstock-Johnson Revised test battery.

The BAS-II and the DAS consist of:

• a composite score (GCA) that incorporates six subtest scores that have a high

loading for Spearman’s factor for general intelligence ‘g’

• 3 defined areas of reasoning or ‘clusters’ made up from the subtests that

contribute to the composite score

o Verbal reasoning

o Non-Verbal reasoning

o Spatial reasoning

• subtests that measure specific cognitive processing skills that do not contribute

to the composite score (diagnostic subtests)

• co-normed achievement measures for word reading, spelling and number

The BAS-II is based on a theoretical framework that sees psychometric g as ‘an

inescapable reality’ (Elliott et al, 1997; Elliott, 2001) that pervades all cognitive

processes. However, the test battery goes on beyond this to consider the different

dimensions of cognitive processing that can reliably show observable differences

between individuals when approaching problem solving, learning and have an effect

on achievement. The British Ability Scales (BAS-II) and Differential Ability Scales

(DAS) differ from the WISC and other older tests in that they were designed as

‘profile’ tests rather than as a measure of global intelligence (Holland and

McDermott, 1996; Elliott, Smith and McCulloch, 1997; Elliott, 2001). The tests have

a different theoretical starting point using the Horn-Catel model of intelligence as a

basis for item selection.

In the Horn-Catel model of extended Gf-Gc theory of intelligence there are a number

of constructs (Horn and Noll,1997; McGrew, 1997):

• crystallised intelligence (Gc) – ‘acculturation knowledge’ and a measure of the

depth and breadth of knowledge of the dominant culture

• fluid reasoning (Gf) – abilities in reasoning to arrive at understanding relations

among stimuli, comprehend implications and draw inferences

• visual processing (Gv) – measured in tasks that involve visual closure,

constancy and fluency in imaging the ways in which objects can be rotated or

flipped

• auditory processing (Ga) – abilities of listening and hearing and measured in

tasks that involve perception of sound under distraction or distortion, includes

phonological awareness

• processing speed (Gs) – rapidity of response in intellectually simple tasks

(those that most people would get right given time)

• quantitative knowledge (Gq) – measure of understanding and ability to apply

mathematical concepts

• short term memory (Gsm) recall of information presented within a minute or

two [also called SAR – Short term Apprehension-Retention or Short term

Acquisition-Retrieval]

• long term memory (Glr) measure of information recalled after several minutes,

hours or longer [also called TSR – Fluency of Retrieval from Long Term

Storage]

• Correct Decision Speed (CDS) quickness to respond to tasks of a non-trivial

difficulty level [also referred to as Gt – Decision/reaction Time]

Carroll (1993, 1997) presents a variation on the Horn-Catel model and McGrew

(1997) breaks each of the factors down into narrower ‘strata’ but these variations will

not be discussed as part of this study. Major clusters appear in many test batteries

including the BAS-II and factorial analysis of scores produces groups that correspond

to constructs (Gc, Gv, and Gf) from the Catel-Horn and Carroll models (Elliott et al,

1997; Elliott, 2001). The DAS give the broadest coverage of Catel-Horn constructs

after the Woodstock & Johnson Revised test battery, with only two constructs not

assessed – Ga and Gt (McGrew, 1997).

Two systems of information processing are considered and are linked to the auditory

and visual modalities. The systems include elements of perception and higher order

cognitive skills and have been found to be ‘neuropsychologically double dissociated’

(Elliott et al, 1997; Elliott, 1997; Elliott, 2001). This means that they represent two

distinct and independent systems of processing and it is possible to find people who

do well in one form of processing but poorly in another. Verbal factors (crystallized

intelligence, Gc) tend to be located in the left hemisphere while visuo-spatial factors

(Gv) tend to be located on the right hemisphere. Elliott (2001) reports that these have

been found to correspond to similar factors on the WISC-R (Performance subtests

correspond to Gv and Verbal subtests correspond to Gc).

The two systems deal with information by:

• starting with simple recognition

• storage of information, via working memory and passing to long term memory

• transformation of information involving conceptualisation and reasoning.

Elliott (2001) comments that cognitive psychology and neuropsychology

provide evidence that separate modalities exist in memory and these remain

doubly dissociated.

A third system involves the integration of both verbal and spatial processing,

represented by fluid reasoning (Gf) and seems to be located in the frontal lobes

(Elliott et al, 1997). This is measured with the Non-Verbal Reasoning cluster.

Elliott (2001) argues that critiques made about the use of cognitive profiles with the

WISC do not apply to the BAS-II because:

• Test-retest reliability for the three BAS-II factors is high indicating cluster

score stability.

• Interpretation only occurs when differences are significantly different, this

takes account of reliability and ensures that differences that could be due to

test measurement error are not reported.

• Reported measures are those that comment on the normative values achieved

rather than ipsative values.

4.4 Examples of cognitive profile studies using the BAS and DAS

McIntosh and Gridley (1993) cite two early studies using the BAS-R that attempted to

look at subtypes derived from cognitive profiles (Table 4.1)

Study Cited Classifications

produced Profile

Thomson (1982) BAS-R Learning Disabled Sample

• Auditory-linguistic

• Visuo-spatial

• Mixed

Visuo-spatial group scored lower on: • Speed of Information Processing • Block Design Level • Block Design Power • Immediate and Delayed Visual

Recall Tyler and Elliott (1988) BAS-R Dyslexic sample

• Mixed Visuo-spatial and linguistic problems

• Sequential processing problems

• Holistic processing problems

Mixed difficulties group scored low on: • Visualisation of cubes • Immediate Visual Recall • Recall of Designs • Recall of Digits • Word Definitions

Sequential difficulties group scored low on: • Speed of Information Processing • Immediate Visual Recall • Recall of Digits • Word Reading

Holistic difficulties group scored low on: • Recall of Designs • Word Definitions • Word Reading

Table 4.1: Cognitive profiles derived in studies using the BAS-R

Thompson’s study found no significant differences between Verbal IQ and

Performance IQ suggesting that the gross verbal processing and visual-spatial

processing pathways were not responsible for the observed differences in the subtest

score profiles. The profiles do not seem to link with the Gf-Gc factors as well as

would be expected from the Horn-Catel model.

There is a weakness with these two studies in that they both classify the subtypes on

the basis of subtest scores and these are more susceptible to measurement error (being

less stable than cluster scores). There is a decreasing validity and reliability when

moving from global scores to component test scores (Youngstrom and Kogos, 1999).

In contrast, factor scores are a halfway house – providing a score derived from non-

overlapping subtest scores. Factor score analyses offers the possibility of

differentiating between pupils while avoiding the ‘pitfalls of method variance and

unreliability’ that may affect subtest analyses (Youngstrom and Kogos, 1999).

McIntosh and Gridley (1993) used the standardisation sample from the DAS to look

for emergent profiles for learning disabled pupils using cluster analysis. They were

able to identify six homogenous sub-groups (Table 4.2).

Label Characteristics Generalized • GCA below average

• Spatial subtests average • Core subtests depressed • Diagnostic subtests depressed • Lowest Word reading and spelling subtest scores of the 6

groups • Significant discrepancy for reading and spelling (p=0.01) • Significant discrepancy for number (p=0.05) • Lowest scores were on Quantitative Reasoning and

Speed of information Processing High Functioning • GCA in the high end of the average range (highest of the

6 groups) • Little variation amongst cluster scores • Word Reading and Spelling significantly below predicted

(p=0.01) • Scores on the Recall of Objects and Speed of Information

Processing were low compared to other subtest scores Normal • average GCA

• few differences in the cluster scores • achievement scores commensurate with GCA • lowest scores on the Recall of Objects subtest (mean

score 1 σ below the mean of standardisation group) Underachievement • below average GCA

• Verbal and Spatial in the average range • Word Reading and Spelling significantly below predicted

(p=0.05)Borderline-Generalized (i.e. might not be considered Learning Disabled)

• low to borderline GCA • attainments consistent with general learning difficulties

rather than specific learning difficulties

Dyseidetic (Boder and Jarrico1982)

• mean GCA in the below average range • significant strength in Verbal Reasoning (p=0.01)

compared to Spatial or Non-verbal reasoning • Non-Verbal reasoning was higher than Spatial reasoning • poor visual-perceptual abilities and weak visual memory

skills • Word Reading and Spelling significantly below predicted

levels (p=0.01)• Number skills commensurate with GCA

Table 4.2 Cognitive profiles found using the DAS (McIntosh and Gridley, 1993)

McIntosh and Gridley’s study starts to show the relevance of the cluster scores. The

dyseidetic group is defined in terms of cluster scores, notably a high verbal score

compared to spatial reasoning. However, in order to correctly classify the pupils into

the different subtypes, McIntosh and Gridley found that both achievement tests and

diagnostic subtests had to be used:

• diagnostic subtest used alone 55.42% correctly classified

• attainments used alone 48% correctly classified

• both used 78.31% correctly classified

McIntosh and Gridley have shown that when a mixture of aptitude and attainment

tests are used to produce a profile of scores these can lead to a high level of successful

classification of pupils into clinical subgroups on the basis of test score alone. The

success rate is not 100% accurate, though it clearly exceeds the 12% of ADHD

children who demonstrate the ACID profile on the WISC (Prifitera and Dersh, 1993).

This suggests that BAS and DAS cognitive profiles are useful in generating

hypotheses about an individual that can then be explored through further assessment.

The predictive ability of the different factors produced by the DAS has been

investigated by Youngstrom and Kogos (1999). The comparison of

ability-achievement correlations is shown for a stratified sample of 1185 children in

Table 4.3.

Factor Reading Spelling Number

Non Verbal 0.509 0.478 0.573 Spatial 0.392 0.337 0.419 Verbal 0.599 0.496 0.486 GCA 0.597 0.523 0.589

Table 4.3 Correlations for attainment and ability for the DAS from Youngstrom and Kogos (1999)

A similar set of correlations is evident in the standardisation data contained in the

BAS-II Technical Manual (Elliott, Smith and McCulloch; 1997). This is shown in

Table 4.4.

Factor Reading Spelling Number

Non Verbal 0.498 0.510 0.653 Spatial 0.343 0.308 0.445 Verbal 0.633 0.548 0.540 GCA 0.590 0.550 0.653

Table 4.4 Mean correlations for attainment and ability for the BAS-II. Table produced by the author using information from the BAS-II technical manual pp 300-303

In each case, the global score (GCA) has the greatest predictive value for spelling and

number, while the verbal factor has the greatest correlation with reading. This would

suggest that pupils with a profile that includes a high verbal reasoning scores relative

to other factor scores would be more likely to succeed with reading and spelling. This

could be due to phonological skills involved in reading sharing underlying processing

routes with defining words and understanding verbal concepts. Alternatively, it could

be that those children who are successful at reading encounter more words through

reading and this increases the knowledge of words and verbal concepts.

However, some studies have found that clinical subgroups determined on the basis of

presenting difficulties do not have particular cognitive profiles. Shapiro et al (1995)

found no significant differences for subtest scores or composite scores for learning

disabled pupils assigned to groups on the basis of their difficulties in either reading or

reading and maths. A similar lack of cluster difference was found by Kercher and

Sandoval (1991) in the profiles for reading disabled children compared to normal

children. The main differences appear to be in:

• low scores on tests of attainment

• low Recall of Digits

• low Recall of Objects (This is named on the DAS but not on the BAS-II and

could invoke verbal recall rather than visual recall)

Many of the earlier studies suffer from a number of weaknesses highlighted by

Holland and McDermott (1996) and include:

• neglecting “to evaluate the replicability and stability of profile groups”

• failure to ensure that exceptional profiles were not in fact common amongst

unexceptional children in the population

In their study, they analysed the school age part of the standardisation sample for the

DAS using a technique that they describe as Cluster Analysis. The chosen clusters

were to ensure maximum coverage, replicability and distinctiveness of each cluster

(pupils within a cluster are similar to each other but not to other clusters). Stability

was investigated using the data from 170 children who took part in the test-re-test

standardisation procedures. Holland and McDermott were able to identify 5 Flat core

subtest patterns and 2 subtests that differ on a factor independent of general ability:

• 16% of children High Verbal-Low Spatial (Verbal Reasoning-Spatial

Reasoning=10 or more)

• 13% of children High Spatial-Low Verbal (Spatial Reasoning-Verbal

Reasoning=10 or more)

4.5 Recent studies using the BAS and DAS

Elliott (2001) has defined 9 cognitive profiles, of which 7 are thought to be relevant to

understanding literacy difficulties (Table 4.5).

Profile Label

Pictorial Representation (the bars represent

spatial reasoning, non-verbal reasoning and

verbal reasoning)

Description

1 Flat profile

No significant difference between the three areas of reasoning

3 Low Spatial - High Verbal

Spatial reasoning is significantly lower than verbal. Non Verbal is in between

4 Low Verbal - High Spatial

Verbal reasoning is significantly lower than spatial. Non Verbal is in between

5 High Non Verbal

Non verbal is the highest score and significantly higher than either spatial or verbal

7Significantly Low Non

Verbal

Non verbal is the lowest score and significantly lower than both verbal and non-verbal

8

Non verbal significantly lower than spatial and not significantly lower than

verbal

Non Verbal is the lowest score and significantly lower than spatial but not verbal

9

Non verbal significantly lower than verbal and not significantly lower than

spatial

Non Verbal is the lowest score and significantly lower than verbal but not spatial

Table 4.5 DAS/BAS-II profiles identified by Elliott (2001)

These profiles are used to place pupils into groupings in four studies reported by

Elliott (2001) and data from Kurie (2000). Poor readers are identified as having a

standard score less than 85 (i.e. falling into the statistical category below average).

These are summarised below in Table 4.6.

Study Sample Low Verbal High Spatial

Low Spatial High Verbal

Flat Cluster Profile

High Non Verbal Reasoning

Low Non Verbal Reasoning

N=2400 10.0 10.7 33.2 14.8 14.4 Reading <85 discrepant

23.6 6.0 32.6 10.5 14.6 DAS Standardisation Sample

Reading <85 not discrepant

9.3 7.0 65.1 9.3 8.1

Reading <85

12.3 4.9 16.0 6.2 42.0 DAS Dyslexic Sample (Turner, 2000) N=160 Reading

>85 8.9 15.2 20.3 5.1 35.4

Learning Disabled Sample (Dumont, 1996)

53 11.3 9.4 37.7 1.9 39.6

Reading <85

17.6 8.8 14.7 4.4 29.4 BAS-II Dyslexic Sample (Turner, 2000) N=126 Children described as being at least mildly dyslexic (index of 0.75 or greater)

Reading >85

17.2 5.2 22.4 13.8 20.7

Reading <85 not discrepant

50 50

Reading <85 discrepant

16 4 28 8 44

Reading >85 not discrepant

11.1 11.1 44.4 33.3

Reading >85 discrepant

20 20 30 30

Kurie Sample N=53

Reading >100

42.9 28.6 28.6

Table 4.6 Profiles found in different populations

Dumount (1996) has found a wider range of profiles than Rourke (1998) for learning

disabled children and this is due to the number of measures that can be provided by

the DAS compared with the WISC. The Performance=Verbal subgroup is represented

by the Flat Profile group; High Verbal-Low Performance is equivalent to the Low

Spatial-High Verbal group; High performance-Low Verbal group is equivalent to the

Low Verbal-High Spatial group. Additionally, Dumount is able to identify children

who differ in non-verbal reasoning. It would be interesting to see if the same children

were placed in the same groupings by the different tests in order to add weight to the

assertion that neurological differences account for differences in processing of

information suggested by the Horn-Catel model and subsequent performance on the

subtests making up each cluster.

The criticism regarding the lack of previous studies to consider the normal incidence

of different profile pattern patters (McDermott, 1996) is addressed by considering

how each population of children compares with the standardisation sample. The most

marked difference appears to be an increase in the incidence of low non-verbal

reasoning scores amongst the dyslexic and learning disabled subgroups compared to

the standardisation sample and this seems to be slightly more common in all samples

when reading is discrepant.

The DAS standardisation sample, DAS dyslexic sample, Kurie sample and learning

disabled sample all show a greater amount of Low Verbal-High Spatial compared to

High Verbal-Low Spatial pupils when reading is discrepant. This can be linked to the

correlations for Verbal Reasoning with reading ability found by Youngstrom and

Kogos (1999) and in the BAS-II standardisation. It could be an indicator of

underlying phonological difficulties or a weakness in verbal-sound processing, or a

lack of word knowledge that results from impoverished reading experiences.

4.6 Profiles in pupils referred to the author

Elliott’s sub-groupings have been put into a table by the author along with the

mathematical expressions used to construct a spreadsheet to identify the grouping to

which pupils belong (Table 4.7).

Profile Label

Mathematical expression to operationalise each profile by

Standard Score (Vr=Verbal reasoning, Nvr= Non-verbal reasoning,

Sr=Spatial reasoning)

1 Flat profile

Nvr-sr<16 AND Nvr-vr<14 AND Sr-nvr<16 AND Sr-vr <16 AND

Vr-sr<16 AND Vr-nvr<14 3 Low Spatial – High Verbal Vr-sr>15 AND Nvr>sr AND Vr>nvr 4 Low Verbal – High Spatial Sr-vr>15 AND Nvr>vr AND Sr>nvr

5 High Non Verbal

Nvr>sr AND Nvr>vr AND Nvr-sr>15 OR Nvr-vr>13 OR (nvr-sr>15 AND nvr-vr > 13)

7 Significantly Low Non Verbal Sr-nvr>15 AND Vr-nvr>13

8Non verbal significantly lower

than spatial and not significantly lower than verbal

Nvr<vr AND Sr-nvr>15

9Non verbal significantly lower

than verbal and not significantly lower than spatial

Nvr<sr AND Vr-nvr>13

Table 4.7 Mathematical representation of Elliott’s subgroups used by the author to identify profiles in an LEA sample

These groupings were then applied to reading and for spelling to a sample of 99

children seen over a period of 4 years by the author. Their schools had referred the

pupils and Psychological Advice had been prepared for Statutory Assessment. As part

of the assessment, the BAS-II had been used and a full set of subtest scores was

available from the reports that were written. Not all of the pupils were referred

because of learning difficulties with a minority presenting as children with

behavioural difficulties. Caution is necessary as this group is far from ‘normal’, but

typical of the types of children referred to LEA Educational Psychologists. In Figure

1.1 these would be the pupils considered to have Special Educational Needs (see

Figure 4.1).

Figure 4.1 Sample population for initial BAS-II profile analysis

Figure 4.1 shows a representation of the sample used. It includes pupils with

identified Special Educational Needs that will include those pupils with spelling

difficulties but will also include pupils that do not have spelling difficulties. The

majority of pupils referred were boys (see Table 4.8) The BAS-II is used to identify

the cognitive profiles in each group.

99 pupils from this level



Spelling difficulty

Preferences to be identified

Boys Girls Total Number in sample 74 25 99 Age range (years: months) 5:8-14:5 6:4-14:10 5:8-14:10 Mean Age (years: months) 9:11 9.4 9:9 IQ range 48-122 39-111 39-122 IQ mean 85.91 79.12 84.20

Table 4.8 Composition of SEN sample referred to author

The first table shows the pattern of profiles for good and poor readers to allow

comparison to previous studies (Table 4.9).

LEA sample (N=99)

Low Verbal High Spatial

Low Spatial High Verbal

Flat High NVR

Low NVR2

Reading < 85 Not Discrepant (n=13) 7.69 7.69 53.85 15.38 15.38

Reading < 85 Discrepant (n=74) 17.57 2.70 35.14 10.81 33.78

Reading > 85 Not Discrepant (n=6) 50.00 16.67 16.67 16.67

Reading > 85 Discrepant (n=6) 33.33 16.67 50.00

Table 4.9 Percentages of each group of children assessed by the author by cognitive profile and reading ability

2 Combined from three subtypes – low NVR (significantly lower than both Verbal Reasoning and Spatial Reasoning), NVR significantly lower than Verbal Reasoning but not significantly lower than Spatial Reasoning, NVR significantly lower than Spatial Reasoning but not significantly lower than Verbal Reasoning. This has been done to allow comparison with Elliott’s data. The graphs show this group further sub-divided.

The analysis was extended beyond previous studies to look for patterns of profiles

found in poor spellers and good spellers (Table 4.10).

