LeMoNAdE: Learner Context Modelling

and Adaptation for E-Learning based on

Socio-Constructivist Approach and

Semantic Measures

Thesis submitted to Pondicherry University in partial

fulfillment of the requirements for the award of the degree of

DOCTOR OF PHILOSOPHY

in

COMPUTER SCIENCE AND ENGINEERING

by

R.SUNTIHA

DEPARTMENT OF COMPUTER SCIENCE

SCHOOL OF ENGINEERING AND TECHNOLOGY

PONDICHERRY UNIVERSITY

PUDUCHERRY – 605014

APRIL 2014

i

CERTIFICATE

This is to certify that this thesis titled “LeMoNAdE: Learner Context Modelling

and Adaptation for E-Learning Based on Socio-Constructivist Approach and

Semantic Measures” submitted by Ms. R.Sunitha, to the Department of Computer

Science, School of Engineering and Technology, Pondicherry University, Puducherry,

India for the award of the degree of Doctor of Philosophy in Computer Science and

Engineering is a record of bonafide research work carried out by her under my

guidance and supervision.

This work is original and has not been submitted, in part or full to this or any other

University / Institution for the award of any other degree.

Place : Puducherry Prof. G. Aghila., B.E.(Hons)., M.E., Ph.D.

Date : (Guide & Supervisor)

Dept. of Computer Science & Engineering,

NIT Puducherry,

Karaikal,

India.

ii

DECLARATION

I hereby declare that this thesis titled “LeMoNAdE: Learner Context Modelling

and Adaptation for E-Learning Based on Socio-Constructivist Approach and

Semantic Measures” submitted to the Department of Computer Science, School of

Engineering and Technology, Pondicherry University, Puducherry, India for the

award of the degree of Doctor of Philosophy in Computer Science and

Engineering is a record of bonafide research work carried out by me under the

guidance and supervision of Prof. G. Aghila. This work is original and has not been

submitted, in part or full to this or any other University / Institution for the award of

any other degree.

Place : Puducherry R.Sunitha

Date :

iii

ACKNOWLEDGEMENTS

I would like to thank my guide and supervisor Prof. G.Aghila for being beyond my

guide, soothing me with her invaluable love, support and direction throughout my

research period. I am indebted to her for the discerning guidance, for the copious

trust she had in me and for the privilege she granted me to explore my own path in

research.

I gratefully acknowledge the perceptive remarks and guidance of my Doctoral

Committee members Prof. N.Sreenath, Department of Computer Science and

Engineering, Pondicherry Engineering College and Dr. V.Prasanna Venkatesan,

Associate Professor, Department of Banking Technology, Pondicherry University.

Their comments, suggestions and ideas have helped me a lot to bring out this thesis

successfully.

I owe my gratitude to my beloved teacher Prof. R.Subramanian, Head of the

Department, Department of Computer Science cum Dean, School of Engineering and

Technology, Pondicherry University for being a source of inspiration for me and for

his support in getting this research work well accomplished.

I am indebted to Prof. S.Kuppuswami without whom I am not what I am today. I

sincerely thank Dr. T.Chitralekha, Dr. T.GeethaRamani for their unconditional love

showered on me. I have a special mention to Dr. S.Siva Sathya, for her scholastic and

moral support in my academic and personal life as well.

I am beholden to my friends Ms. V.Uma and Ms. P.Shanthi Bala for their

unprejudiced friendship, love and care which have been transpiring my life at

Pondicherry University ever wonderful.

I am grateful to Dr. K.Saruladha for her support, prayers and encouragement. I

appreciate the friendship, motivation and support of my research mates

Dr. K.S.Kuppusamy, Dr. P.Thiyagarajan and Mr. Ajit Kumar. I am thankful to

Dr. R. Baskaran, PAC, Cuddalore for his help in bringing out this thesis.

I sincerely thank Mr. T.Siva Kumar, Dr. M.Nandhini and Ms. S.Vijayalakshmi for

their love and care and Mr. K.Suresh, Senior Technical Assistant for his technical

iv

support. I express my appreciation to Dr. K.Suresh Joseph for his moral support

during my crucial times.

Beyond the academic purview, I am obliged to the love, care, support and

understanding shown by members of my family towards all walks of my life.