LEA sample (N=99) Low

Verbal High

Spatial

Low Spatial High

Verbal

Flat High NVR Low NVR

Spelling <85 Not discrepant (n=11) 9.09 9.09 36.36 9.09 36.36

Spelling <85 Discrepant (n=79) 16.46 5.06 35.44 11.39 31.65

Spelling >85 Not Discrepant (n=6) 16.67 66.67 16.67

Spelling >85 Discrepant (n=3) 33.33 66.67

Table 4.10 Percentages of each group of children assessed by the author by cognitive profile and spelling ability

The different profiles evident in each group of pupil are shown below (Figures 4.2 to 4.5).

NVRsig less than VR

NVRsig less than SR

LowNVR

High NVR

LowVR, High SR

LowSR, High VR

Flat

Cou

nt

4.5

4.0

3.5

3.0

2.5

2.0

1.5

1.0

.5

SPELLING

Poor speller

good speller

Figure 4.2 Cognitive profiles for children with spelling levels as predicted by their GCA

NVRsig less than VR

NVRsig less than SR

LowNVR

High NVR

LowVR, High SR

LowSR, High VR

Flat

Cou

nt

30

20

10

0

SPELLING

Poor speller

good speller

Figure 4.3 Cognitive profiles for children with spelling levels significantly below levels predicted from GCA

p

NVRsig less than VR

NVRsig less than SR

High NVR

LowVR, High SR

LowSR, High VR

Flat

Cou

nt

8

7

6

5

4

3

2

1

0

READING

Poor reader

Average reader

Figure 4.4 Cognitive profiles for children with reading levels commensurate with predicted levels

NVRsig less than VR

NVRsig less than SR

LowNVR

High NVR

LowVR, High SR

LowSR, High VR

Flat

Cou

nt

30

20

10

0

READING

Poor reader

Average reader

Figure 4.5 Cognitive profiles for children with reading levels significantly below predicted levels

The profiles for reading and spelling in the LEA sample are very similar – this is

partly because the children who are poor readers are also poor spellers. It could also

be that the same cognitive processes that underlie reading are involved in spelling.

All possible profile types are found for reading and spelling (allowing for small

samples) but in different amounts. This supports the evidence from previous studies.

The relative numbers of each profile for reading shows:

• the incidence of low non verbal reasoning for non-discrepant readers is higher

than that found in the DAS standardisation sample for non-discrepant readers

• the incidence of low non-verbal reasoning for discrepant readers is higher than

in the DAS standardisation sample and more in line with the other samples

reported by Elliott (2001), suggesting a match with dyslexic profiles

• for non-discrepant poor readers (‘common garden’ poor readers), the highest

profile is the Flat profile, possibly consistent with a high incidence of general

learning difficulties. This matches the DAS standardisation sample with the

flat profile accounting for most pupils.

• the incidence of low verbal-high spatial and low spatial-high verbal profiles

for non-discrepant readers is equal and this is roughly the same as that found

in the DAS standardisation

• the discrepant sample of poor readers produces a pattern of profiles that

matches that found by Dumont with learning disabled pupils, Turner with

dyslexic poor readers (DAS), dyslexic good readers (BAS-II), Kurie with

discrepant poor readers. The incidence of profiles is in the following order:

o flat

o low non-verbal reasoning

o low verbal-high spatial

o high non-verbal reasoning

o low spatial-high verbal

• The proportion of pupils with Low Verbal High Spatial scores compared to

pupils with High Verbal, Low Spatial scores is about 8:1 for discrepant poor

readers in the LEA sample and 3:1 for poor spellers. This suggests that a

phonological deficit is more likely than a visuo-spatial deficit for these pupils.

The data from all of the studies seem to show that when reading is not discrepant from

general ability about half (37-65%) of pupils have a flat profile unless they are

dyslexic, when this drops to about a quarter (20.3-22.4%). This could be due to the

way that the different populations are defined – Turner includes other measures to

define dyslexia (Turner, 1997) that would reduce the sample size. This would mean

that for the DAS standardisation sample the flat profile includes a mixture of children

that would be defined as dyslexic and those that would be defined in some other way.

4.7 Other psychometric approaches

A study by Benbow and Minor (1990) used a variety of measures to attempt to find cognitive

profiles in gifted children. Factor analysis indicated that there are three distinct profiles:

• verbal

• non-verbal

• spatial/speed

These factors appear compatible with the Horn-Catell model of crystallized and fluid

intelligences. Benbow and Minor concluded that giftedness was not due to one particular factor

(IQ) but could be viewed as multiple talents. The types of giftedness explored by the Benbow

and Minor were verbal giftedness and mathematical giftedness and were found to be distinct

and associated with different cognitive profiles. Put more simply this suggests that if a pupil can

process some types of information well then they will be good at performing tasks that require

that type of information processing (if given exposure to the tasks).

4.8 Critique of methodology used by different researchers

There are a number of difficulties with the methodologies reviewed:

• There is an assumption that a standard score of less than 85 can be used as a

cut-off point for ‘poor readers’. The score is derived by comparing how many

words are identified from the graded reading list by the child with the amount

read by a child of equivalent age from the standardisation sample. However,

not all poor readers defined by this procedure are doing the same thing. As

children get older they will read more words. This means that the same

standardisation score does not equate to the same number of words decoded

from the word list for children of different ages. As the words on the reading

list become more difficult, children will encounter spelling rules, silent letters

and irregular letter combinations. This means that the cognitive processes

underlying reading become more complex as the list progresses. An older

child with a standard score of 85 will not be using the same cognitive

processes as a younger child with the same standard score. If this is true, then

we would expect the cognitive profiles for poor readers to be varied because

more than one cognitive process is underlying the reading of complex words

on the reading test. We might expect more non-verbal deficits in older children

who are poor readers as the complexity of words increases requiring combined

verbal and spatial decoding strategies. Comparing ability scores might be a

better option as it equates more closely with task complexity rather than with

how well the child reads relative to other children of the same age.

• The way in which the cut-off points are used to decide if verbal scores are

higher than non-verbal or spatial scores varies from researcher to researcher.

This changes the profile group to which children are allocated. There is a need

to have scores that are significantly different. Elliott uses the mean age values

to produce a statistically significant difference at the 0.05 level. Holland and

McDermont (1996) used a difference of 10 points between the Verbal and

Spatial clusters. Gridley and Roid (1998) describe how to calculate the

difference needed for statistically significant results in order to take account of

variability inherent in the testing process. An illustration of these differences is

shown in below (Table 4.11):

Study NVR3-SpR VR-NVR VR-SpR Gridley and Roid simple differences method

18 points 16 points 13 points

Elliott Mean values from technical manual (regression method)

16 points 14 points 16 points

Holland and McDermont

10 points

Table 4.11 Variation in decision points for defining cognitive profiles

• None of the studies take into account the age of the child and the expected

variation for children of that age shown in the standardisation sample.

3 NVR = Non Verbal Reasoning; SpR =Spatial Reasoning; VR = Verbal Reasoning

4.9 Implications for the current study

Particular profiles do not seem to be exclusive to a particular clinical sample of

children. Recent studies have shown that the distribution of profiles changes when

different clinical samples are compared with the more general population. However,

each clinical sample seems to have more than 1 cognitive profile found within it. This

suggests that the cognitive skills required to complete subtests on psychometric tests

such as the BAS may be used in different combinations to complete academic tasks.

The cognitive profile remains useful at the ipsative level to identify individual

strengths and weaknesses and in the current study is used to identify a profile that is

then explored in terms of its utility in describing how the child makes sense of the

world around them and hence processes information from academic tasks.

The phonological deficit hypothesis would predict that difficulties associated with

language processing in the left hemisphere would lead to reading and spelling

difficulties. While verbal reasoning correlates most highly with reading and spelling,

the low verbal profile does not constitute the highest incidence of poor readers or poor

spellers in recent studies.

The visual deficit hypothesis would predict that difficulties associated with visuo-

spatial processing in the right hemisphere would lead to reading difficulties. The low-

spatial profile is one of the lowest incident profiles found in recent studies.

The executive control function deficit hypothesis proposed by Elliott (2001) seems to

be well represented in the pupils found in the recent studies. However, low non-verbal

reasoning does not account for all pupils with reading, spelling or dyslexic

difficulties.

This leads to the possibility that reading and spelling difficulties can result from a

range of cognitive deficits that can be detected using a BAS-II profile. This may have

implications for understanding the underlying cause and subsequent interventions. In

the current study this will be explored further by seeing whether strengths in either

verbal or spatial reasoning lead to greater success if learning of spellings is improved

through the cued direction of attention using one sensory modality or the other.

Chapter 5 Summary of literature review and research questions

5.1 Introduction

Contemporary thinking in educational psychology places a high emphasis on the role

of phonological processing in reading and spelling. The phonological deficit

hypothesis can be summarised as a difficulty with decoding sound or spoken language

that inhibits development of normal spelling or reading. A weakness in phonological

processing is considered to be a major cause of literacy difficulties (Bradley and

Bryant, 1978; Bryant and Bradley, 1983; Baddeley and Hitch, 1974; Gathercole et al,

1994; Snowling, Hulme and Nation, 1997; Hulme and Snowling, 1992, 1994, 1997

Bradley and Bryant, 1978; Miles, 1983; Snowing, 1981; Gathercole et al, 1994; Rack,

1994; Hornsby 2002).

However, alternative explanations of literacy difficulties are possible and these have

been explored in the earlier chapters of this thesis:

• Visual processing must be an important factor as text must be detected before

it can be re-coded to sound. This has led to the development of the visual

deficit hypotheses which can be summarised as a difficulty with decoding

visual information that inhibits development of normal reading or spelling

(Talcott et al, 1997, 1998; Stein, 1994, 1996; Stein et al, 2000; Williams,

1999; Lovegrove and Williams, 1993; Slaghuis and Lovegrove, 1985; Rennie,

1996; Jordan 2000)

• The transient magnocellular deficit hypothesis considers that difficulty with

responding to rapid changes of sound or visual information interferes with

normal reading and spelling (Livingstone and Hubel, 1987; Livingstone, 1997;

Stein, 1994, 1996; Lovegrove, 1994; Chase, 1996; Stein and Talcott, 1999;

Stein et al, 2000; Livingstone et al, 1991; Talcott et al, 1998, 1999, 2000;

Witton et al, 1998; Tallal, 1980). The magnocellular pathways are divided into

those that process visual information and those that process auditory

information.

• The developmental delay hypothesis considers that there is a general

immaturity in neurological development and this means that reading and

spelling develop along normal lines but much more slowly (e.g. Treiman,

1997).

• The central executive control dysfunction hypothesis considers that integration

of information from the visual and verbal pathways is incomplete and this

inhibits reading and spelling development (e.g. Elliott, 2001b).

• The automaticity impairment hypothesis argues that fluency in decoding

textual information is impaired, making reading and spelling more arduous

(Fawcett and Nicholson, 1999; Nicholson, Fawcett and Dean, 1995; Nicholson

et al, 1999).

There are difficulties in determining a single explanatory causation for developmental

dyslexia. At the neurological level many areas of the brain have been examined and

found to indicate possible differences that are associated with dyslexia. The

neurological evidence is not without controversy with differences found in some

studies not being supported in others. The areas that seem to have been studied most

are those associated with speech and language on the left hemisphere. Other studies

have found differences on the right hemisphere. This suggests that a monoaetiological

perspective cannot be supported. The single factor that unifies the participants chosen

for study is a weakness in reading and spelling. In contrast the brain can be thought of

as consisting of a number of smaller processing components that link together to

enable fluent reading and spelling to occur. A difficulty in any one of the modules,

particularly early on in the decoding or recoding processes can lead to impairment in

reading and spelling.

Difficulties in cognitive processing produced by underlying neurological difficulties

can be detected more successfully when tests have been designed with this purpose in

mind. Factor analysis reveals patterns of performance that are doubly dissociated and

match with the neurological pathways observed in the brain. Studies to investigate

particular profiles achieved on cognitive tests show that no single profile is consistent

with dyslexia (McIntosh and Gridley, 1993; Youngstrom and Kogos, 1999; Elliot et

al, 1997; Shapiro et al, 1995; Kercher and Sandoval, 1991; Holland and McDermont,

1996; Elliott, 2001; Kurie, 2000). This was also found to be the case in the 99 pupils

referred to the author with literacy difficulties. This further supports the need for

intact processing ability in order to decode and recode text efficiently.

The different explanations arise partly from the starting perspective of the researchers

and the way in which they define dyslexia and select participants for their

investigations. However, evidence presented in the previous chapters shows:

• each hypothesis is supported by research evidence

• different parts of the brain are implicated

• a range of cognitive profiles are evident

• developmental processes influence ocular stability and control; speech

production and phonological awareness and; ability to respond to the level of

textual information (logographic � morphemic)

This leads me to the view that dyslexia is a collection of conditions characterised by

poor reading and/or poor spelling with a number of cognitive processes involved.

5.2 The focus of the investigations in this study

This study focuses on two main areas – visual spatial processing and verbal

processing. The assumption being made is that if there is sufficient evidence to

support these two main types, then further research and study could identify other

subtypes. A model (Figure 5.1) that summarises the main links between visual

processes and verbal processes was proposed by Ellis (1984). While it is an old

model, it contains the main features relevant to this study.

Figure 5.1 Ellis' model of cognitive processes involved in recoding language when reading or spelling

Ellis’ model suggests that both verbal processing systems and visual processing

systems are needed to successfully decode reading. He has a feedback loop from the

Spoken Word

Acoustic analysis system

Written Word

Visual analysis system

Acoustic code Letter code

Auditory word recognition system

Visual word recognition systemDirect route

Semantic system

Phonemic assembly

Grapheme-phoneme correspondences

Phonemic word production system

Phonically mediated reading

Read but not understood

Phonemic code

Pronunciation

Semantic code

Phonemic buffer

phonemic buffer to the auditory word recognition system that provides an inner voice

when reading and allows access to meaning when textual information is decoded

using phonically mediated reading or synthetic phonics. The model allows for higher

reading skills such as scanning or skimming where the rate of reading exceeds

pronunciation by having a direct route to meaning from the visual pathway. He also

allows for pupils who can translate the letter code directly to phonemic code and ‘bark

at print’ without understanding what has been read.

5.3 Cognitive preferences

The processes involved in reading are not the result of evolutionary development

specifically for dealing with literacy, but have developed for helping us make sense of

our wider world. A weakness in any of the cognitive processes can contribute to poor

reading and spelling, but will also influence other behaviours. As such, the efficacy of

the different sub-processes can be tapped into using a variety of measures and

observations (Figure 5.2).

Figure 5.2 The link between neurology, cognitive preference and possibilities for assessment

5.4 The research questions

This study is concerned with trying to identify two particular subtypes of poor readers

or poor spellers and then seeing if this helps to select a method of cueing recall to help

the pupil improve spelling accuracy.

This leads to a number of questions:

• Does the categorisation of pupils on the basis of BAS-II cognitive profiles into

subgroups that have either verbal strengths or spatial strengths reflect:

Underlying neurological differences in the doubly dissociated pathways involved in visual processing and verbal processing

Cognitive Preferences for dealing with information and interacting with the world

Learning Experiment

BAS-II Cognitive Profile

Teacher Questionnaire

Assessment Possibilities

Learning Style

Problem solving skills

Observable performance on literacy tasks

Self reflection of choice of pastime activities

Q-Sort

Analysis of spelling errors






writing subtasks, which on face validity, depend on either verbal skills




• It is hoped that the assessment strategies can inform future interventions and

this leads to a further question. Can pupil performance on the learning

experiment be predicted more accurately using the teacher questionnaire or the

pupil choices or the BAS-II profiles?

• The ecological validity of the BAS-II as an assessment tool can be explored by

examining the types of errors that children make in their naturalistic writing.

Do pupils with particular profiles tend to make less spelling errors consistent

with their cognitive preferences?

Chapter 6: Methodology

6.1 Introduction and overview

This part of the project set out to explore five assessment techniques:

• The use of psychometric assessment to identify types of cognitive preference

based on whether verbal reasoning is significantly better then spatial reasoning

or vice versa.

• The use of a teacher questionnaire to explore whether teachers notice the types

of errors that children make and whether this can be used to categorise


• The use of q-sort methodology to ask pupils about the types of pastime

activities that they like or dislike to see if this falls into the two types of


• The use of a learning experiment to investigate whether one type of perceptual

cueing is better for particular pupils while a different cue is better for others.

• The analysis of spelling errors made by children in their naturalistic writing to

see if this matches with their cognitive preferences identified on the BAS-II.

Comparisons of the categories of cognitive preference identified by each assessment

technique should allow the merits of each of the first three methods to be explored

with regard to utility in selecting learning style for pupils.

6.2 Selection of the participants

Two samples of pupils were selected for two phases of the study. The first phase

explored the first 4 assessment techniques. The second phase involved a much smaller

sample of pupils to look for ecological validity by comparing handwriting samples.

In the first phase seventeen primary school children aged between 8:0 years and 11:0

years were selected for this study on the basis of an opportunistic sample, drawn from

pupils referred to the County Psychological Service with concerns about literacy

acquisition.

The sample was narrowed by identifying pupils for involvement in the study on the

basis of their scores on the British Ability Scales (BAS-II). Children were identified

as having either higher verbal than spatial score or vice versa (see Figure 6.1 and

Table 6.1).

Boys Girls Total

Number in sample 16 1 17 Age range (years: months) 8:2-10:11 10:1 8:2-10:11 Mean Age (years: months) 9:6 10:1 9:6 IQ range 71-118 82 71-118 IQ mean 89.94 82 89.47

Table 6.1 Composition of sample used in main study

The number of boys clearly outweighs the number of girls, the archival data from the

99 pupils used in the earlier part of the study suggests that the ratio of boys to girls

referred to the County Psychological Service is about 3:1. In this part of the study a

lower proportion of girls was found that matched the requirements (right age and

having an appropriate cognitive preference), this is due to the nature of the

opportunistic sample. The majority of referrals to the County Psychological Service of

children with literacy difficulties tend to be boys. Within the restricted sample

obtained, only those pupils who had identifiable verbal or spatial strengths could be

used. The previous part of this study had found that only about 1 in 5 pupils referred

for Statutory Assessment have such a profile (see Table 4.10). For every 100 pupils

we would expect 20% to have special educational needs, about 1 in 4 of these would

be referred for Statutory Assessment and about 1 in 5 of this group would have the

required profile (i.e. a rough estimate puts this at about 1% of all pupils). For this

study, pupils must be aged between 8:0 and 11:0. This severely limits the number of

pupils available for inclusion in the project.

Previous studies had used a range of methods for identifying such a difference (see

Chapter 4) and it was decided to take account of age and use the age related tables for

significant difference in the BAS-II test manual. In this study a comparison is made

between the cluster scores (verbal reasoning and spatial reasoning) and the general

conceptual ability (GCA). This means that:

• verbal reasoning is an indication of relatively better verbal processing than

spatial processing if

o verbal reasoning is significantly higher (according to the test manual at

the 0.05% level or better) than GCA while spatial reasoning is not

o spatial reasoning is significantly lower than GCA while verbal

reasoning is not

• spatial reasoning is an indication of relatively better visuo-spatial processing if

o spatial reasoning is significantly higher than GCA while verbal

reasoning is not

o verbal reasoning is significantly lower than GCA while spatial

reasoning is not

Figure 6.1 Sample of pupils for the main study

Further information was then collected for the same group of pupils in the study using

a teacher questionnaire. The same pupils were then subjected to individual

assessments and work using:

• q-sort

• a learning experiment

17 pupils with identifiable cognitive profiles on the BAS-II selected from this subgroup on an opportunistic basis.



Spelling difficulty

1%

6.3 Teacher questionnaire methodology

6.3.1 Design of the questionnaire

The first questionnaire was designed early on in the development of this project and

before the scope of the project had been reduced. Questions were selected that related

observations that the teacher could make to established cognitive models. At this stage

of the study, questions were selected that related to areas felt relevant to spelling

difficulties. They are reproduced here so that the reader can appreciate the task being

presented to teachers – with the refinement of the study; many of these questions were

removed:

General cognitive ability type questions to separate out general learning difficulties from specific learning difficulties. • Do you think the child’s literacy skills are developed to the same level as their general ability • Is the child able to reason and discuss ahead of their ability to record information or read • Can the child bring relevant information into discussions • If it wasn’t for the child’s level of literacy skill, would you think that they could cope with a higher

level of work generally? • With the exception of literacy, does the child learn most things quickly?

Verbal ability • Identified history of language difficulty • Difficult with language comprehension • E.g. confusing similar sounding words/letters • Difficulty following instructions that are given verbally • Do questions or instructions need to be repeated • Difficulty with language production • Does the child take part in group discussions • Do the child’s spelling errors make some sense if sounded out/ are spellings phonetically plausible • Does the child have more difficulty in using polysyllabic words in speech than monosyllabic

words • Does the child have more difficulty learning polysyllabic words than new shorter words • Is the child good at providing rhymes for words • Can the child say words that begin with the same sounds • Does the child have a good understanding of words • Does the child have a rich vocabulary • Does the child speak in complex sentences or simple sentences

Visual ability • Identified history of visual difficulty • Difficulty following pictorial representations of instructions e.g. flow diagrams • Does the child remember better if instructions are written • Can the child spell some words well • Does the child lack strategies for approaching unfamiliar words (in reading and spelling) • Is the child good at discriminating between left and right • What types of spatial difficulties does the child have • Does the child reverse letters in writing, reverse letter sequence • Is the child good at matching words that have similar features or look similar • Can the child copy from the board? • Can the child copy from a model e.g. use underwriting, copy from a word frame • Does the child tend to confuse similar looking words/letters • Does the child make more reading errors when asked to read continuous text than single words • Does the child reverse letters, invert letters or reverse the sequence of letters in a word Long Term Memory/Semantic memory • What sorts of interests does the child have • What types of reading does the child enjoy • Is the child good at games that require memory Long Term Memory/Sequential information knowledge • Can the child recite the alphabet • Does the child know the days in the week/months in a year Motor control • Is the child’s handwriting good • Can the child copy pictures well • Is the child a good drawer/ good at art • Has the child got a good pencil grasp • Did the child learn to tie their shoe laces easily/at what age did the child learn to tie their shoes • Can they ride a bike/good at PE Left neglect • Do the child’s drawings tend to have more detail on one side of the drawing or are both sides

equally well developed? If so, which side? Speed • Is the child slower to complete all tasks than other children • Can the child complete tasks if given more time General open-ended • What strengths would you say the child has now? • What would you say their major difficulties are? • What things does the child do well? In what ways? • How would you describe the main problem? • In what ways has the child made progress since joining your class? • In what ways have difficulties become more noticeable? Past experience • Has the child changed schools frequently? • Has the child attended school regularly since starting school?

These questions were divided into several sections (see Appendix 1):

• Section A collected background information that the teacher and SENCo

would be expected to have ascertained through the staged approach to

assessment recommended by the Code of Practice for Special Educational

Needs (DfEE, 1993). The questions are open-ended and specific questions

were added related to vision and language.

• Section B asked for more specific information about attainments and was

designed to allow:

o Allow check against County Criteria (Staffordshire County Council,

1996)

o To look for patterns of results that might indicate Specific Learning

Difficulties rather than general learning difficulties e.g. differences

between English Speaking and Writing.

• Section C contained a list of statements for teachers to rate how well each one

applied to the child. It is essentially a ‘within-child’ rating scale.

• Section D contained a list of statements for the teacher to rate that compared

the child with others in the class – a ‘between child’ rating scale.

• Section E asked a number of specific questions.

Two different rating scales were selected for use: Low ability------------------------Average in class-----------------------High Ability 0 5 10

No -----------probably no--------possibly yes-------------yes 0 1 2 3

Open ended questions were also used to:

a. Provide qualitative information e.g. through identifying constructs of the child’s

strengths and weaknesses that can be used to support cognitive style predictions

b. Use qualitative data to refine problem definition and generate new hypotheses and

to support or refute hypotheses from ratings and teacher collected data

c. Reframing of teacher perceptions by getting them to reflect e.g. key questions

such as “How has/in what ways has the child made progress since joining your

class?”

It was hoped that ratings and open-ended questions could be used to identify strengths

and weaknesses within key information processing components. In particular

identifying those pupils who had a strength in verbal reasoning or those who had a

strength in spatial reasoning. (see Table 6.2)

Hypothesis tested Items contributing Over anxious school/parents

Comparison of NC attainments with TGAT expected range Compare NC attainments with County Criteria

Past experience A1, A2, General LD B - all scores low SpLD B – speaking and listening high, reading and writing low,

science high, using and applying high C1 – N C2 – Y C3 – Y C4 – Y/PY C5 – Y

Verbal A4 B – Speaking and listening low compared to other scores C9 – weakness if Y C13 – strength if Y C17 – strength if Y C19 – strength if Y C20 – weakness if N C21 – weakness if N C22 – weakness if N C23 – weakness if N D1, 10-D2, 10-D3, 10-D4, D5, D6, 10-D7

Visuo-spatial A3 B – Weakness if Shape low compared to others C7 – strength if Y C8 – strength if Y C10 – weakness if N C11 – weakness if N C12 – weakness if N C14 – strength if Y 10-D8, 10-D9, 10-D10, D11, 10-D12, 10-D13, 10-D14

WM C7 – weakness if Y C5 – strength if Y C13, C14 – strength if Y D11

Left Neglect E1 Speed of processing C6 – Y, 10-D18 Sequencing C15, C16, C18 LTM C15, C16, C18 Motor difficulties Discount if … D15, D16, D17, D19

E2, E3 Table 6.2 Question items contributing towards each area of cognitive processing (see Appendix

for questionnaire)

6.3.2 Analysis of the pilot questionnaire

A pilot questionnaire for teachers was designed and sent to schools referring of 10

pupils and 8 were received back for analysis.

The format was too large and too ambitious in what is it was asking teachers to report

on. Several teachers commented on this and further refinement was made to remove

items not directly related to the study. However, teachers were able to answer the

questions in the format required.

A shorter version was designed (see Appendix 2) that:

• reduced background information but retained Q3, Q4, Q5, Q8

• removed box B

• from section C and D, removed questions that try to distinguish between Specific

Learning difficulties and general learning difficulties. Individual casework had

revealed a number of pupils with specific difficulties being treated as if they had

more general problems and this suggests that teachers may not be sufficiently

aware of the pupil to respond accurately to this issue. (Questions C1-5)

• Removed questions related to left neglect

• Removed Section E

Analysis of the remaining questions in Section C and D revealed that C7 did not

produce a differential response across the 8 pupils – written instructions were felt to

be unhelpful to all children referred with reading/spelling difficulties. This question

was also removed from the questionnaire.

For the purpose of the study the main cognitive features to be identified are verbal or

spatial weaknesses and strengths. It was decided to remove questions related to other

cognitive processes (working memory, motor skills, long term memory and speed of

information processing).

There were also more verbal questions in the initial questionnaire than spatial and this

has been balanced out by removing those that produced least variation in teacher

responses for the 8 pupils rated.

This produced a much shorter version of the questionnaire that most teachers

participating in the study reported they were able to complete in between 5 and 10

minutes.

Scoring of Version 2

Section B award points as follows: No=0, Probably No = 1, Possibly yes= 2, Yes=3

Cognitive processing measured

Items contributing to score

Verbal (Max = 85) (3-B3)+B7+B9+(3-B10)+(3-B11)+C1+ (10-C2)+ (10-C3)+(10-C4)+ C5+ C6+(10-C7)

Visuospatial (Max = 85) B2 + (3-B4)+ (3-B5)+ (3-B6)+B8+(10-C8)+(10-C9)+ (10-C10)+C11+(10-C12)+(10-C13)+(10-C14)

Table 6.3 Question items contributing towards each area of cognitive processing (see Appendix for questionnaire)

Once scored the questionnaire produces two scores for the child. One for verbal processing and

one for visuospatial processing and this allows comparisons to be made to see if both types of

processing are the same or one is perceived of as being a strength.

A number of possible scores can be obtained:

• Global Verbal versus Global Spatial

• Within-child verbal versus Within-child spatial

• Related to peer group verbal versus Related to peer group spatial

The utility of the different scores is considered in the analysis of the data.

6.4 Q-sort methodology

6.4.1 Design of the Q-sort

Pastime activities that children might engage in were brainstormed and listed. The list

was then presented to a group of practising educational psychologists to classify as

mainly requiring verbal processing, spatial processing, both or neither. (See

Appendix 3).

7 sets of responses were received and items selected for the Q-sort from this list to

give the items face validity. The results are shown in Table 6.4.

I like to… b=both, n=neither, v=verbal, s=spatial

Nei

ther

both

verb

alsp

atia

l

Read b v v v n s b 1 2 3 1Draw s s s s s s s 0 0 0 7play with plasticine s s n s s s s 1 0 0 6build paper models s s s s s s s 0 0 0 7Sing v n v n v n v 3 0 4 0talk with friends v v v v v v v 0 0 7 0say poems or rhymes v v n v v v v 1 0 6 0Run s s s n n s s 2 0 0 5Paint s s v s s s s 0 0 1 6listen to my radio v v n v v n v 2 0 5 0listen to pop stars v v n v v n v 2 0 5 0Colour s s n s s s s 1 0 0 6play music v n v n v s b 2 1 3 1listen to stories v v n v v n v 2 0 5 0look at photographs s s v s s s s 0 0 1 6play I-spy s n v s v v v 1 0 4 2play football s s n s n s s 2 0 0 5tell jokes v v v v v v v 0 0 7 0listen to riddles v v v v v v v 0 0 7 0look at the moon and stars s n n s n n s 4 0 0 3do jigsaws s s s s s s s 0 0 0 7build sandcastles s s s s n s s 1 0 0 6play matching games s s b s n s s 1 1 0 5cut out shapes from paper s s s s s s s 0 0 0 7make voices for my toys v v v v v v v 0 0 7 0

Table 6.4 responses received from practising educational psychologists

This allowed the following items to be selected: Spatial Rating Verbal Rating I like to build paper models 7 I like to talk with friends 7 I like to play matching games (like ‘pairs’, or ‘snap’)

7 I like to make voices for my toys

7

I like to do jigsaws 7 I like to tell jokes 7 I like to cut out shapes from paper 7 I like to listen to riddles 7 I like to draw 7 I like to say poems or

rhymes 6

I like to look at photographs 6 I like to listen to my radio 5 I like to play with plasticine 6 I like to listen to my

favourite pop stars 5

I like to paint 6 I like to listen to stories 5 I like to colour 6 I like to play I-Spy 4 I like to build sandcastles 5 I like to sing 4

Table 6.5 Items with the highest consensus chosen for the q-sort

It can be seen that there was not total agreement for all of the items selected, but this

methodology allowed 5 items to be rejected completely and those items with the

greatest face validity retained.

Some of the respondents completed the questionnaire but added that they were unsure

about some items. This could be because some of the things that children do could

utilise one form of cognitive processing for most children but there may be different

for other children. For example, all psychologists agreed that talking to my friends

utilises verbal processing but some people think about the content visually and then

transform the thoughts to verbal in discussion. This is more applicable to ‘I-Spy’

requiring looking for objects (visual search) and comparing phonology of onset

(verbal) to target phoneme. Psychologists who rated ‘I like to sing’ as not being verbal

identified it as being neither a verbal task nor a visual task and this is harder to explain

(since it is not kinaesthetic, gastric or olfactory type processing).

6.4.2 Administration of Q-sort

The chosen items were given a pictogram to support the child when sorting (since the

children in the study are referred for reading and spelling difficulties). Each item was

presented on a separate card, shuffled and placed on the table one at a time with the

psychologist reading the card to the child. When all 20 cards are on the table the child

was asked to choose the 2 cards that are the things most like them. Those cards are

removed. Next the child was asked to choose the 2 least like them. These cards are

removed. The process was repeated with the next 4 that are most like the child, then 4

least like the child. From the remaining 8 cards the child selected the 4 most like

them, leaving 4 least like them (Table 6.6).

Q-sort layer

Description Number of cards required

1 Most like me 2 2 Next like me 4 3 Little bit like me 4 4 Not quite like me 4 5 Not like me 4 6 Not like me at all 2

Table 6.6 The number of cards selected for each layer

6.4.3 Scoring procedure

It has been argued that cognitive preference is due to an underlying neurological

component and as a consequence is pervasive and influence the choice of things that

the child likes to do. If the child has a verbal strength then it would be expected that

verbal items will appear more in layers 1-3 than in 4-6. The distance from the centre is

also important and a simple multiplier was used to score the items (see Table 6.7).

Layer Points assigned

to each item 1 32 23 14 -1 5 -2 6 -3

Table 6.7 Scores allocated to each layer

A perfect separation for a verbal preference would produce:

Verbal score = (2x3)+(4x2)+(4x1)+(0x-1)+(0x-2)+(0x-3) = 6 + 8 + 4 + 0 + 0 + 0

= 18 Spatial score = (0x3)+(0x2)+(0x1)+(4x-1)+(4x-2)+(2x-3) = 0 + 0 + 0 -4 -8 -6 = -18 Similarly a perfect separation for a spatial preference would give: Verbal score = -18 Spatial score =18

No preference would result in an even distribution of scores as items would be

selected equally from both areas of reasoning:

Verbal score = (1x3)+(2x2)+(2x1)+2x-1)+(2x-2)+1x-3) = 0

Spatial score = (1x3)+(2x2)+(2x1)+2x-1)+(2x-2)+1x-3) = 0

This can be simplified into a single measurement scale:

Perfect No Perfect Verbal �verbal� Cognitive �spatial� Spatial Preference Preference Preference

Verbal items +18 0 -18 Spatial items –18 0 +18 As the scales are perfect reciprocals of each other it is only necessary to track one set.

As the spatial items had the greatest face validity and consensus when presented to

practising psychologists, this set was been chosen to measure cognitive style.

6.4.4 Piloting the Q-sort

The procedure was tried out with a colleague who attained an overall score of V=+1,

S=-1. She felt the evaluative comment of not having an overall preference and

perhaps using both strategies equally was right. She cited examples of using verbal

strategies in some situations and spatial strategies in others.

Four pupils were used to test the suitability of the materials. As each card was

presented to the child, the sentence was read out and the child cued in to the picture.

When all 20 cards were on the table the procedure outlined above was followed. The

children did not have any difficulty in selecting items and seemed to enjoy the

activity.

6.5 The learning experiment

The learning experiment aimed to present a graded list of words for children to learn

to spell using two different techniques to cue perception. The first technique

attempted to cue children into the auditory features of the word while the second cued

in to visual features of the word.

There are many features in each modality that could have been used. For an auditory

analysis the child could be cued in to:

• individual letter sounds

• individual phonemes that might consist of letter groups

• syllables

• onset and rime

For visual analysis the child could be cued in by:

• physical features of the letters e.g. ascenders and descenders

• splitting words into morphemes

• mixing lower case and upper case letters

• making the whole word part of a more elaborate shape e.g.

• using colour to cue to particular letters.

In this experiment it was decided to use individual letter sounds to cue the child in to

auditory features and 3 colours to act as visual cues.

6.5.1 Selection of words to be used

There are 44 phonemes in spoken English but only 26 graphemes; however these are

used in a highly consistent way (DfEE, 1999a). Part of the difficulty in learning to

spell is that about 140 combinations of the 26 letters are needed to represent the 44

phonemes (DfEE, 1999b). The words chosen must be capable of being produced

using either a verbal strategy or a visual spatial strategy and this restricts words to

being phonically regular.

In the experiment, there was a need to control for demands on working memory by

ensuring that there was an equal length and number of units (letters/syllables) when

matching words to be learned in each way.

It was decided that the words should be graded in some way to support the child’s

self-esteem, moving from easier words to more difficult words. The children being

included in the study will be aged between 8 and 11 years so graded words should

range from 2 years below to age appropriate. It was decided to use words spanning the

6 to 10 year age level.

Having more difficult words also ensures that there are words to be learnt by children

who may already know how to spell the lower words.

One source considered for word selection was the Boder test of reading-spelling

patterns word lists (Boder and Jarrico, 1982).

Age Phonetic (Visual when read as flash, phonetic

when untimed [max 10 sec]) Non-phonetic (Visual)

6 years After Bird Came Funny Dog Horse Fish Shoe Man Was Box Apple Hand Girl Sat Store Under There Then Work

7 years Ever Does Faster Eyes Name Right Show Table Step Talk Grass Any Keep Buy Much City Nest Gone Well Today

8 years Awake Great Child Knife Flowers Laugh Forget Listen Yesterday Should Block Half Farther Lose Hundred Sewing North Sugar Tool Whole

9 years Holiday Flight Hunger Friendship Quit Honest Remember Pigeon Study Weigh Important Blood Kettle Comb Nobody Fought Painting Prove Unless Rough

10 years Crocodile Beauty Human Business Program Character Scrambled Cough Tomato Height Divide Chalk Elbow Freight Example Pleasure Lame Review Quilted Wrist

Table 6.8 Words available on the Boder reading test

There are advantages of to using the list in Table 6.8:

• The words are already graded into difficulty making the selection of target words

easier e.g. 2 from each level for each list produced.

• Phonically regular words have been identified

Equally there are flaws with the lists producing disadvantages:

• magic e appears in both phonetic and non-phonetic e.g. came and table

• ‘ir’ is considered to be a visual match rather than a phonetic match e.g. bird, girl;

but ol or il is not e.g. holiday, child’

• words are not matched for length or number of syllables or morphemes

• the frequency of occurrence in reading, writing or spoken language is not known

or controlled

This means that further consideration had to be made when selecting the words. The

words chosen are shown in Table 6.9 along with their spatial representation.

Age grade Verbal A Verbal B Spatial A Spatial B 6 years Sat Dog Sat Dog

Hand Fish Hand Fish 7 years Show Step Show Step

Nest Well Nest Well 8 years Block Child Block Child

Flowers Hundred Flowers Hundred 9 years Unless Kettle Unless Kettle

Hunger Nobody hunger Nobody10 years Human Elbow Human Elbow

Example Quilted Example Quilted Table 6.9 Words selected for the learning experiment

The disadvantage of this list is that words have not been controlled for prior learning,

word familiarity, meaningfulness, frequency of use in English language or

visualisabilty of each word. An alternative approach to control for these factors might

have been to use non-sense words. A selection of such words, graded for difficulty

and to correspond to the National Literacy Framework could have been taken from

Palmer and Reason (1999) (Table 6.10).

Grade Verbal A Verbal B Spatial A Spatial B Red level Jux Vup Jux Vup

Rek Cal Rek CalOrange level Nach Dack nach Dack

Shup Bith shup Bith Yellow level Colp Stim Colp Stim

Twuf Feng twuf Feng Green Level Clist Dwolt clist Dwolt

Sprext Fratch Sprext Fratch Blue Level Lipe Jote Lipe JoteIndigo Level Prounch Trowmp Prounch Trowmp

Table 6.10 Alternative word lists considered

The difficulty with these words is that they will need to be dictated to the child. There

may be more than one way of saying each e.g. nach could rime with back or watch.

6.5.2 Procedure for administration during the pilot study

The words were printed on individual plastic coated flash cards and kept in 4 separate

envelopes with the list name on the outside to make identification easier.

Pupils were selected to receive one of the four counterbalanced presentations:

Verbal A then Spatial B Spatial A then Verbal B Verbal B then Spatial A Spatial B then Verbal A

Switching the order of presentation from Visual first to Spatial first was to control for

order effects such as practice effects or fatigue effects across the sample. Presenting

each list in each format allows for control of differences in the words used in the lists

(e.g. frequency encountered in language, meaningfulness and visualisability).

Each list was presented to the pupil in the same order, moving from easier words to

more difficult words. The child was cued into one perceptual feature of each word on

the list (letter sound or colour). Once the whole list had been presented, the child was

asked to write each word down to dictation using sentences from the Boder test. This

was repeated up to 10 times, or until the child spells all 10 words correctly.

6.5.3 General instructions

A set of instructions was formulated to control for variations in the presentation of the task.

“We are going to try to find out the best way for you to learn how to spell some words. In a

moment I am going to show you a word and ask you to remember it. I will then show you

another word and ask you to learn it. All together I will show you 10 words. Then we will see

how many you can write down as I say each word from the list.

I do not expect you to get them right first time. If you are not sure about a word then have a

guess. We will repeat the spellings 10 times and I will record your score each time.

When we have finished the first list, we will do the same with a second list. This time we will try

to learn the spellings in a different way.

When we have done both lists, we should be able to see which way you learn the quickest. You

will be able to tell me which way you like the best.”

Verbal lists

The words are produced in monochrome. One word is placed in front of the child at a

time. The child is asked to say each letter sound out loud. “tell me the sound of each

letter in this word” This is repeated with the next word until the end of the list is

reached.