I am much obliged to my parents Mr. K.Ramamirtham and Mrs. R.Kalpana for their

tremendous faith in me and for cheering me up whenever I felt down in my spirit.

I bestow my gratitude to my adorable husband Dr. K.Pajanivelou who pulled me off

my comfort zone and kindled the light within me. I am thankful to my loving daughter

S.P.Maanila and my cute little son S.P.Aadhan for being empathetic, serene and

tolerant during the period of my research.

I am happy to have a wonderful set of family members Mr. B.Manoharan,

Mrs. R.Kanmani, Mrs. R.Lalitha, Mr. M.Sadish Kumar, Mr. R.Ramkumar,

Mrs. R.Ramya and Mr. M.Nandha Kumar who were always been there for me to

share, rejoice and lean on.

I had always let the choice onto Him and He has always given me the best; finally, I

thank the Almighty for this beautiful life.

R.Sunitha

v

ABSTRACT

E-learning has become an inevitable web application serving as the catalyst changing

the model of modern day learning. An adaptive e-learning environment provides an

alternative to the traditional “one-size-fits-all” approach to avoid the disorientation,

disinterest and frustration of the learner combined with the commercial objective of

one-to-one marketing. Adaptation based on learner’s context has become the need of

the hour as the current application development paradigm moves toward smartness.

Three important components of context aware adaptive e-learning viz. the learner

context model, the adaptation strategy and the learning resources have been focused

in this thesis with the aim of ameliorating the performance of context aware adaptive

e-learning systems. The focus is on providing a dynamic learner context model and an

adaptation strategy that is based on the understanding of the learner’s context

grounded by theoretical foundations of learning. The thesis also focuses on employing

the semantic metadata knowledge of learning objects for complementing the process

of adaptation. The conceptual framework comprising the proposed learner context

model, the adaptation strategy and the semantic measures is named as LeMoNAdE

(Learner Context Modelling and Adaptation for E-learning).

A detailed literature review has been carried out related to context aware adaptation in

e-learning and this research focuses on addressing the identified shortcomings viz.

absence of context models that exemplify the dynamic situation of the learner, the

proportional amalgamation of theory and technology for effective adaptation and the

usage of semantic metadata of learning resources for adaptation.

A dynamic learner context model has been proposed incorporating Learning

Efficiency, a contextual dimension that dynamically embodies the learner’s situation

based on an accurate definition of context proposed in milieu of e-learning. The

mathematical formulation of learning efficiency has been expressed as LEMOn the

Learning Efficiency Computation Model. An efficient rule-based adaptation strategy,

based on “Zone of Proximal Development”, a socio-constructivist approach has been

proposed which is based on the proposed learner context model. The semantic

metadata of the learning objects has been espoused to propose a suite of semantic

vi

measures encompassing the Thematic Distance Measure, Thematic Proximity

Measure and Feature Similarity Measure.

The proposed learner context model has been found to be complete and sound through

the statistical and empirical analyses respectively. The results of the correlation

analysis carried on the different parameters constituting LEMOn indicate that the

proposed learner context model is complete. The results of the collaborative filtering

recommendation system, CARE (Context Aware Recommendation for E-learning)

built on the basis of the proposed learner context model prove the soundness of the

context model. The employment of the learner context model improves the accuracy

of predictions which has been evaluated through the precision, recall and mean

percentage error metrics. SAFE (Scaffolded E-learning), a system developed on the

basis of the proposed adaptation mechanism to provide learner context based adaptive

support during assessment, shows increase in learning efficiency against comparison

with standard system. The application of the proposed semantic measure suite for

ranked learning objects provision and learning sequence generation has shown

significant increase in learner satisfaction measured using normalized discounted

cumulative gain and learning efficiency of the learner.

The conclusion derived from this thesis is that a perfect amalgamation of an accurate

learner context model and an effective adaptation mechanism improves the

performance of context aware adaptive e-learning. This has been proved by the

inclusion of learning efficiency, a computed contextual dimension in defining the

learner context which resulted in the increase of learner satisfaction and performance.

The use of a learning theory in designing the adaptation strategy and the inclusion of

semantic metadata knowledge of learning resources for adaptation also have proved to

escalate the learner performance.