Spatial lists

The words are presented in 3 colours and the child’s attention is drawn to this fact.

The child is asked to note which letters are black, red and blue. “tell me the black

letters, tell me the red letters, tell me the blue letters”. This is repeated for all of the

words in the list.

6.5.4 Testing

Each word was read out for the child to spell to dictation. A total is recorded for

words spelled correctly.

A new piece of paper was given to the child and the procedure repeated until the child

had written each word 10 times.

6.5.5 Scoring

Initially scoring the word as correctly spelled or incorrectly spelled was considered.

However, one problem with doing this is that children with a phonological preference

may write words that are phonological plausible but which do not match the target

word. Effectively this is a wrong answer but it is better than an answer consisting of a

random jumble of letters. Equally children with a visual preference may get some

visual features correct e.g. the right letters but in the wrong order.

The second problem with whole word scoring is that it does not allow smaller

increments of learning to be detected. In order to get around these two difficulties it

was decided to have a scoring system that counted the right letters in each place in

each word.

It could be anticipated that for each child there will be two scores produced:

• number of trials to spell all 10 words using a visual perceptual learning cue

• number of trials to spell all 10 words using a verbal perceptual learning cue

However, some children will not achieve a perfect score an alternative measure is

produced:

• number of words learnt

For the purpose of the pilot study these were combined into a single score for each

condition by assuming that once a child has achieved a perfect score they have learnt

the word and would achieve perfect scores on subsequent trials. They were given

credit for successive trials and a total number of words learnt calculated.

E.g. a child gets 3 words right on the first trial, 4 on the second, 7 on the third, 8 on

the fourth and 10 on the fifth. Credit is given for trials 6 to 10. The overall score

becomes:

3+4+7+8+10+(5x10) = 82

6.5.6 Changes after the pilot study

The materials were tested with 4 pupils. The word lists were found to be suitable with

all 4 pupils having some words that could be learnt. The children tended to get bored

with repeating the same spellings ten times and the amount of time required was more

than could be reasonably given because of the relative demands on EP time. It was

found that 5 trials was sufficient to show a difference in learning under the two

conditions and this was chosen as the number of learning trials for subsequent pupils.

In addition, the following changes were made as a result of the pilot study:

The spelling test was carried out first before any learning trials had taken place to

establish a baseline. This was because 3 of the pupils used in the pilot study were able

to spell some of the words already.

Measure success by counting the letters correctly placed by the child.

Change the procedure for cueing the child in to the visual features by asking them to

tap the coloured squares on the cue-card without saying anything.

If the child was able to get all of the words right (i.e. scores 52) during a trial, then

one more trial was done to ensure that this was not just a chance event (up to a

maximum of 5 trials).

Example: Condition Baseline Trial 1 Trial 2 Trial 3 Trial 4 Trial 5 Verbal 23 25 28 28 30 32 Spatial 25 24 25 27 29 32

Verbal score = 32-23 = 9 Spatial score = 32-25 = 7

If the child has achieved a perfect score before completing 5 trials then arrive at a

score for both conditions based on the same number of trials.

Condition Baseline Trial 1 Trial 2 Trial 3 Trial 4 Trial 5 Verbal 23 34 52 52 Spatial 25 24 25 27

Trial 2 is the perfect score for Verbal learning, trial 3 confirms this. Using trial 2 for

both conditions gives:

Verbal score = 52-23 = 33 Spatial score = 25-25 = 0

6.6 Analysis of spelling errors in naturalistic writing.

The study was extended to explore this possibility further by using archive samples of

handwriting for 7 further pupils with known cognitive profiles. The composition of

this opportunistic sample is shown in Table 6.11.

Boys Girls Total Number in sample 5 2 7 Age range (years: months) 7:1-11:0 10:8-10:10 7:1-11:0 Mean Age (years: months) 9:2 10:9 9:7 IQ range 74-124 80-94 74-124 IQ mean 104.2 87 99.29

Table 6.11 Composition of sample used for naturalistic spelling analysis

Each handwriting sample has been used to identifying patterns of spelling errors.

Spelling errors can be classified in a number of ways. Treiman (1997) outlined the

types of writing summarised in Table 6.12.

pre spelling ‘writing scribble’ Marks made are smaller than

drawings and usually in a line Syllabic-grapheme Each syllable is represented by a

single letter Phoneme-grapheme One letter per sound

• Phonemic representation (e.g. it for eat)

• final consonant omissions • vowel omissions • immature speech patterns

evident as immature spelling e.g. ‘dr’ becomes ‘jr’, ‘tr’ becomes ‘ch’

Start of orthographic awareness Phonological correctness takes precedence over orthography – phonologically plausible errors

Visual checking of orthography Correcting words from ‘how they sound’ to ‘how they look’

Morphemic rules appear Meaning and etymology take precedence over phonology. Errors now involve using the • wrong morpheme

Table 6.12 Summary of writing types by developmental sequence

However Treiman is writing from a phonological perspective and this does not take

into account a number of visual errors that were identified in the learning experiment:

• gestalt errors involving closure e.g. seeing ‘i’ as a single line like ‘l’

• visual sequence errors right letters but in the wrong order

• visual orientation errors e.g. n/u, b/d, p/q type confusions

• visual form similarity errors e.g. n/h, l/j, a/e/o type confusions

Previous studies have identified also global gestalt errors in which the word has the

right start and end letters and is about the right length (Boder and Jarrico, 1982).

A case study approach is used to try to answer the question, ‘Does the cognitive

profiling preference identified by the BAS-II have ecological validity in the types of

spelling errors made by pupils in free writing?’ Samples of handwriting are analysed

and spelling errors classified according to the types of errors identified above. Some

spelling errors may have more than one interpretation. The classified errors are

presented in Appendix 13 and summarised as a count of error types in Table 7.11.

6.7 Summary of the methodology

Five measures have been selected as possible methods for assessing cognitive

preference and as such each one categorises pupils into either having a verbal

preference or a visuo-spatial preference. The level of measurement is categorical (or

nominal) in each case.

For each pupil participating in the main study the following measures are available:

• BAS-II verbal reasoning and spatial reasoning standard scores used to identify

cognitive preference as either verbal or spatial

• Learning experiment number of letters correctly placed in the final learning trial

compared to baseline measures under each condition of cued learning. The biggest

improvement is used to identify the pupil as having a spatial, verbal or neither

type of learning style (and cognitive preference).

• A spatial score from the q-sort that is positive indicating a spatial cognitive

preference in selection of pastime activities; zero, indicating no preference; or

negative, indicating a verbal cognitive preference.

• 3 sets of scores from the teacher questionnaire to indicate whether the pupil is

perceived as having a spatial, verbal or neither cognitive preference.

For the 7 pupils participating in the spelling analysis the following measures will be

available:

• BAS-II verbal reasoning and spatial reasoning standard scores used to identify

cognitive preference as either verbal or spatial

• The number of spellings of each type categorised into visual errors or

phonological errors.

6.8 Choice of data analysis

Five assessment techniques are explored in this project to see if there is agreement in

the type of cognitive preference identified. This places the level of measurement at the

nominal level – pupils will either be found to have a verbal preference or a visual

preference. Descriptive statistics can be used to explore mean differences in the two

types of processing and the distribution of scores between the categories for each

assessment method used. This can give an indication of any agreement on a case by

case level and also for the sample as a whole.

Analytical statistics can be used to explore whether or not random chance effects

could have led to the same pattern of results being produced. The level of

measurement is not at the level of interval data and this means that parametric tests

cannot be used. Nominal data can be interpreted using chi squared. However, the

sample size used is small and this means that some cells are likely to have small

numbers, requiring the use of the Fischer Exact Test. This is not a very sensitive test

and this means that if an effect is not found that this will need to be interpreted with

caution.

In each comparison, the probability levels will need to be considered for one-tailed

hypotheses. It is predicted that a child having a verbal preference on one measure will

also have a verbal preference on the other measures. Similarly a child having a visual

preference on one measure will have a visual preference on other measures.

The learning experiment is a repeated measures design with pupils completing both

types of cued learning. This allows performance increases between each type of

learning to be compared using a Wilcoxon Signed Rank test. This is a non-parametric

test selected because the data cannot be assumed to be normally distributed

(precluding the use of parametric tests such as the related t-test), but the level of

measurement is at the interval level with actual scores being used rather than

categorical data. A one-tailed hypothesis is tested with the prediction that a verbal

preference identified on the BAS-II will lead to better learning when verbal cued

recall is used. Similarly, a child with a visual preference identified on the BAS-II is

predicted to learn better when visual cued recall is used on the learning experiment.

Chapter 7 Results

7.1 Introduction

This study has been concerned with the identification of assessment methods that help

to identify pupils for a particular type of intervention. The initial exploration of pupil

cognitive profiles outlined in Chapter 4 has suggested that this approach might be

useful for a small percentage of pupils who are referred to the County Psychological

Service with difficulties in spelling. A rough estimate suggested that this group of

pupils with identifiable cognitive profiles represents about 1% of all pupils.

Nonetheless, they are a group who are having extreme difficulty in acquiring spelling

skills and for whom the standard approaches to literacy remediation have not

appeared to be helpful.

The main part of the study has been to explore whether the identification of cognitive

preferences using different assessment techniques is helpful when cueing recall during

the learning of spelling. This study is concerned with trying to identify two particular

subtypes of poor readers or poor spellers and then seeing if this helps to select a

method of cueing recall to help the pupil improve spelling accuracy.

A number of questions have been addressed:

• Does the categorisation of pupils on the basis of BAS-II cognitive profiles into

subgroups that have either verbal strengths or spatial strengths reflect:






writing subtasks, which on face validity; depend on either verbal skills




• Can pupil performance on the learning experiment be predicted accurately

using the teacher questionnaire or the pupil choices or the BAS-II profiles?

• Do pupils with particular profiles tend to make less spelling errors consistent

with their cognitive preferences?

This chapter will present the findings in the following order:

• The first question is about the extent that the different assessment techniques

agree with each other. A number of measures were achieved for all 17 pupils

aged 8 to 11 years who participated in the main study. This raw data is

summarised in the appendix (see Appendix 12). A comparison of the different

measures is made by showing inter measure reliability (7.2)

• Comparison of the BAS-II with the other measures (7.3-7.5)

• An outline of qualitative observations made at the time of the learning

experiment (7.6)

• An extension of the study to consider naturalistic samples of handwriting for a

further 7 pupils and these results are presented towards the end of the chapter

(7.7)

7.2 Inter measure reliability

Background research information provided in Chapter 2, 3 and 4 suggests that

cognitive preferences occur because of the way that information is processed and

handled at the neurological level. A major division between verbal processing and

visual processing occurs and in some individuals there may be apparent strengths or

weaknesses between these systems. If this is true, then it would be expected that an

individual would be pre-disposed to approach the world through their preferential

processing modality. It would follow that this would be pervasive and have wide

ranging effects that should be detectable through a range of measures. We could

predict that if all of the measures were good measures and if the pupil had a strong

cognitive preference then there would be a high degree of agreement between the

measures used in the study.

In principle, for each measure there are three possible outcomes: the pupil can be

categorised as having a verbal preference, a visual preference or neither preference.

The lowest chance of any two measures producing the same preference randomly is

1/3 x 1/3 = 1/9, or 11.11%. However, in practice the BAS-II can only have 2

possibilities since this was used to select participants. The other measures could

identify some children as having neither preference so this would bring the random

chance up slightly to almost 17% (when comparing with the BAS-II). The raw data

(Appendix 12) for the remaining pupils reveals that there are patterns of agreement

and disagreement between the different category measures used. All of these

agreements exceed the predicted chance level. The weakest agreement is between

teacher within child ratings and the outcome of the learning experiment. The strongest

agreement is between the BAS-II and the learning experiment (almost 4½ times the

chance level). This is summarised in Table 7.1.

Agreement out of 16 pupils

BAS-II Learning Exp Q-sort Teacher

overall Teacher within

Teacher related

BAS-II 75 56 63 44 63

Learning Exp 75 56 50 31 56

Q-sort 56 56 69 50 63

Teacher overall 63 50 69 56 75

Teacher within 44 31 50 56 44

Teacher related 63 56 63 75 44

Table 7.1 Percentage of agreements between categories of pupil identified by different measures

(Percentages are rounded for clarity)

In this study the pupils were selected on the basis of their BAS-II scores, with 8

having a relative verbal strength and 8 having a relative spatial strength. The other

measures did not produce the same distribution of strengths and weaknesses. This is

summarised in Table 7.2.

Number of pupils with each strength

BAS-II Learning Exp Q-sort Teacher

overall Teacher within

Teacher related

Spatial 8 7 10 14 9 11 Verbal 8 8 5 2 7 2 Neither 0 1 1 0 0 3

Table 7.2 Distribution of cognitive preferences identified by different measures

Performance on the learning experiment seems to most closely match the distribution

of pupils identified by the BAS-II profiles. However, the distribution of scores on the

learning experiment could simply be random. Each set of measures is now considered

in more detail in relation to three questions raised at in Chapter 5 (5.4).

7.3 BAS-II compared to pupil choices

Pupils were selected for the study on the basis of their BAS-II cluster scores with the

assumption that this reflects underlying neurology (discussed in Chapter 4). This leads

to the possibility of a pervasive predisposition to approach the world through a

predominantly verbal modality or a spatial modality. This should be reflected in the

choices of activities that pupils choose to do as part of their pastime activities. In

Chapter 5, this lead to the following question:

Does the categorisation of pupils on the basis of BAS-II cognitive profiles into

subgroups that have either a verbal preference or a spatial preference reflect pupil

perceptions of their preferences in choice of activities?

The degree of agreement is summarised in Table 7.3

Q-sort Spatial Q-sort Verbal

BAS-II Spatial 6 2BAS-II Verbal 4 3

Table 7.3 Relationship between BAS-II and Q-sort category identification

Table 7.3 shows that nine of the children’s ratings of their preferred activities agree

with the style found using the BAS-II. When subjected to a chi squared analysis this

produced a chi squared value of 0.536. When the Fisher’s Exact Test is used this

produces a one-tailed probability of p=0.427. This means that the null hypothesis

cannot be rejected and the distribution of the q-sort categories relative to those on the

BAS-II could be attributed to chance. The two measures are not reporting the same

cognitive preference with a high degree of agreement. This suggests that cognitive

preferences identified on the BAS-II are not evident in the choices children make in

their pastime activities or that the q-sort does not adequately detect the preferences.

One reason for this may have been that the children were over-loaded when twenty

items were presented to them. This did not seem to be the case, with children taking

their time to select items.

A second possibility is that the children were unsure of what the item cards were

showing. Although a picture was added to each to act as a visual cue to compensate

for poor reading, they may have confused similar looking picture items. At the time of

presentation I read each item out to the child. As each item was selected, I read the

text out to check with the child. None of the children placed cards down or swapped

them at this point.

A third possibility is that the items selected do not represent items that children of this

age would choose to do. The item validity was checked by asking other adults

whether they thought the items were primarily verbal or spatial in task. It might have

been better to try to gauge the types of activities that children like to by surveying a

large number of children aged between 8 and 11 prior to the study. However, most

children found it easy to pick the items that they most wanted to do from the list and

the items they would least like to do. They found it more difficult to work through the

other layers of the q-sort technique. One child did not want to reject any items on the

last sort. This suggests that the items selected were appropriate for children of this

age.

7.4 BAS-II compared to teacher ratings

The selection of pupils for the study was based on their cognitive preference identified

by their BAS-II cluster scores with the assumption that this reflects underlying

neurology (discussed in Chapter 4). This leads to the possibility of a pervasive

predisposition to approach the world through a predominantly verbal modality or a

spatial modality. This should be noticeable by teachers and reflected in how pupils

approach literacy tasks. In Chapter 5, this lead to the following question:


subgroups that have either a verbal preference or a spatial preference reflect teacher

ratings of classroom performance in literacy?

The correspondence between BAS-II types and teacher ratings in the different parts of

the questionnaire is shown in Table 7.4.

Teacher Rating BAS-II Spatial BAS-II Verbal

Spatial 8 6 Overall

Verbal 0 2

Spatial 4 5 Within

Verbal 4 3

Spatial 7 3 Related

Verbal 0 3

Table 7.4 Agreement between BAS-II and teacher identified cognitive categories

The teacher ratings can be compared to the BAS-II type by categorising the

agreement. When this is done the Fisher Exact Test can be used to evaluate the

likelihood of the results being due to chance:

• Teacher overall ratings 10 agree with BAS-II, (Fisher’s Exact, p=0.233)

• Teacher within ratings 7 agree with BAS-II, (Fisher’s Exact, p=0.500)

• Teacher related ratings 10 agree with BAS-II and 3 show no overall

preference, (Fisher’s Exact, p= 0.070)

In each case the probability of the results being due to chance exceeds 0.05 and this

means that the null hypothesis cannot be rejected.

The agreement between the teacher ratings categories for cognitive preference and for

the BAS-II categories of cognitive preference is higher than predicted for chance

alone (see Table 7.1) but not high enough for the Fisher Exact Test to allow rejection

of the hypothesis that the results could be due to chance. This could be due to the

small numbers participating in the study and the lack of sensitivity of Chi Squared and

the Fisher Exact Test with such small numbers.

The three teacher measures would be expected to be most similar since the same

teacher is answering questions about the same pupil. However this is not the case. The

reason for this might be a lack of construct reliability in the questionnaire design or it

could be due to the teacher thinking differently about the pupil’s relative strengths and

weaknesses when asked to consider how each statement applies to a particular child

compared with how the child fares in the class. Overall, teachers seem to be indicating

that pupils are relatively stronger in their spatial skills than they are in their verbal

skills. Perhaps this view results from perceived difficulties that pupils have with

reading. The dominance of the phonological deficit hypothesis may well persuade

teachers that all pupils who struggle with reading do so because of poor phonological

skills (and therefore relatively better spatial skills). Alternatively, pupils may make

more phonological errors when reading or writing compared to visual-spatial errors

and this performance is observed by teachers (this is explored through analysis of

handwriting samples in the final part of the study).

The open-ended questions on the first part of the questionnaire allowed teachers to

add additional comments. These are presented in Table 7.5.

Pupil Comments regarding phonological

skills Comments regarding visuo-spatial skills

CM ‘is now able to sound out and read cvc words’ ‘is now able to make phonic attempts at spelling words’

‘can now recognise more key words’

JD ‘learns words by sight’ DS ‘vocabulary has improved’ RH ‘was identified early by Speech and

Language Therapist’

Table 7.5 Teacher comments related to cognitive processing

Not all teachers commented directly on either phonological skills or visuo-spatial

skills. The few comments received is too small to generalise across the sample but the

ratio of phonological comments to visual comments is 2:1. No teachers commented on

a phonological regularity effect evident in some children’s spellings.

In a previous study, it was found that teachers used both phonological and visuo-

spatial explanations for literacy difficulties and dyslexia (Squires, 2001). The

constructs used by teachers are reproduced on the next pages in Figure 7.1 and Figure

7.2.

Concentration

MemoryIntelligence

Processing

A collection of difficulties

Recording

DecodingSpecific difficulties

Confusion

Teacher Constructs of Dyslexiaby defining the concept

"problems with concentration (but not behaviour)"

"problems with rote learning number bonds""Quite intelligent (but not all dyslexics are)""Children may be really good academically andin conversation but experience problems withwriting coherently."

"problems with processing information""Difficulty with words based on neurologicalprocessing which differs from that ofnon-dyslexic pupils"

"Many aspects""There are different types of dyslexia."

"I feel the term dyslexia is too muchof an umbrella term. I can see traitsof it in all the children in my class- so it is not very useful."

"recording problems"

"Not being able to decipher symbols""disruption of symbols (letters/numbers) in a set order"

"Trouble with reading, writing and spelling."

"Confusion with numbers and letters""Jumbling reading, spelling andnumber - in columns and in words""...confusion with reading, writing and number work.""letterforms can be reversed and in the wrong order""Getting letters backwards or havingthe wrong order to letters."

Figure 7.1 Construct Map for dyslexia (Source: Squires, 2001)

Poor readers

Poor Spellers

Good readers

Good Spellers

Teacher constructs of literacydifficulties by comparing pupils

"lack of reading experience at home""Lack of phonological skills"

"In their ability to arrange letters in the correct format"frustrated by his inability and this affects behaviour

Confidence

"less confident than good readers"lack of confidence before opening book, hard to motivate

"hesitant with board/maths/English""often believe they are 'doing wrong' because theydo not feel able to cope with reading questions or tasks"

Independence

"difficulties encountered when doingindependent researching for units of work"

"may not progress as quickly-need more help with research"

"more teacher dependent - they needlots of reassurance to carry out tasks"

"In their ability to arrange letters in the correct format"does not know that their spellings are wrong

"seem easier to help with"

Language Use

Poor speller is unable to structure sentences"won't use a range of vocabulary in stories"

very good orally at science and good at maths"not reinforced phonic sounds"

Affective

they are prepared to ask for help, they try to self-check"gets down heartened, more so than with reading"

"more teacher dependent - theyneed lots of reassurance to carry out tasks"

"often believe they are 'doing wrong' because theydo not feel able to cope with reading questions or tasks"

" don't attempt a word"

"more aware and experience of reading"Good reader better at writing than the poor readersgood at recognising symbols in their correct format.very poor orally but reads avidly

Confidence

"more confidence in getting on with tasks""more confident than poor readers""more confidence with comprehension and copying from board""more confident and cope better""more confident and work with greater understanding and speed""They have more self confidence andwill attempt new ideas more readily""set themselves challenges and are more adventurous""stand to read to the class and volunteer to take part in plays"

Independence"do not need help and have betterself-esteem, do not need additional support""are independent, have more self-relianceand their quality of work is better"

"can retain good information on the correct structureof symbols for spelling and can transfer that to paper""very good all round"

Confidence

"more confident and cope better""confidence with story/sentence writing""more confidence in getting on with tasks""have a go at a word""more confident and work with greater understanding and speed""more self confidence and will attempt new ideas more readily"

Language use"variety of language and use of words,confidence with words""is able to use a larger vocabularyand can write good sentences"

Independence"do not need help and have betterself-esteem, do not need additional support""are independent, have more self-relianceand their quality of work is better"

Figure 7.2 Construct Map for literacy difficulties (Source: Squires, 2001)

Further analysis was done by paraphrasing the emergent pole from teacher responses

and donating a contrast pole to produce bipolar constructs to show the extent of

construct overlap and this is summarised in Table 7.6.

Dyslexia Concept Overlap Literacy Difficulty Concept

• many aspects/traits �� single difficulty

• different types of dyslexia �� single types of literacy difficulty

• problems with rote learning �� rote learning effective

• Confusion with textual information �� success with textual information

• Sequencing difficulties �� success with ordering letters

• Intelligence �� lack of intelligence

• Neurological processing difficulty �� neurological processing efficiency

• Good oral/aural �� poor written

• Poor concentration �� good concentration

• Trouble with reading, writing and spelling �� skilled at reading, writing and spelling

• Not being able to decode symbols �� effective symbolic decoding

• Lack of experience of reading �� good experience of reading

• poor phonological skills ��good phonological skills

• poor behaviour due to frustration �� not frustrated

• low confidence �� high confidence

• independent �� dependent

• self-reliant �� teacher reliant

• good quality of work ��poorquality of work

• slow ��fast • does not need

reassurance �� thinks everything is wrong

• hard to motivate �� well motivated

• extensive vocabulary in writing �� restricted vocabulary

Table 7.6 Constructs used by teachers (Source: Squires, 2001)

There is a difference in the constructs used when defining literacy difficulties as

opposed to dyslexia. Teachers use phonological difficulties as an explanation for

literacy difficulties and dyslexia, but visual difficulties are only included in their

concept of dyslexia. Perhaps this is skewing questionnaire responses so that teachers

could be looking and expecting to find phonological problems and therefore rate

children’s visuo-spatial skills as being better.

7.5 BAS-II compared to learning experiment

A cognitive preference that reflects underlying neurological or cognitive processing

would be expected to indicate the modality through which learning could proceed

most efficiently. Of the different measures used to assess cognitive preference, the

BAS-II had the greatest agreement with the type of learning cued. This addresses the

question:


subgroups that have either a verbal preference or a spatial preference reflect

subsequent performance on a learning experiment in which recall of spellings is cued

by sound or visual features?

A total of 17 pupils participated in this part of the study with results summarised in

Table 7.7. One pupil (CM) did not attend equally on both parts of the experiment,

preferring to count to number of letters that were each colour rather than looking to

see what each letter shape was.

At first glance, over half of the pupils (9) used in the study appear to learn best when a

verbal cue is used for learning and this seems to support the phonological deficit

hypothesis. On closer analysis, 12 pupils learn best when the learning cue matches

their cognitive preference.

The following analysis excludes his data and is based on 16 pupils with data counted

only if it showed a preference:

Learning Spatial

Learning Verbal

BAS-II Spatial 6 2 BAS-II verbal 1 6

Table 7.7 Relationship between BAS-II profile and performance when learning

This has been analysed using SPSS for windows and Chi Squared:

• sample (excluding CM), Fisher’s Exact Test p=0.032

The probability is less than 0.05 so the null hypothesis can be rejected, in favour of

the alternate hypothesis. Learning style appears to be affected by cognitive processing

preferences when these are identified on the BAS-II. This suggests that the BAS-II is

a good predictor of the modality that is most effective for learning spellings for

children that have difficulty in this area.

Developmental models would predict that younger children would tend to a verbal

preference in learning style. This was taken into account as much as possible when

selecting participants by trying to match ages of pupils with a verbal preference to

those with a spatial preferences (within a couple of months). The mean age of pupils

for each BAS-II category is summarised in Table 7.8.

Excluding CM Including CM

BAS-II type Mean Age (months)

N Std. Deviation Mean Age (months)

N Std. Deviation

Spatial 115.00 8 9.472 113.67 9 9.721 Verbal 115.63 8 10.663 115.63 8 10.663 Total 115.31 16 9.748 114.59 17 9.900

Table 7.8 Recruitment ages for each condition

The age of pupils responding best to each condition of the learning experiment is

shown in Table 7.9.

Excluding CM Including CM

Learning type Mean Age in months

N Std. Deviation Mean age in months

N Std. Deviation

Neither 113.00 1 . 113.00 1 . Spatial 116.14 7 7.904 116.14 7 7.904 Verbal 114.88 8 12.182 113.56 9 12.063 Total 115.31 16 9.748 114.59 17 9.900

Table 7.9 Ages for each condition based on performance

There is no real difference in the ages of pupil either in terms of recruitment or

performance on the learning experiment. This suggests that observed differences are

not due to developmental differences related to chronological age.

For some pupils the difference in the increase in performance in each condition is not

very different. For JG, the increase in accuracy was the same under both conditions.

When the mean increase under each condition is compared, then there is a slightly

better increase when the cue matches the cognitive preference identified on the

BAS-II (Table 7.10).

Learning Exp N Mean Std. Deviation Minimum MaximumPreferred score 16 9.50 4.290 1 17 Non-Preferred Score 16 8.13 5.353 0 18

Table 7.10 Mean improvement during learning across the sample

There is a slight increase in performance when the learning modality matches the

cognitive preference When submitted to a Wilcoxon ranked sign test analysis the

probability of this difference being due to chance is 0.038, sufficient to allow the

rejection of the null hypothesis. Although there is only a slight difference, it can be

attributed to the experimental manipulation. The small overall difference is partly due

to there only being 5 learning trials, with more opportunities for learning to take place

the size of the difference would be expected to be greater.

Further analysis is needed of what happened for particular pupils to provide more

insight into the utility of the learning experiment as an assessment tool. This is

possible using notes made at the time of the assessment and by looking at the types of

changes made to spellings as pupils moved through each trial.

7.6 Case Studies The case studies are derived from qualitative observations made at the time of the

learning experiment.

7.6.1 CM

CM provides an insight into the difficulties of using prompts to trigger particular

types of cognitive processing. Although he was asked to look at the letters, he chose

to count how many were of each colour – it is unclear as to whether or not he attended

to shape or simply responded to colour. In contrast, he spent a lot longer attending to

the sounds of the letters during the verbal cued learning. Under both conditions, he

sounded out the letters when writing during the spelling tests. On the BAS-II reading

test, he struggled to decode words and took a long time to find the right sounds for

individual letters and then blend them to make the target words. His teacher

commented that a lot of effort had been made to teach him using a synthetic

phonological approach.

CM’s errors do not split neatly into one type of processing error or the other.

• ‘Child’ went through a number of transitions: Chid � chld � Cheild The first

attempt is similar to that predicted by Treiman, there is a phonological

immaturity in the ending of the word. The second attempt could be interpreted

as either a visual confusion based on the previous phonological error

(swapping l for ‘i’ by making a visual gestalt), or as a visual error involving an

omission of a vowel, or as phonological error involving an omission of a

vowel. The third attempt is interpreted as a phonological error //che/long i/ld//

or //ch/ei/ld//.

• ‘elbow’ included both visual errors (reversal of b) and orthographic errors

(using ‘oh’ rather than ‘ow’ to correspond to the final phoneme) to produce

‘eldoh’

7.6.2 AW

Overall AW learnt better using the verbal learning and recall cue but his verbal

performance dipped on the penultimate trial. I asked AW about this and he told me

that the verbal learning is, ‘a bit boring’ and a lot ‘harder than just tapping’ (tapping

the coloured squares corresponding to the different letters). This could suggest an

emotional component contributing to motivation to learn in a particular way. For AW,

he preferred to use the visual cue but his performance was better when using the

verbal cue. His preference on the learning experiment corresponded to his preference

on the Q-sort, his performance corresponded with cognitive profile identified on the

BAS-II. The role of verbal processing was evident in AW’s tendency to sub-vocalise

letter sounds when writing. It might be possible to reconcile these differences in

remedial teaching by starting the teaching from a sound based approach but when AW

makes a mistake to then focus on correcting the error using visual mnemonics or

visualisation to engage a more positive emotional drive for learning.

AW was able to use the next learning trial as a mechanism for self checking

performance on the previous trial e.g. “oh! I’m missing one letter out of hand”. The

role of emotion was also evident in AW’s unwillingness to engage in risk taking. On

several words he told me, “that word was right in my head – but I didn’t put it down”.

AW was able to decode double consonant clusters that represent single phonemes

such as ‘st’ and ‘ch’. He was unaware of orthographic rules such as ‘u’ always

following ‘q’.

7.6.3 JS

JS sounded out each individual letter of each word but did not automatically blend

sounds. Errors tended to be phonologically plausible e.g. ‘hundrid’, ‘quiltid’

‘chi//uld’, ‘sampul’, ‘h//yoo//man’. Some visual corrections were made for example

changing ‘cetul’ � kettle; ‘elobowe’ � ‘elbow’

7.6.4 JG

JG had no real preference in his choice of activities on the q-sort and this

corresponded to his performance on the learning experiment with his scores

increasing by the same amount using both cues.

7.6.4 JM

JM was very well motivated and keen to see how his spelling scores increased after

each learning trial.

JM made visual errors in his early attempts at spelling e.g. substituting ‘m’ for ‘h’ to

make ‘muman’ for human; ‘g’ for ‘n’ to make hugger for hunger. In each case these

were corrected with further exposures to the stimulus material.

JM was able to split words into phonemes without prompting. He was heard to read:

• h//an//d

• sh//o//w

• n//e//st

• un//less

The BAS-II preference suggested that he would learn better using the verbal prompt

but this was not the case. This was because he had to concentrate much harder on

identifying the letter colour and matching it to the cue card than when he was saying

the letter sounds. In order to succeed, he had to allocate additional attentional

resources when using a visual strategy and this additional effort made memory more

effective.

7.6.5 DS

Although DS had been very co-operative and enjoyed the cognitive assessment, he

was extremely resistant when it came to the learning experiment. He was fidgety and

needed heavy prompting to re-focus his attention. This emotional response to literacy

tasks was supported by observations from his teacher regarding performance in class.

DS made no progress on the verbally cued learning task but did improve his

performance using the visually cued learning task. This supported the BAS-II profile.

He told me that the spatial task was ‘easy’ and he tended to choose visuo-spatial

activities using the q-sort.

7.6.6 RH

RH offered rhyming words for each word presented without any prompts e.g. I said

‘well’, he responded, ‘bell’. This led to me considering that his teacher had spent a lot

of time working on developing his phonological awareness. This was checked out

afterwards and I was told that he had presented with early speech difficulties and a

speech and language programme had been run in school to develop his articulation

and awareness of sound.

Despite having a strong spatial preference in his BAS-II profile and performing better

on the learning experiment when visual cues where used, he tended to make visual

errors. He readily confused:

• n with h gestalt error involving visual form

• b with d a visual reversal

• l with ‘i’ a gestalt error involving visual closure

7.6.7 AK

AK was very hard to motivate during all phases of his assessment. He continually

tried to avoid demands placed upon him by:

• saying ‘this is boring’

• complaining, ‘how many more do we have to do?’

• asking, ‘aren’t we going to do something else?’

• saying, ‘I can’t do …’ even when tasks were well within his capability

• attempting to distract through engaging in conversation

Despite these difficulties during the assessment, all measures suggest that he is better

at spatial processing than verbal processing.

Errors in spelling tended to be visual rather than phonological e.g.:

• chude for child possible gestalt linking vertical lines from ‘i’ and ‘l’ to

form ‘u’

• stoep for show

• ledow for elbow visual sequence error

• flowse for flowers combined phonological error and visual sequence error

(fl//ow//es/ with the ‘es’ reversed)

• nodoby either visual reversal or a visual sequence error

Some errors were phonological:

• hunga

• exzample

• ketl

• blok

7.6.8 SH

SH was able to utilise visuo-spatial cues and to internalise the cues. At one point she

told me, ‘I did hand without using the colours to help’.

7.6.9 SJ

SJ learnt better using a verbal cued strategy compared to a visual cued strategy

supporting the BAS-II profile. He tended to spell words phonetically and a sample of

handwriting from class confirmed that this was true about his free writing. Errors in

class work included:

• tim (time) orthographic error – not understanding how the position of the

e modifies the vowel sound

• wos typical phonological attempt at a non-phonological spelling

• cav (cave) orthographic error

• opn (open) missing vowel identified in Trieman’s paper

• wen orthographic error

• frew (threw)

• wot

• wel

• don (down) word ending difficulty identified in Trieman’s paper

• goien

• nevr

• a gen (again)

SJ tended to blend phonemic groups of letters automatically during the verbal cued

learning trials (‘st’, ‘un’, ‘sh’). During the visual trials he was heard to say ‘black, red,

blue’ when writing the words during the recall phase.

The ecological validity of the cognitive profile defined by the BAS-II seems to be

confirmed by SJ’s class work. However, one is a very small sample and this

concordance might be unique to SJ. This question is explored further in the next

section.

7.7 Ecological Validity of BAS-II cognitive preferences

The observations made from SJ’s class work and observations made during the

learning experiment lead to the following question:

Does cognitive preference identified by the BAS-II have ecological validity in the

types of spelling errors made by pupils in free writing?

In order to address this question, samples of spellings were analysed from the work of

7 pupils who had a known cognitive profile. The types of errors made are summarised

in the appendix (Appendix 13).

A simple count of phonological versus visual errors can be done for each BAS-II type.

An error that can be interpreted in two or more different ways within the same overall

processing type is only counted once. A word with more than one error within the

same processing type gets a count for each error. This is summarised in Table 7.11.

BAS-II profile Phonological Errors Visual Errors

Spatial strength (N=4) 19 12 Verbal strength (N=3) 13 25

Table 7.11 Summary of error types in handwriting samples

0

5

10

15

20

25

30

Verbal Spatial

PhonologicalErrorsVisual Errors

Figure 7.3 The proportion of spelling errors made by BAS-II preference

The data shows that pupils make both kinds of errors in their spelling but seem to

make more errors of the type that does not match their cognitive preference. No cells

have a count of less than 5 and this allows Chi Squared to be used. A one-tailed Chi

Squared test gives a probability of the pattern of results being due to chance as being

p=0.013. This allows the null hypothesis to be rejected. This suggests that pupils with

a spatial strength make more phonological errors than visuo-spatial errors and pupils

with an identified verbal strength make more visual errors than phonological errors.

This is a small sample but it adds ecological validity to the data already collected.

Chapter 8 Discussion

8.1 Introduction

This project started with the assumptions that children are different in the way in

which they process information and that fluid and competent reading and spelling are

complex processes. When there is a failure to learn to read and spell this is due to a

variety of reasons rather than having a single underlying causation.

Information has been presented from different research fields (neurology, cognitive

psychology, psychometrics, developmental psychology, and reading research) that

suggested that:

• Cognitive functioning required for reading and spelling is componential. That

is, different sub processes are involved and these are located in discrete areas

of the brain that act on particular types of information. A failure of any of the

sub-processes is thought to lead to difficulties with literacy acquisition. This is

a view supported by other authors (e.g. Sternberg, 1982; Rack, 1994).

• Different psychometric profiles are possible and these indicate the relative

strengths and weakness of different cognitive processes. Other psychometric

studies had shown that children who are considered to be dyslexic do not have

the same profiles. This study was based on an exploration of the cognitive

profiles of 99 children referred to the author as having difficulties with

acquiring literacy skills and compared to data provided by other authors

(Elliott, 2001; Turner, 2000; Dumount et al, 1996; Kurie, 2000) .

• The main types of processing that form the basis of this project are those

linked with visuo-spatial processing and verbal-auditory processing. But other

types of processing are also important (e.g. the processing of transitory stimuli

in the magnocellular layers; the development of automaticity in processing;

the integration of information from different modalities).

The current study explored this assumption by trying to identify cognitive preferences

between visual or verbal processing. Cognitive preference was defined in different

ways (Figure 5.2, page 103):

• Psychometrics were used to obtain standard scores for each of the three

clusters measured on the BAS-II. A cognitive preference was considered to be

a strength in one type of processing when compared to the other.

• As a choice of type of pastime activity that children would make from a

limited choice available. On the assumption that we choose the things that we

do the best and underlying neurological functioning would pre-dispose

children to select particular activities.

• On the basis of teachers noticing that children make errors that are consistent

with a weakness in one type of processing relative to the other.

The study explored the importance of identifying such a link by attempting to see

whether children who are identified as belonging to one particular cognitive

preference category would learn better if cued by the same modality. For example,

would children with a strength in verbal processing learn more effectively if spelling

was cued by sound rather than by colour? Conversely would children who have a

strength in spatial processing learn more effectively if cued by colour rather than

sound?

A learning experiment was devised that involved a counterbalanced repeated

measures design with 17 children learning to spell matched lists of words with either a

sound cue or a colour cue. This enabled learning to be quantified and pupil

performance classified as being better with one type of cue or the other. Observations

were recorded of behaviour during the experiment and analysis was done of the types

of errors made in spelling during the experiment. This provided supplementary

qualitative information.

The qualitative information about spelling types led to a further study of spelling

errors by classifying errors made by a further sample of 7 pupils in naturalistic

writing. The overall error type (visual or verbal) was counted and compared to

psychometric cognitive preferences. This was to explore the possibility that cognitive

preference may influence spelling behaviour.

8.2 Summary of the results

The initial data collected and analysed to look at the distribution of cognitive profiles

of a sample of 99 pupils came from pupils referred to the author as part of ongoing

work as an LEA educational psychologist. Not all of the children would be considered

to have a severe and persistent difficulty in acquiring literacy skills to warrant a

definition of being dyslexic (BPS, 1999). The pattern for those that would be

considered to be poor spellers (Figures 4.2 on 88 and Figure 4.3 on page 89)

suggested that:

• Not all pupils with literacy difficulties have the same underlying pattern of

strengths and weaknesses. This supports the view expressed by Curtin, Manis

and Seidenberg (2001) that developmental dyslexia is not a homogenous

condition. This is contrary to a mono-aetiological explanation of dyslexia (e.g.

Van der Wissel, 1987)

• Just over half of pupils referred have a flat profile and this represents the most

common profile identified amongst children with literacy difficulties. These

are referred to as ‘common garden poor readers’ (Elliot, 1989; Stanovich,

1991). Those with spelling difficulties could be referred to as ‘common garden

poor spellers’.

• A small number of pupils (about 20%) show a difference in their scores for

processing verbal and spatial information. In this study, this has been defined

as their cognitive preference.

• Other profiles have been noted, but have not been explored further in this

study.

• Similar distributions have been noted by other researchers and practitioners

(Turner, 2000; Dumount et al, 1996; Elliot, 2001; Kurie, 2000; McIntosh and

Gridley, 1993).

The second part of the study, involved 17 pupils. Two profiles were selected from the

BAS-II as approximating to verbal processing and spatial processing strengths or

weaknesses. These were selected on the basis of test design, derived from factor

analysis using the Horn-Catell model and thought to be indicative of the two types of

processing. Neurological evidence suggests that the two systems act independently

(are doubly dissociated) and a number of areas of the brain are involved in processing

each type of information. The investigations carried out in this study seem to suggest

that:

• The different assessment measures used (BAS-II, Teacher ratings, Q-sort and

the learning experiment) show some agreement on the categorisation of pupils

by cognitive profile (Table 7.1). This exceeds the predicted chance levels in all

measures but statistical significance was only found between the BAS-II and

the learning experiment (Fisher’s Exact Test p=0.032).

• Pupils can be grouped on the basis of BAS-II scores and this cognitive

preference is indicative of how the majority of these pupils learn spellings the

best when low perceptual cues are used to direct attention to either visual or

verbal aspects of the spelling of the target word (Fisher’s Exact Test p=0.032).

The difference between performance using preferred modality for cueing

recall and non-preferred modality was found to be significant (Wilcoxon

Ranked Sign Test p=0.038). The ecological validity of this finding was further

explored thought the analysis of spelling errors in independent writing.

Spelling errors in naturalistic samples of writing can be classified into pre-

spelling, visual, phonological, orthographic or morphological types of errors.

BAS-II profiles seem to match patterns of distribution of spelling errors made

by a small sample of seven children in normal independent writing. This

indicates that pupils make most errors of the type that matches their non-

preferred cognitive preference modality (Chi Squared Test p=0.013).

• BAS-II profiles did not agree with teacher perceptions of children’s learning

style at a level sufficient for statistical significance in this study. This may

have been due to too small a sample being used and a lack of sensitivity of the

Fischer Exact Test in detecting significance. Alternatively it may be due to a

lack of construct validity in the teacher questionnaire, however a higher

agreement was found with the BAS-II than predicted for a totally random

response. Thirdly, it may be due to the prevalence of the phonological deficit

hypothesis in education. Teacher phonological comments outweigh

visuo-spatial comments, but only for a very small sample of six comments. A

previous study revealed that teachers use both types of constructs when

thinking about dyslexia but only phonological constructs are used in defining

literacy difficulties (Squires, 2001). Given that teachers are more likely to

encounter common-garden poor spellers than those with a more distinctive

profile or cognitive preference then it is likely that teachers thinking will be

skewed towards the most common explanations. Slower development of

literacy due to general learning difficulties is more consistent with the

developmental models in which phonological skills must precede orthographic

and morphological skills (Treiman, 1997; Frith, 1985). This could have been

explored further through the use of exit interviews with teachers. If the

interviews revealed that teachers’ thinking is predisposing them to consider

that literacy difficulties have a predominantly phonological weakness as the

causation then it could be speculated that further training in the use of the

questionnaire might lead to a different pattern of results.

• BAS-II profiles did not agree with children’s perceptions of their cognitive

preferences (q-sort) when measured through the choices that they make in

pastime activities at a level that was statistically significant. This may have

been due to having too small a sample and a lack of sensitivity on the Fisher

Exact Test.

8.3 Methodological issues This part of the thesis will cover some of the methodological issues that have arisen

and discuss how these have been resolved (when they have been resolved) and the

implications for validity, reliability and sensitivity of measures used and results

obtained.

8.3.1 Development of the main part of the project and pilot studies When reading research articles in journals there is often the impression that the

project underlying the research effort emerged in perfect form. There are clear

research questions, reported methods and outcomes – it is hard to see the learning and

development that underlies the project being reported.

The development of the current project started with the initial observations that some

of the children who had literacy difficulties and were being assessed did not have a

verbal weakness. This seemed to contradict the phonological deficit hypothesis that

was dominating the educational psychology literature at the time.

My undergraduate training in psychology had been undertaken with the Open

University and I was influenced by the course on Cognitive Psychology. This led to a

re-reading of some of the general cognitive psychology texts (Eysenck and Keane,

1992; Aitkenhead and Slack, 1990; Greene and Hicks, 1984; Matlin, 1983) and

looking for possible alternative explanations for weaknesses in literacy development.

This led to broad models of cognitive processing that might be involved and to

thoughts about how differences in processing information through different cognitive

pathways might be apparent in classroom behaviour, particularly during literacy.

These initial ideas led to the development of the first version of the teacher

questionnaire. Questions were devised that were congruent with the initial reading

that had been undertaken, giving the questionnaire a degree of face validity and

content validity. At this stage, no other instruments were used to check for the

agreement in teacher responses on the questionnaire to give a measure of external

validity. This was at a very early stage in the development of the project, before the

research questions had been more tightly defined and before the bulk of the

background reading had been undertaken.

The initial pilot study of the teacher questionnaire was carried out by sending the

questionnaire to all schools referring pupils with literacy difficulties to the author over

a three-month period. 8 questionnaires were returned out of 10 sent out. Instructions

on the front of the questionnaire invited comments back from teachers. Initially the

plan had been to use teacher comments to check out the face validity, however,

teacher comments tended to be about how easy (or difficult) the questionnaire was to

answer. Not all teachers offered comments and perhaps a better strategy to have used

would have been to have a brief interview with the teachers completing the

questionnaires to tease out views about the questionnaire as a whole and about

particular questions. The question format seemed to have been understood and

teachers were able to respond using the rating systems and Likert scales. The

comments back from teachers suggested that the questionnaire was too long and too

ambitious in the areas it tried to cover.

Some of the questions did not discriminate between different pupils and were not

sensitive enough to the demand of categorising children into the different groupings

needed for the study. As such they provided no useful assessment information and

they lacked discriminant validity; these questions were removed.

An initial comparison between the types of difficulties identified on the teacher

questions and cluster scores on the British Ability Scales (BAS-II) seemed to suggest

possible links between the verbal categories and visual-spatial categories. This

suggested some external validity in the construction of the questionnaire on these two

factors. No links seemed evident between the Non-Verbal cluster and categories on

the questionnaire (nor had it been intended that the questionnaire would identify

difficulties with integrating information). Further background reading had taken place

and the research questions were starting to focus on differences between two major

types of processing – verbal and visual. This allowed a massive reduction in the size

of the questionnaire by concentrating on verbal processing and visuo-spatial

processing. Version Two of the questionnaire was constructed by removing those

areas not relevant to the project and removing those questions that did not

discriminate.

Development of the teacher questionnaire proceeded development of the other

measures used in this study. The pattern of development was useful in that it allowed

an individual measure to be evaluated before adding the complexity of the other

measures. This strategy was repeated for other measures as they were developed.

The development of the q-sort started from observations of children who were

previously referred to me and undergoing Statutory Assessment. As part of the build

up of rapport with pupils I had tended to ask general questions about the kinds of

things that they liked doing in their spare time. Some children seemed to have a

preference for visuo-spatial activities and others for verbal orientated activities. This

led to brainstorming the types of activities that 8 to 11 year olds might like to do.

In order to reduce researcher bias and increase face validity, these items were

presented to 7 practising educational psychologists who were asked to identify each

activity as being predominantly verbal or visuo-spatial (see Table 6.4). The items with

the greatest degree of agreement were retained as having the greatest face validity and

content validity.

The measuring system designed for use with the q-sort required allocating a value to

each item selected on each level of the sort. The face validity of this measure was

trialled by asking a colleague to try out the scale and reflect on her score (see 6.4.4).

The q-sort was then piloted with 6 pupils undertaking Statutory Assessment to see if

they understood what was expected of them. This allowed the final set of instructions

to be devised and pictures were added to the cards to aid recall of the spoken text

printed on each card. This was necessary because pupils could not always read the

activity – they were being referred for assessment because they had literacy

difficulties.

Pupils seemed to be able to choose the activities that they wanted to do or did not like

(this is discussed in the results section see 7.3). However, the source of activities was

not the children themselves and this might have introduced some biases. For example,

older children may not have wanted to be seen to enjoy playing in sand. A better way

of going about the selection of activities might have been to start with asking a sample

of pupils aged between 8 and 11 years to list pastimes that they enjoyed and pastimes

that they did not enjoy. This could have been done with individuals or in small focus

groups. The activities would still have to be classified as belonging to visual or verbal

processing types.

The development of the learning experiment is discussed in detail in the methodology

(see 6.5). Several changes were made during the development to improve the

measurement made. The final decision was to measure learning by comparing a

baseline measure of how many letters were in the correct position when spelling the

target word with a post 5-trial exposure count of number of letters in the correct

position. Reliability was improved by using a counter-balanced repeated measures

design. This has the advantage of removing individual confounding variables through

having the same pupil participate under both conditions of the experiment. Order

effects are removed by presenting one type of learning first to half of the participants

while the other half receive the reverse order. Material differences are controlled by

having two matched lists and using List A for half of each learning type and List B for

the other half of each type (see 6.5.2 for the four counterbalanced conditions).

Once all of the measures had been piloted individually, a smaller group of 4 pupils

was selected to pilot the measures together. This was to allow timing, presentation of

the different strands and collection of different pieces of information to be co-

ordinated so that a final protocol could be established for the main part of the study.

The success in matching pupils according to their identified cognitive preference on

the BAS-II is shown in Table 7.9 with the mean age of both groups categorised by

learning outcome being found to be just over 1 month’s difference.

Inter-measure reliability of the different assessment techniques is discussed in the

results (see 7.2)

8.3.2 Sample recruited

Three samples of pupils were selected for this study. The first consisted of an archival

data search of 99 pupils from assessments carried out by the author to explore the

distribution of cognitive preferences. All of these children had identified special

educational needs, though not all had spelling difficulties. The data collected

represented referrals to the County Psychological Service over a period of 4 years and

for whom a full BAS-II assessment had been undertaken. There were many other

pupils referred with difficulties for which this sort of assessment was not carried out.

This leads to a question of how representative is this sample of pupils with spelling

difficulties and of pupils more generally.

In Chapter 4 data showing the distribution of scores from other populations was

presented. The DAS standardisation (Table 4.6) shows that in the normal population,

a spatial preference is found slightly more often as a verbal preference (1.3:1) when

reading is not discrepant from general ability. When reading is discrepant the ratio

changes drastically to almost 4:1. The sample referred to the author produced ratios of

1:1 and 6.5:1 (Table 4.8). This suggests that more pupils with relatively weak verbal

skills are referred for assessment than occur in the normal population. Comparisons

with other special educational needs populations (Table 4.6) produced the following

ratios for reading discrepant pupils (Table 8.1):

Study Spatial:Verbal preferences

Turner (2000) DAS Dyslexic sample 2.5:1

Dumount (1996) Learning disabled 1.2:1

Turner (2000) BAS-II Dyslexic sample 2:1

Table 8.1 Ratio of cognitive preference found in other studies

This suggests that the LEA sample is very different from these other special needs

samples and seems to consist of more pupils who have a verbal weakness relative to

spatial difficulties. There is a second important difference in the archival sample

compared with either the DAS standardisation sample or the special needs samples in

other studies. There are more pupils with flat profiles – ‘common garden’ poor

readers and spellers who have general learning difficulties.

The second sample consisted of 17 pupils who had identified special educational

needs and a weakness in spelling and either a verbal preference or a visual preference

in their profile. At the time of designing the investigations it had been hoped to use a

larger sample of pupils for the learning experiment (24 instead of 16). However, a

number of changes occurred at the same time that influenced the amount of children

suitable for the study:

• The DECP Working party report on the role of educational psychologists

(BPS, undated) started to influence discussion within the County

Psychological Service about EP role. This was extended as the LEA moved

towards an Ofsted inspection with requests made by Service Managers for

examples of how schools were being supported.

• The crisis in recruitment of educational psychologists occurring in most LEAs

led to wider thinking about EP role. This was extended to school level with the

publication of the Service Level Agreement outlining some of the things that

EPs could do (other than provide evidence for Statutory Assessment)

• The County introduced funding for children with Special Educational Needs

that did not require a Statement of SEN and was not contingent on the child

being seen by an EP. This reduced the requests from schools for pupils with

literacy difficulties to be assessed by an EP. There was a move by schools

towards using EP expertise for helping teachers and teacher assistants support

pupils more effectively that already had financial support.

• The pupils who continued to be referred for literacy difficulties tended to be

more complex. They were either demonstrating emotional difficulties as well

as difficulty acquiring basic literacy skills or, they were more of the ‘common

garden type’ having more extensive general learning difficulties. There

seemed to be fewer pupils referred who had a verbal weakness. This could

possibly because the National Literacy Strategy and Additional Literacy

Support focus on the development of phonological skills or it was easier for

schools to find sufficient evidence (e.g. a report from a Speech and Language

Therapist) to obtain additional funding.

The overall effect was to change schools’ expectations about what I could offer to

support the school and reduced requests for assessment of children’s strengths and

weaknesses using psychometric assessment. In order to obtain sufficient pupils that

matched the criteria for the study it was necessary to:

• ask colleagues to identify possible candidates aged between 8:0 and 11:0 who

had been assessed recently using the 6 core BAS-II subtests

• the sample was then reduced by identifying those that had a relative difference

between verbal and spatial skills

• parents were contacted to seek permission to work with their children to carry

out additional assessment of cognitive preference

• for those parents who replied, schools were then contacted to seek their

co-operation in the study

• arrangements were then made to carry out the learning experiment and q-sort

and to ask class teachers to complete the questionnaire

The net effect was a sample that was a compromise involving a smaller number of

participants than originally intended, but available within the time constraints of the

study.

The second sample remained an opportunistic sample of pupils with literacy

difficulties who were undergoing assessment and who matched the initial criteria and

who were available. This group of pupils was matched for age but not for gender.

Boys outnumbered girls, with only one girl being included in the sample. Gender was

not considered to be an important issue in the design of the study. While it is

recognised that boys tend to outnumber girls by 3:1 in referrals to the author and it

could be speculated that this might be due to how girls process language more

effectively than boys, the study required selection on the basis of how verbal skills

compared to spatial skills. Assumptions about generalisations related to gender were

ignored by making a direct assessment of the relevant factor contributing to

information processing at the cognitive level. This study is not about girls or about

boys it is about pupils with a particular pattern of cognitive profile. It could be argued

however, that the results of the study are limited in this respect since gender could

remain important because social expectations or interactions might influence

performance. This issue has not been explored in the current study.

The third sample was also an opportunistic sample using archive samples of

handwriting for seven pupils of known cognitive profile. This group had many

similarities with the second sample (similar age range, all had spelling difficulties, all

had either a verbal or spatial cognitive preference).

The nature of the samples is such that the results obtained are really only applicable to

children (mainly boys) aged between 8:0 and 11:0 years who have been identified as

having special educational needs with a difficulty in spelling and have either a verbal

preference or a spatial preference.

8.3.3 Laboratory type learning experiment versus longer term teaching

Traditionally, psychological implications are tested out over time. In schools, this is

done through the setting up of a remedial programme which targets learning of

specific, measurable, attainable goals through resourced and timed intervention (the

individual education programme). When this is well carried out, useful information is

provided from monitoring records that are kept about the relative effectiveness of the

learning programme and teaching strategies used (e.g. Brooks and Weeks, 1999).

However an IEP was not considered to be a suitable mechanism for investigating the

link between cognitive preferences identified using the BAS-II and learning

effectiveness because:

• There are difficulties controlling irrelevant variables:

o how often the IEP is carried out

o how well distractions to learning are reduced

o differences and beliefs in the person carrying out the intervention

o the extent to which the intervention could be undertaken without

contamination of other teaching methodologies

• IEPs tend to be more complex than focusing on a small number of cognitive

processes – outcomes tend to be in terms of reading ages or number of words

recognised or improvement in performance comparable to peers.

• When resources are linked to failure to make progress, there may be agendas

at play that are not consistent with motivating adults to help a child improve in

a skill area. This would reduce the reliability of school-based data.

The learning experiment was chosen to allow:

• two methods of cueing perceptual systems and subsequent cognitive

processing to be carried out by the same child using a repeated measures

counter-balanced presentation to reduce order effects

• baseline measures for the target material to be established

• definition of fine grain measures (letters in the correct position) to be used

• a fixed number of learning trials and subsequent performance levels to be

compared

There are a number of weaknesses of the learning experiment:

• It is conducted by someone unfamiliar to the child and this may mean that the

child is anxious about their performance or about working with the unknown

adult.

• The learning experiment is only conducted on a single occasion and this does

not allow for the possibility that the child might be having an ‘off-day’. There

is an assumption that factors that impede performance act equally over both

visual processing and verbal processing. However, this may not be the case.

• The extent to which learning is retained over time is not measured, nor is the

possibility of generalisation of learning to the classroom. However, this also

means that the possibility of confounding variables such as the class teacher

selecting one of the target words for learning in a weekly spelling test is

reduced.

8.3.4 Cued learning and recall and actual processing

The learning experiment involves cueing attention through the use of perceptual cues

(colour or sound) in the belief that this would lead to particular cognitive pathways

being activated. However, we do not really know what children do with information

inside their head. Just because the child has been asked them to attend to the visual

aspect of the stimulus materials it does not mean that they have not sounded out the

letter using synthetic phonics. Sub-vocalising the sounds of letters or concentrating on

the overall visual shape of the word or colour of the letter can nullify the expected

effect of the cued recall. This can mean that although the child was expected to learn

using one particular strategy, they have used another strategy. This has been noted in

other studies (e.g. see Springer and Deutsch, 1993; mentioned in Chapter 2.3.1)

Previous teaching may play an important role, particularly if the child has experienced

remedial teaching in small groups or one to one and there has been a heavy emphasis

in teaching by using a phonological approach. The child may be used to thinking

about spellings in this way when working with his teacher. This might mean that the

child suspends preferential cognitive processing in order to select socially expected

learning. The social dimension and past experience could both contribute to future

performance.

Equally, the child could just have felt like using a particular method for directing

attention when learning or recalling. This cannot be controlled for easily. It could be

that flooding of that modality with meaningless information would prevent that type

of processing (e.g. previous experimenters have tried to get participants to flood the

phonological loop with repeated words to prevent sub-vocalisation or repetition of

verbal information). There is evidence from previous research that suppression of the

phonological loop does not impair performance in competent readers suggesting that

decoding of text can take place in a purely visual way (Baddeley, 1979).

8.3.5 Different types of measures used

Cognitive preference is being measured in different ways depending on the nature of

the observable behaviour being detected. The BAS-II is measuring higher order

reasoning skills but the other measures used in this study focus on low-level

perceptual skills or on observable behaviours or self-reports of behaviours. This was

summarised in Chapter 6.6 and is expanded on here to consider the type and level of

measurement used.

The BAS-II profile does not identify strengths or deficits per se, rather it allows a

comparison of processing skills for a particular individual with the standardisation

sample. This produces a pattern of strengths and weaknesses relative to the

standardisation sample. The categorisation used in this study involves looking at how

the individual fares in each area of reasoning and then allocating to one of two

conditions. A verbal preference was defined in Chapter 6.2 as resulting from either a

verbal reasoning standard score significantly higher than GCA or a spatial reasoning

score significantly lower than GCA. A visual-spatial preference was identified by the

reverse pattern of scores.

Scores on the learning experiment and teacher questionnaire part B are more ipsative

and do not ask for comparisons. Part C of the teacher questionnaire did ask for

comparison of pupil performance on identified literacy skills with other pupils in the

class. The q-sort produces a different type of measure again – asking the child to

select different activities and producing a tension between those liked and those not

liked. The handwriting samples were used to produce a count for different categories

of spelling error. The different measures are summarised in Figure 8.1.

Figure 8.1: Different types of measures used

Q sort Tension between types of preferred activities

BAS-II Relative pattern of strengths and weaknesses when compared to a standardisation sample to produce type of preferred processing style

Reasoning tasks

Compared to Reference population

Teacher questionnaire Observations of types of reading and spelling errors by task

(Section B) and by comparison to class (Section C)

Literacy tasks (B)

Compared to Class (C)

Learning experiment Performance on matched tasks to what type of perceptual cueing is

best for learning.

Handwriting Samples Analysis of spelling errors to identify visual and verbal type errors

and quantify the cognitive processing error.

The different types of measures allow the identification of cognitive preference

categories (types of pupils). The level of measurement is nominal and prevents

corelational analysis. The measures are satisfactory for giving an indication of

possible underlying neurological functioning with the assumption that all of the

measures tap the same processing.

Consideration of other measures included:

• Using BAS-II ability scores to give an indication of performance on each task

rather than comparatively to the age-appropriate reference group used in

standardisation. This relies on using the design of the BAS-II and its reliance

on the Rasch model to determine how hard items are on each scale used. Mean

values could be produced for verbal reasoning and for spatial reasoning. This

was decided against because none of the previous studies have used this

method of scoring. All have used scaled scores or standardised scores for

identifying cognitive profiles and cognitive preference (Rourke, 1998;

McIntosh and Gridley, 1993; Shapiro et al, 1995; Kercher and Sandoval, 1991;

Holland and McDermott, 1996; Elliot, 2001; Kurie, 2000; Dumount et al,

1996; Gridley and Roid, 1998).

• Two separate scores could be derived for the q-sort by simply multiplying

distance from the ‘no preference’ point by the number of items chosen on each

level. This would give two scores that could be compared directly to gains in

learning on the verbal cued task and visual cued task. This would allow

exploration of whether or not a stronger activity preference was correlated to a

stronger learning effect in each modality. This was not used because the q-sort

is not an open-ended rating system – if one item is selected as a preference

then another item must be rejected. At the pilot study level, children seemed to

be able to cope with this decision. It may have been more difficult than

anticipated and this was evident with one child who did not want to reject any

items after the first round and had to be persuaded to make a choice between

the remaining items. An alternative to the q-sort would have been to use an

open-ended rating system with a ‘mark out of 10’ given to each item.

The handwriting samples allow an exploration of the extent of cognitive processing

style that has a high ecological validity. It gives an insight into what children with

particular cognitive strengths and weaknesses are able to do in the classroom. This

suggests a possibility for using handwriting samples as a mechanism by which

classroom performance can help identify cognitive processing style and in turn

identify what type of remediation might be beneficial. Further research is necessary to

see whether particular samples of handwriting and spelling errors are predictive of

BAS-II profile or (more importantly) learning style. If so, then this would mean that

samples of independent class work could be used to identify the particular spellings

that children have difficulty with (to help with spelling dictionaries, spelling lists etc)

and the predominant type of error (visual versus verbal). Both pieces of information

could then be used to personalise remedial teaching by content and by method.

It could be argued that the method chosen for scoring performance on the learning

experiment could have focussed on phonemes rather than individual letters. This was

not selected because it was felt that this might lead to a bias towards phonological

processing – the wrong orthographic representation of the spelling could still be

counted as correct if an alternative spelling of the phoneme was present. The phoneme

is a word chunk larger than the letter (in sound), thinking of the word in this way may

reduce demands on working memory (allocation of attentional resources and

phonological loop capacity). The same phoneme can be represented in different ways

(see Spencer, 1999 for examples of how the 44 phonemes can be represented). A

similar kind of chunking can be done using visual units (words within words,

morphemes, familiar letter combinations) and this would also reduce demands on

working memory (allocation of attentional resources and visuo-spatial scratchpad

limitations). The compromise position that did not seem to benefit either chunking

strategy was to keep measurement to the letter level. Emphasis on the letter as the

perceptual element was maintained by asking pupils to attend to the sound or to the

colour of the letter.

8.3.6 Modality based assessment

This study involves measurement of the relative performance of two modalities –

visual processing and verbal processing. Connor (1994) makes three criticisms of

modality based assessment and intervention:

• “claims that particular patterns can be identified may be based on

unsystematic observations

• more controlled studies have produced either no subtype patterns or patterns

quite different from the auditory or visual categories hypothesised

• the question of whether a visual problem (for example) is nothing to do with

visual perceptual processing, but is actually a result of an ability to attach

verbal labels to visual stimuli”

The current study addresses each of these three criticisms. Firstly, this study suggests

that the BAS-II can be used to systematically make observations about the relevant

strengths of visual processing and auditory processing that impact directly on both

assessment (having ecological validity with observations of spelling samples, see

Appendix 13) and intervention (performance on the learning experiment, see Table

7.8 and Chapter 7.5). Secondly, definitive cognitive profiles have been identified and

the distribution of pupils across cognitive profiles suggests that other dyslexic

subtypes are possible in addition to verbal and visual types (see Table 4.8 and 4.9).

Thirdly, there seems to be a predictive link between cognitive preferences identified

on the BAS-II and performance on the learning experiment. The nature of the learning

experiment reduced visual cueing to noticing and responding to colour patterns that

did not require the attachment of verbal labels (see Table 7.8). This was a sufficient

perceptual cue to engage attentional resources in noticing grapheme positions.

8.4 Implications for assessment

There are many ways that assessment of spelling can be approached in order to inform

remediation. Measures include:

• Spelling ages - the child’s performance is matched to an average child in the

standardisation sample of the chosen test. Usually this involves some spellings

that are graded e.g. in terms of frequency (and familiarity of the word) or

complexity (regular phonological to complex morphological or orthographic).

While this gives an idea of how well the child is doing in broad terms

compared to peers it does not provide diagnostic information about what to

teach next.

• Curriculum based – the child’s performance is compared to target performance

on the curriculum being presented. This can take several forms:

o National Curriculum SAT levels – but spelling is incorporated into an

assessment of several skills used in writing. It is too broad to be of use

in deciding what to do next in teaching a child.

o National Literacy word lists can be used as a guide to what the child

should know at particular stages of schooling. Probes can be devised to

test which parts of the target list have been acquired and progress can

be expressed as a raw score. Teaching is focussed on the curriculum

rather than on what the child needs to know for everyday performance.

There is an inherent assumption that when the lists were compiled they

were produced on the basis of what most children need to know at each

key stage.

o Regular spelling tests compiled by the teacher and given to the class.

Raw scores indicate how well the child has learnt the rules or words

being practised and how this compares to the rest of the class. This can

be diagnostic and guide the teacher at a class level, deciding what to do

next or whether a particular rule or set of words needs to be revisited.

o Classroom performance can be assessed by looking at the child’s work

and noting spelling errors. This can be diagnostic and inform

remediation. The information gleamed can then be used as a basis for

teaching what the child needs to know in order to improve accuracy.

These measures are really focussed on the ‘what’ question of teaching – what does the

child need to learn next. They do not deal with the ‘how’ question – how can the child

learn best? Or, how can the teacher structure learning to support the child?

This study has focussed on the ‘how’ questions. Assessment was considered in the

following ways:

• Trying to use a teacher questionnaire to make use of teacher observations of

the child in the classroom. The teacher within-child ratings had the weakest

agreement with any of the other measures. When teachers were asked to

compare the target child to others in the class, the categorisation produced was

more successful and correctly identified the type of learning cue that would

lead to the best performance for 56% of pupils. It also produced agreement

with the preferences identified on the BAS-II for 63% of pupils. This suggests

that a shortened form of the questionnaire could be developed from the

Teacher Related part of the existing questionnaire. Further exploration is

needed, particularly in exploring the influence of the phonological deficit

hypothesis on teachers’ thinking about children with spelling difficulties. This

could be approached through the training of teachers in how to use the

questionnaire.

• Trying to use the q-sort to make use of what the children knew about

themselves in order to provide an indication of how they processed

information the best. There was some agreement with other measures, mostly

with teacher perceptions of the child.

• Trying to use the BAS-II to identify particular strengths and weaknesses. This

seemed to be successful for the majority of pupils assessed and linked with

learning success in spelling under the controlled conditions of the learning

experiment. The strengths and weakness investigated were in the relative

performance of verbal reasoning (as an indication of verbal processing) and

spatial reasoning (as an indication of visual-spatial processing). This is not

quite the same as dyseidetic and dysphonetic types identified by Boder and

Jarrico (1982). Their classification considered weaknesses in the two systems

based on observations of literacy skill (hence the ‘dys’) whereas the current

study simply looks for one cognitive system performing better than the other

system.

• Using the learning experiment itself to see which way children seemed to learn

best produced noticeable differences in the short-term. This is consistent with

other studies where visual spatial readers improve reading accuracy best when

material was designed to activate the right hemisphere (Robertson, 2000).

What is needed now is further research to see whether the short-term

difference in performance translates into longer-term improvement through

remedial teaching carried out over time. This is difficult given the nature of

IEPs and how IEPs are implemented in schools. Brooks and Weeks (1999)

have conducted such studies into individualised learning and found them to be

effective in their one-year trial. The Brooks and Weeks study involved

allowing teachers and children to choose a learning method from 10 possible

strategies. How the teacher made a choice about which strategy to use was

unclear but included:

o Consideration about the effectiveness of different strategies using

research information

o ‘other research and views’

o ‘child’s previous history and responses’

o teacher’s favoured method

o child’s preferred method – derived by teaching 3 or more different

strategies, each strategy for 1 week and choosing the most effective for

the child

Brooks and Weeks found that children aged 6-8 years, tend to learn best when

they use their preferred learning strategy. This suggests that the learning

experiment used in the current study could be used to identify which of the two

modalities support learning of spellings best for a particular child and this could

then be used to fine-tune and select appropriate learning approaches from those

used by Brooks and Weeks:

o Simultaneous Oral Spelling the letters are sounded out as they are

written (verbal approach)

o Own Voice recorded the word and matched to spelling e.g. page …p-a-

g-e (verbal approach)

o Onset and Rime (verbal or spatial depending on presentation of sub

units)

o Phonics the word is spelt out in plastic letters and the child says the

letters in sequence and then says the whole word (verbal)

o Neurolinguistic programming (visual – visualisation technique)

o Mnemonics (can be verbal or visual depending on type used, in Brooks

and Weeks’ study the method chosen was to form a verbal mnemonic)

o Picture association (visual) For each word that is wrong the child is

encouraged to think of a picture that best shows the word.

o Look Learn Cover Write and Check (LLCWC) The child looks at the

word for 10 seconds, covers the word, writes it and then checks for any

mistakes. (Visual)

o Tracing (kinaesthetic) The word is written on sand paper to provide a

high resistance when the child traces over the word in order to develop

muscle memory (a kinaesthetic anchor)

o Look and Say, the child looks at the word for 10 seconds and then says

the whole word (visual)

• The classification of spelling errors in naturalistic handwriting samples seems

to match the types of cognitive preferences identified using the BAS-II. The

BAS-II has been found to link to performance on the learning experiment.

This leads to the hypothesis that careful analysis of spelling errors in a

diagnostic way can lead to identification of cognitive preference and in turn

suggest how remediation can best be approached. This hypothesis remains

untested and further research is needed to explore this possibility. However, it

is consistent with the study by Curtin, Manis and Seidenberg (2001) that has

shown that spelling errors can be used to classify children with developmental

dyslexia into phonological dyslexics and surface dyslexics. It is also consistent

with Boder and Jarico’s use of literacy errors to classify dyslexics as

dysphonetic and dyseidetic. This suggests a further methodology that teachers

could be trained to use and would make use of classroom based writing and

curriculum based assessment.

This leads me to consider that assessment can be approached in a systematic way that

can guide remediation (see Figure 8.2).

Figure 8.2: Systematic approach to assessment of spelling

(NLS = National Literacy Strategy, BAS-II = British Ability Scales, IEP = Individual Education Plan)

8.5 Implications for teaching

One critique of this study is that the learning experiment is simplistic and does not

represent real teaching nor does it say anything about whole class teaching. This study

has been concerned with a particular group of pupils, namely those that have

identified special educational needs, a difficulty in spelling and have a cognitive

preference. It has involved mainly boys aged between 8 years and 11 years with

literacy difficulties and specific identifiable cognitive profiles on the BAS-II. This

limits the extent to which the findings can be generalised to normal classroom

teaching. However there are some considerations arising, particularly for children

matching the sample profile who participated in the study and these will be discussed

in this section.

What should the child be taught during remediation? (Targets for the IEP)

Curriculum Based Measures

NLS list performance

Class spelling tests and spelling probes

Free writing spelling errors to give words and types of errors

How should the child be taught during remediation? (Approaches for the IEP)

Identification of preferred style

BAS-II

Learning experiment

The archival study of 99 pupils suggests that not all pupils have the same cognitive

profile and this is consistent with the standardization sample of the DAS representing

a more general normal population of pupils. This suggests that not all pupils have the

same cognitive preference. The learning experiment suggests that for some pupils

their cognitive preference contributes to their learning style. The learning experiment

has a focus on perceptual cueing of cognitive processes and shows how cognitive

preference can influence learning and this could be taken into account at the word

level of remedial literacy work. The results of this exploratory study suggest that low

level perceptual cueing that is linked to the child’s processing preference can lead to

more successful learning in the short-term. This suggests that some children might

benefit from different approaches when learning to spell and supports the findings of a

previous study into individualised learning (Brooks and Weeks, 1999).

The analysis of spelling errors suggests that cognitive preference influences the kinds

of strategies that children use in classroom based handwriting. This supports the

hypothesis that cognitive preference is due to an underlying neurological difference

that is pervasive and could be noticed by teachers if they were suitably trained.

Cognitive preference could be due to a state of readiness in neural pathways so that

the pupil is likely to process the information more readily using the pathway that is

preferred. Alternatively, cognitive preference might result from a weakness in one

part of the system. It could be that some parts of the least preferred route is damaged

or not formed correctly – matching observations made by neurologists. However, in

this study not all children learnt best when presented with cues that matched their

cognitive preference. This suggests that the learning of spelling is more complex (and

summarised in Figure 8.3). Observations made of the children and their approaches to

the learning task that introduced confounding variables to the study suggest that

teachers need to be aware of:

• Effort and involvement in the task is indicative of the allocation of attentional

resources to different types of processing. For example, CM allocated more

attentional capacity to decoding sounds that he did to counting letters of each

colour, this led to better performance on the verbal cued learning task than the

visual cued learning task. AW showed a marked dip in performance when the

task became ‘a bit boring’. JM had to allocate more resources to identifying

the colour and matching to the cue card then saying the letter sound and

performed better on the visual cued learning.

• Previous exposure to failure led to reduced engagement in the learning task

and resistance to the task with motivation directed towards task-avoidance.

This was most evident with DS and AK. In each case there was a need to

re-focus and re-direct attention. In both cases, improvement was most

noticeable when the learning cue matched their cognitive preference.

• Successful previous teaching was evident in the strategies that children

adopted without prompting. This suggests that ‘social priming’ leads to

activation of one type of cognitive processing in preference to another. In the

case of RH, this was outweighed by cognitive preference in matching

performance on the learning task.

Figure 8.3: Factors influencing selection of cognitive processing

Rack (1994) argues that remedial programmes should target weaknesses and seek

ways around severe difficulties which may be resistant to teaching. The current study

suggests that things are not quite so clear cut. It seems better to try to activate

cognitive processes that the child prefers using because it leads to better learning

performance in the short term. The observation of the children used in the study

suggests that this is less likely to cause emotional reaction from pupils and can also

increase motivation by enlisting processes that come more easily to the child.

Verbal-phonological Processing or Visual-spatial Processing is used for learning or completing the task

Reading and spelling task

Motivation and emotional responses enable pupil to reject some strategies and select compensatory strategies

Previous teaching of strategies increases selection of one cognitive route by social priming

Relative activation levels and neurological thresholds make cognitive processing by one route more likely than the other route.

A state of readiness exists in one perceptual route to enhance cognitive processing. Or damage and malformation in parts of one route reduces cognitive processing.

Detected by

BAS-II ?

Rack might be right however in the long-term if the cognitive weakness is due to low

activation levels or low connectivity in particular neural pathways responsible for

specific cognitive processes. The study of musicians described by Schlaug et al

(1995b) suggests that training and use of neural pathways leads to further

physiological development of those pathways. This suggests that teaching to

weaknesses might lead to a strengthening of particular neural pathways and lead to a

reduction in activation levels so that information can be processed more readily.

This has not been investigated in the current study. The argument would be that if

strengthening of particular pathways responsible for gross processing rather than fine

processing is required (i.e. visual processing rather than letter shape processing) then

the teacher and child might be encouraged to engage in non-reading activities to

support processing skill development. For example, in order to develop visual-spatial

skills the child might spend time doing mazes or completing dot-to-dots or looking for

embedded figures in figure-ground pictures. This was the approach that followed the

Illinois Test of Psycholinguistic Abilities (e.g. see Kirk and Kirk, 1971). Currently

this type of intervention tends not to feature on IEPs. Contemporary educational

practice places the intervention more tightly to the presenting problem. The child

cannot read well or spell well so teaching is focussed at this level. The study could be

extended to try teaching children with their less preferred modality cueing attention to

see if improvement occurs over time by using verbal mediation to emphasise the

features not readily processed by the child.

The analysis of spelling errors made during observations of children engaged in the

learning experiment and for the further sample of handwriting suggests that for the

types of children who participated there are more than phonological processing

abilities to consider. This suggests that the methodology for analysing spelling errors

could be used to increase teacher awareness and help them in making an assessment

of the child’s cognitive preference through classroom based assessment. This could

include using the tables in Appendix 13 as a template for recording the types of errors

made.

This is a small scale exploratory study and the results suggest that the generalised

developmental model outlined in Chapter 3 (Figure 3.2) could be extended by adding

visual type error analysis to complement Treiman’s phonological analysis of spelling.

This would change the model slightly to take into account errors not fully explained

by Treiman’s descriptions (Figure 8.4).

Figure 8.4: Extending the developmental model to include both phonological and visual modalities

Logographic

Syllabic

Phonemic

Orthographic

Morphemic

Random

Whole Word

Letter Sequence

The role of the visual gestalt has been added to show how errors can occur. The child

can learn to spell whole words by sight memory but confuse them with similar

looking words (particularly were letter reversals or closure are involved). A finer level

of visual discrimination can be used and this is when letter gestalt errors occur (for

instance closure of ‘i’ to be perceived as ‘l’). Cossu, Gugliotta and Marshall (1996)

demonstrated that young children make more matching errors involving letter

sequence reversals than older children. Further research is needed to establish whether

older children are more likely to make errors in selecting letters when spelling rather

than similar looking words or part words compared to younger children. It could be

that a single box would be better in the model to represent whole word spelling that

incorporates both aspects of getting visual chunks correct and letter sequences and

selection correct.

When thinking about remedial teaching with children aged between 8 and 11 years of

age who have spelling difficulties, this study suggests that:

• About 1 in 5 children with special educational needs have a cognitive

preference for learning using verbal skills or visual skills. The majority of

children referred to the author with learning difficulties have a flat profile.

Where a cognitive preference exists, more children have a weakness in verbal

reasoning than spatial reasoning.

• Some children learn better when learning to spell using the sensory modality

that is linked to their cognitive preference.

• Diagnostic analysis of spelling errors made in independent writing seems to be

linked to cognitive preference with pupils making more errors of the type that

matches their relative weakness.

• Other factors have been identified (as confounding variables in this study) that

interfere with this straightforward link (social priming, motivational factors,

compensatory strategies, attentional capacity)

• Developmental models of spelling suggests that learning to spell should follow

a particular pattern. However, the current study suggests that this may not

wholly linked to phonological development and a parallel visual spatial

development is suggested to complement Trieman’s model.

8.6 Conclusions and implications for further research

Learning to read and spell in English is a complex process involving many factors.

Two factors have been explored in this study but other contributing factors have been

identified, both in the background research and as confounding variables in the study

undertaken. This has been a small-scale exploratory study to investigate the relative

merits of considering cognitive preference in order to support pupils who are having

difficulty with spelling.

A number of hypotheses have been produced that emphasise particular aspects of

dyslexia. The current study seems to support:

• Phonological deficit hypothesis that suggests a difficulty with decoding sound

or spoken language inhibits development of normal spelling or reading. Some

pupils have been identified with a relative weakness in verbal reasoning and

this has matched with lower performance on learning to spell when cueing has

been reliant on phonological features rather than visual features. Where a

cognitive preference exists it is more likely to be indicative of a verbal

processing weakness than a spatial weakness (approximately 6.5:1 in the

current study).

• Visual processing deficit hypothesis that suggests a difficulty with decoding

visual information inhibits development of normal reading or spelling. Some

pupils have been identified with a relative weakness in spatial reasoning and

this has been matched with lower performance on learning to spell when

cueing has been reliant on visual features rather than phonological features.

This study has not explored other hypotheses directly:

• Transient magnocellular deficit hypothesis – difficulty with responding to

rapid changes of sound or visual information interferes with normal reading

and spelling. It is possible that transient magnocellular difficulties have

contributed to either a lowering of the performance on the verbal-cued

learning or the spatially-cued learning.

• Developmental delay hypothesis – there is a general immaturity in

neurological development and this means that reading and spelling develop

along normal lines but much more slowly. This model has been extended to

include slower development of either visual routes or verbal routes.

• Central executive control dysfunction hypothesis – integration of information

from the visual and verbal pathways is incomplete and this inhibits reading

and spelling development. This has not been investigated directly – but pupils

with a low non-verbal reasoning score were identified in the samples of pupils

referred (see Table 4.8 and Table 4.9 and Figures 4.2 to 4.5).

• Automaticity impairment hypothesis – fluency in decoding textual information

is impaired, making reading and spelling more arduous.

A number of confounding variables have been identified as providing possible

explanations for unexpected learning difficulties (e.g. social priming, motivation).

These have not been explored in this study.

The following questions have been raised as possibilities for further research:

• The teacher questionnaire could be developed further, using the Teacher

Related part of the questionnaire and providing teachers with some training to

support their observations and responses. Would this lead to greater agreement

between the British Ability Scales (BAS-II) profile and the teacher profile?

Could this lead to a diagnostic checklist to inform teachers about how best to

emphasise spelling patterns when teaching a particular child (for example by

choosing a suitable strategy from the list produced by Brookes and Weeks,

1999)?

• Can simple diagnostic error analyses protocols help identify children with

different types of processing difficulty (such as the spelling analysis used in

this study)? Can these be used to identify cognitive preference when used

more widely by teachers who have been trained on its use? Can the protocols

be used to identify the strategies most likely to lead to successful learning on

remedial programmes?

• Short-term gains have been suggested with the learning experiment but how

do these translate when remediation is carried out using a particular modality

for longer periods? Would observed differences still be noticeable after 3

months? 6 months? A year? How might this be included in individual

education plans?

• Does more elaborate cueing of the perceptual system lead to greater rates of

learning e.g. does having more colours or colour positions that change from

trial to trial improve visual cued recall more than 3-colour constant position?

• Does modality based chunking help increase learning outcomes for children

with spelling difficulties e.g. visual chunking of letters into morphemes;

auditory chunking of letter sounds into phonemes?

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Appendix 1: Pilot Teacher Questionnaire

Cognitive Profile Questionnaire

Child’s name Teacher

A. Background 1. How many schools has the child attended (including this one)?

2. Does the child attend school regularly? If not how frequently are they absent?

3. Is there an identified history of visual difficulty? If so, please give details.

4. Is there an identified history of language difficulty or delay? If so, please give details.

5. What would you say the child’s main strengths are?

6. How would you describe the main problem? What things would you say the child has major difficulties with?

7. What would you say that the child does well? In what ways?

8. In what ways have they made progress since joining your class?

9. In what ways have difficulties become more noticeable?

B. Please complete this section on National Curriculum

attainment

National Curriculum area

Current Teacher Assessment of

attainment

Previous SAT scores and date of

SATs

Comments (if any)

English Speaking and Listening Reading Writing Maths Using and Applying Number Shape, Space and Measures Science

C. For the next statements please rate how well each one applies to the child

Statement No Probably

No Possibly

Yes Yes

Do you think that the child’s literacy skills are developed to the same level as their general ability?

Is the child able to reason and discuss ahead of their ability to record information or to read?

Can the child bring relevant information into discussions?

If it weren’t for the child’s level of literacy skill, would you think that they could cope with a higher level of work generally?

With the exception of literacy, does the child learn most things quickly?

Can the child complete tasks if given more time? Does the child remember better if instructions are written?

Can the child spell some words well but nor others?

Does the child lack strategies for approaching unfamiliar words in reading and spelling?

Is the child good at discriminating between right and left?

Is the child good at matching words that have similar features or look similar?

Can the child copy from a model e.g. use underwriting, copy from a word frame?

Is the child good at games that require remembering words or phrases?

Is the child good at games that require remembering objects or shapes?

Can the child recite the alphabet? Does the child know the sequence of days in a week?

Does the child take part in group discussions? Can the child recite the names of months in a year?Do the child’s spelling errors make sense if sounded out (are they phonetically plausible)?

Does the child have more difficulty with polysyllabic words in speech compared with monosyllabic words (e.g. repeating words)

Does the child have greater difficulty learning new polysyllabic words than shorter words

Is the child good at finding rhymes for words? Is the child good at saying words that begin with the same sounds (alliteration)?

D. For the next statements please rate how well the child compares with others in the class.

Low/Less than most

Average High/More than most Statement or question

1 2 3 4 5 6 7 8 9 10How well can the child follow verbal instructions?

Questions or instructions often have to be repeated

They confuse similar sounding letters They confuse similar sounding words The child has a rich vocabulary They have a good understanding of words

The child’s speech consists of mainly simple sentences

The child has difficulty following pictorial instructions (e.g. flow diagrams, activity guides)

The child reverses letters when writing The sequence of letters is reversed in written words

The child can copy from the board They confuse similar looking words They confuse similar looking letters The child inverts some letters when writing (e.g. uses ‘u’ instead of ‘n’, ‘p’ for ‘b’, ‘w’ for ‘m’ etc

The child’s handwriting is ….. The child can copy pictures ….. Pencil grip is …. The time taken to complete tasks is …. How good is the child at PE?

E. Other relevant information Do the child’s drawings tend to have more detail on one side of the drawing, or are both sides equally well developed? If so, which side? What types of spatial difficulties does the child have? At what age did the child learn to tie their shoe laces? Can they ride a bike? What sorts of interests does the child have? What types of reading do they enjoy?

Appendix 2: Modified Teacher Questionnaire Cognitive Profile Questionnaire (version 2)

This form should be completed by the class teacher of the pupil referred and returned to the school EP prior to work with the child. If more than one teacher works with the child then a composite response could be sent, or the teacher who works with the child most often could complete the questionnaire. Teacher Child’s name Date of Birth

A. Background 1. Is there an identified history of visual difficulty? If so, please give details.

2. Is there an identified history of language difficulty or delay? If so, please give details.

3. What would you say the child’s main strengths are?

4. In what ways have they made progress since joining your class?

B. For the next statements please rate how well each one applies to the child.

(Please select only one rating for each question.)

Statement No Probably No

Possibly Yes

Yes

1. Can the child complete tasks if given more time?

2. Can the child spell some words well but not others?

3. Does the child lack strategies for approaching unfamiliar words in reading and spelling?

4. Is the child good at discriminating between right and left?

5. Is the child good at matching words that have similar features or look similar?

6. Can the child copy from a model e.g. use underwriting, copy from a word frame?

7. Is the child good at games that require remembering words or phrases?

8. Is the child good at games that require remembering objects or shapes?

9. Does the child take part in group discussions?

10. Does the child have more difficulty with polysyllabic words in speech compared with monosyllabic words (e.g. repeating words heard or saying words)

11. Does the child have greater difficulty learning new polysyllabic words than shorter words

C. For the next statements please rate how well the child compares with others in the class.

(Please select only one rating per question.)

Low/Less than most

Average High/More than most Statement or question

1 2 3 4 5 6 7 8 9 101. How well can the child follow

verbal instructions?

2. Questions or instructions often have to be repeated

3. They confuse similar sounding letters

4. They confuse similar sounding words

5. The child has a rich vocabulary 6. They have a good

understanding of words

7. The child’s speech consists of mainly simple sentences

8. The child has difficulty following pictorial instructions (e.g. flow diagrams, activity guides)

9. The child reverses letters when writing

10. The sequence of letters is reversed in written words

11. The child can copy from the board

12. They confuse similar looking words

13. They confuse similar looking letters

14. The child inverts some letters when writing (e.g. uses ‘u’ instead of ‘n’, ‘p’ for ‘b’, ‘w’ for ‘m’ etc

Appendix 3: Q-sort brainstorm questionnaire I am putting together a list of items for a Q-sort to be used with children. The items need to correspond to: • verbal or auditory reasoning • visuo-spatial reasoning Some items do not belong to either category and need to be removed. Please tick which category you think the item belongs to:

Verbal Spatial Neither I like to read I like to draw I like to play with plasticine I like to build paper models I like to sing I like to talk with my friends I like to say poems or rhymes I like to run I like to paint I like to listen to the radio I like to listen to my favourite pop stars I like to colour I like to play music I like to listen to stories I like to look at photographs I like to play I-spy I like to play football I like to tell jokes I like to listen to riddles I like to look at the moon and stars I like to do jigsaws I like to build sandcastles I like to play matching games (like ‘pairs’, or snap)

I like to cut out shapes from paper I like to make voices for my toys

I will need about 20 items all together – please add some more if you can think of any. My next step will be to try it out with children and this will result in more items being rejected.

Appendix 4: Q-sort cards

I like to build paper models

I like to play matching games (like ‘pairs’, or ‘snap’)

I like to do jigsaws

I like to cut out shapes from paper

�

I like to draw

I like to look at photographs

I like to play with plasticine

I like to paint

I like to colour

I like to build sandcastles

I like to talk with friends

I like to make voices for my toys

I like to tell jokes

I like to listen to riddles

I like to say poems or rhymes

I like to listen to my radio

I like to listen to my favourite pop stars

I like to listen to stories

I like to play I-Spy

I like to sing

Appendix 5: Q-Sort Scoring sheet Name Age

(Layer 1=3, 2=2, 3=1, 4= -1, 5= -2, 6= -3) Item Verbal Spatial I like to build paper models 3 2 1 –1 –2 –3I like to play matching games (like ‘pairs’, or ‘snap’)

3 2 1 –1 –2 –3

I like to do jigsaws 3 2 1 –1 –2 –3I like to cut out shapes from paper 3 2 1 –1 –2 –3I like to draw 3 2 1 –1 –2 –3I like to look at photographs 3 2 1 –1 –2 –3I like to play with plasticine 3 2 1 –1 –2 –3I like to paint 3 2 1 –1 –2 –3I like to colour 3 2 1 –1 –2 –3I like to build sandcastles 3 2 1 –1 –2 –3I like to talk with friends 3 2 1 –1 –2 –3 I like to make voices for my toys 3 2 1 –1 –2 –3 I like to tell jokes 3 2 1 –1 –2 –3 I like to listen to riddles 3 2 1 –1 –2 –3 I like to say poems or rhymes 3 2 1 –1 –2 –3 I like to listen to my radio 3 2 1 –1 –2 –3 I like to listen to my favourite pop stars 3 2 1 –1 –2 –3 I like to listen to stories 3 2 1 –1 –2 –3 I like to play I-Spy 3 2 1 –1 –2 –3 I like to sing 3 2 1 –1 –2 –3

Totals (Bottom) (Top)

Verbal preference Equal Spatial preference 18 16 14 12 10 8 6 4 2 0 2 4 6 8 10 12 14 16 1818 16 14 12 10 8 6 4 2 0 2 4 6 8 10 12 14 16 18

Red = negative values

Appendix 6: Word cards for learning experiment

Verbal A Verbal B Spatial A Spatial B

sat

dog

sat dog

hand

fish

hand fish

show

step

show step

nest

well

nest well

block

child

block child

flowers

hundred

flowers hundred

unless

kettle

unless kettle

hunger

nobody

hunger nobody

human

elbow

human elbow

example

quilted

example quilted

Appendix 7: S-Cue Card

Appendix 8: V-Cue Card

Appendix 9: Learning Experiment Record Sheet (version 1) Name Age

Counterbalance Verbal A then Spatial B � Spatial A then Verbal B �Verbal B then Spatial A � Spatial B then Verbal A �

Verbally Cued Perception Word Trial

1Trial

2Trial

3Trial

4Trial

5Trial

6Trial

7Trial

8Trial

9Trial 10

12345678910

Total

Visually Cued Perception Word Trial

1Trial

2Trial

3Trial

4Trial

5Trial

6Trial

7Trial

8Trial

9Trial 10

12345678910

Total

Overall scores (Total for each trial, giving credit for all trials after a perfect score has been achieved) Verbal score Spatial score

Appendix 10: Learning Experiment Record Sheet (version 2) Name Age

Counterbalance Verbal A then Spatial B � Spatial A then Verbal B �Verbal B then Spatial A � Spatial B then Verbal A �

Verbally Cued Perception Word Baseline Trial 1 Trial 2 Trial 3 Trial 4 Trial 51 (3) 2 (4) 3 (4) 4 (4) 5 (5) 6 (7) 7 (6) 8 (6) 9 (5) 10 (7)

Total (52)

Visually Cued Perception Word Baseline Trial 1 Trial 2 Trial 3 Trial 4 Trial 51 (3) 2 (4) 3 (4) 4 (4) 5 (5) 6 (7) 7 (6) 8 (6) 9 (5) 10 (7)

Total (52)

Verbal score Spatial score

Scoring For each word – count each letter correctly in place (discounting omissions or substitutions). E.g. ‘flowers’ – possible score = 7

Fowrs = 5 Flowrs = 6 Flowurs = 6

Additions. Credit all letters up to the additional letter, subtract 1 for the additional letter, then score the remaining letters as above. Flouwers =

flo = 3, u=-1 [addition], wers=4 = 6

Flawours = fl=2, a=0 [substitution], w=1, ou=0, -1 [substitution & addition], rs=2

For each learning method: Compare both methods. If the child has not scored 52 in either condition then use the final trial score and subtract the baseline score to get a difference for each condition.

Appendix 11: Sentences for dictation (Source: Boder and Jarrico, 1982) List A

Sat The boy sat on the floor Hand He throws the ball with his right hand Show Show me how tall you are Nest The bird is in the nest Block Put one block on top of another Flowers Roses are flowers Unless Don’t buy anything unless you need it Hunger Hunger makes food taste better Human The human hand is a wonderful instrument Example A peach is an example of a fruit

List B

Dog The dog barked Fish A fish lives in water Step Don’t step on the wet floor Well She sings very well Child The child is awake now Hundred Big aeroplanes hold more than 100 people Kettle The water is boiling in the kettle Nobody Nobody answered the phone Elbow The arm bends at the elbow Quilted A quilted jacket is light and warm

Appendix 12: Summary of measures

BAS-II Teacher View (TeacherQuestionnaire)

Child'sView Learning Experiment Age M/F

Pupil

Verbal

Score

Non-verbal

Spatial Score

GC

A/

SNV

C

Overall

Verbal

Overall

spatial

Within

Verbal

Within

Spatial

related verbal

related spatial

Q-sort

(spatial score)

Verbal

Baseline

Verbal

Final

Verbal

Difference

Spatial B

aseline

Spatial Final

Spatial difference

List order

P1 118 96 95 104 67 54 7 7 60 47 4 -1 6Spa A,Ver B

10yr8 M

P2 84 87 100 88 35 28 8 2 27 26 7 13 5Ver A,Spa B 8yr 6 M

P3 78 100 90 94 None available -4 10 3Ver B,Spa A

10yr4 F

P4 86 64 104 81 19 38 0 7 19 31 5PilotGroup Not

PilotGroup done 9yr 2 M

MM 79 62 87 71 38 41 10 8 28 33 2 31 44 13 35 38 3Spa A,Ver B

10yr5 M

LW 68 83 84 73 27 47 7 5 20 42 5 46 56 10 41 53 12Ver A,Spa B

10yr6 M

ME 118 87 111 107 43 51 9 6 14 36 -2 37 47 10 37 41 4Spa B,Ver A

10y3 M

GC 82 71 91 77 40 44.5 5 9.5 35 35 4 32 47 15 32 44 12Ver B,Spa A 8yr 6 M

SH 78 86 92 82 42 57 5 7 37 50 1 21 36 15 19 36 17Spa B,Ver A

10y1 F

JW 94 86 103 93 51 52 8 4 43 48 -6 28 32 4 27 30 3 Ver A, 9y 0 M

Spa B

JD 71 69 98 74 27 49 5 8 22 41 4 8 18 10 8 19 11Ver A,Spa B

8yr11 M

KH 86 80 93 83 29 46 8 9 21 37 6 28 47 19 39 49 10Spa A,Ver B 8yr 7 F

JG 115 130 99 118 51 40 13 7 38 33 0 43 48 5 41 46 5Spa A,Ver B 9y 5 M

JM 117 101 93 105 40 48.5 7 4.5 33 44 2 33 49 16 34 52 18Ver A,Spa B 10 yr M

JS 110 96 99 102 30.5 34 4.5 6 26 28 1 39 48 9 40 44 4Ver B,Spa A

10y11 M

AW 110 83 84 91 37 38 6 7 31 31 10 27 38 11 28 36 8Spa A,Ver B

10y3 M

SJ 103 93 86 93 39 38.5 3 4.5 36 34 -6 42 43 1 39 39 0Ver A,Spa B 9y 3 M

DS 80 82 100 84 32 36 8 7 24 29 9 38 38 0 28 33 5Spa B,Ver A 9y 2 M

CM 77 100 100 90 25 44.5 7 6.5 18 38 4 15 35 20 16 22 6Ver B,Spa A 8y 7 M

RH 82 87 94 85 36 40.5 9 6.5 27 34 -11 20 29 9 22 33 11Spa A,Ver B

8y11 M

AK 69 83 98 89 42 64 3 9 39 55 -1 35 39 4 39 48 9Spa A,Ver B

10y2 M

JS2 123 104 86 106 32 33 5 6 27 27 -1 9 18 9 8 13 5Ver B,Spa A 8y 2 M

LA 92 75 67 74 39 45 7 10 32 35 2 28 39 11 29 39 10Spa B,Ver A

8y10 M

Appendix 13 Categories of spelling errors made by 7 pupils

Phonological Visual

Phoneme-grapheme

Pupil

BA

S type S=Spatial, V=V

erbal)

Pre-spelling

Syllabic-grapheme

Phonemic representation

(one sound per syllable)

Final consonant om

ission

Vow

el omission

Imm

ature speech pattern evident in

spelling

Orthographic (phonologically

plausible)

closure

Sequence/addition/omission

orientation

form

global

Morphem

ic

DLe S Dws(does)

LaurthingWhresBrigeAlthowFolt (fault)Measherd(measured)Wen(corrected to‘when’ asseen)

Whres (worse) Sourch(search)

Soal(soil)Chach(catch)Sourch(search)Bright(bridge)

Mourning(morning)Althow(although)Des/dos(does)

Phonological Visual

Phoneme-grapheme

Pupil

BA


erbal)

Pre-spelling

Syllabic-grapheme



Final consonant omission

Vow

el omission

Imm

ature speech pattern evident in spelling

Orthographic (phonologically plausible)

closure


orientation

form

global

Morphem

ic

DMo V EnbarestInbarest(embarrassed)OfftenOf (off)FreazeTo (two)

Sould (should)Offten (+f)Of (off)Supsotuated (+‘a’, - ‘t’,substituted)Stiker (striker)To (two)

Supsotuated(p/b)

Enbarest(m/n)

Freaze(freeze)

OftenOf (off)FreazeTo (two)

Phonological Visual

Phoneme-grapheme

Pupil

BA


erbal)

Pre-spelling

Syllabic-grapheme




Vow

el omission

Imm



plausible)

closure


orientation

form

global

Morphem

ic

TSh S Lising(listening)

Berch (birch)WaginBoRt(bought)

Thoms (a)Lising(listening)Thak (n)

Colder(called)

OBa V Arm(home)

‘wurk’(work)‘wurck’(walk)

Darck (back +r)Muring (-n)Shit (+h)Dush(bus +h)

Darck (b/d)Dush (b/d)Dox (b/d)

Muring(morningo/u)

Thet(then)

Phonological Visual

Phoneme-grapheme

Pupil

BA


erbal)

Pre-spelling

Syllabic-grapheme




Vow

el omission

Imm



plausible)

closure


orientation

form

global

Morphem

ic

Mho S breakd Smash(smashed)Put (b/psound)

Skying(skiing)

Frount (+u)‘Thieir’

Put(but b/p)

skying

Phonological Visual

Phoneme-grapheme

Pupil

BA


erbal)

Pre-spelling

Syllabic-grapheme




Vow

el omission

Imm



plausible)

closure


orientation

form

global

Morphem

ic

LHi V Chamboree(ch/j)

Chille(//Chil//ly)Were(homophone)Genrally

Acomping(accompanying)Chille (Chile)Be (omittedword)Were (-h)Meet (+e)Genrally (-e)

Were(where)

Phonological Visual

Phoneme-grapheme

Pupil

BA


erbal)

Pre-spelling

Syllabic-grapheme




Vow

el omission

Imm



plausible)

closure


orientation

form

global

Morphem

ic

JRi S Frans(France)FereyOwer (our)

Ower(our)

Cognitive Preference and Spelling Difficulties

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