The future directions of this research include the design and augmentation of

quantification mechanisms for learner’s psychological aspects like motivation and

emotions into the learner context model and the design of appropriate adaptation

strategies.

vii

TABLE OF CONTENTS

CERTIFICATE i

DECLARATION ii

ACKNOWLEDGEMENTS iii

ABSTRACT v

LIST OF TABLES xii

LIST OF FIGURES xiii

LIST OF ABBREVIATIONS xvi

1. INTRODUCTION 1

1.1 CONTEXT AWARE COMPUTING 2

1.2. CONTEXT AWARE E-LEARNING SYSTEMS 3

1.2.1 Context Modelling 4

1.3 CONTEXT AWARE RECOMMENDATION IN E-LEARNING 5

1.4 CONTEXT AWARE ADAPTATION IN E-LEARNING 6

1.4.1 Factors for Adaptation 6

1.4.2 Elements of Adaptation 7

1.4.3 Approaches to Adaptation 8

1.5 LEARNING THEORIES 8

1.5.1 Zone of Proximal Development 9

1.6 LEARNING OBJECTS AND METADATA STANDARDS 10

1.7 ONTOLOGY AND SEMANTIC MEASURES 10

1.8 MOTIVATION 11

1.9 CONTRIBUTIONS OF THE THESIS 12

1.10 ORGANIZATION OF THE THESIS 12

2. LITERATURE SURVEY 14

2.1 CONTEXT AWARE E-LEARNING 14

2.1.1 Definition of Context 15

2.1.2 Classification of Context 17

2.1.3 Parameters of Learner Context 20

2.1.4 Representation of Context 24

viii

2.2 CONTEXT AWARE E-LEARNING APPLICATIONS 25

2.2.1 Context Aware Recommendation 26

2.2.2 Context Aware Adaptation 29

2.3 THEORETICAL UNDERPINNINGS TO E-LEARNING 35

2.3.1 Zone of Proximal Development 35

2.3.2 Scaffolding 36

2.4 LEARNING OBJECTS AND METADATA STANDARDS 38

2.5 SEMANTIC MEASURES 39

2.5.1 Path based Measures 40

2.5.2 Information Content based Measures 40

2.5.3 Text based Measures 42

2.5.4 Semantic Measures in E-learning 42

2.6 SUMMARY 43

3. PROBLEM STATEMENT AND RESEARCH METHODOLOGY 44

3.1 PROBLEM STATEMENT 44

3.2 SCOPE OF THE RESEARCH 46

3.3 RESEARCH METHODOLOGY 47

3.4 SUMMARY 47

4. OVERVIEW OF LeMoNAdE 48

4.1 THE LeMoNAdE FRAMEWORK 48

4.2 THE LEARNER CONTEXT MODEL 49

4.3 THE ADAPTATION STRATEGY 50

4.4 THE SEMANTIC MEASURES 50

4.5 SUMMARY 51

5. LeMoNAdE: THE LEARNER CONTEXT MODEL 52

5.1 NEED FOR UNDERSTANDING LEARNER CONTEXT 52

5.2 OBJECTIVES OF LEARNER CONTEXT MODEL 52

5.3 LEARNER CONTEXT DEFINED 53

5.3.1 Characteristics of the E-learning Environment 53

ix

5.3.2 Proposed Definition of Learner Context 53

5.4 PROPOSED LEARNER CONTEXT MODEL 55

5.4.1 Presumed Characteristics of Learner Context Model 55

5.4.2 Elements of the Proposed Learner Context Model 56

5.5 THE COGNITIVE CONTEXT OF LEARNER 57

5.5.1 Knowledge Level of the Learner 57

5.5.2 Preference of the Learner 58

5.5.3 Cognitive Ability of the Learner 58

5.6 ACHIEVEMENT LEVEL DESCRIPTORS 58

5.7 THE RESOURCE CONTEXT OF THE LEARNER 59

5.7.1 Achievement Level Descriptors for Learning Resources 59

5.8 THE LEARNING EFFICIENCY CONTEXT OF THE LEARNER 59

5.8.1 Learning Efficiency as a Consolidation of Learner Context 60

5.8.2 Features of Learning Efficiency 61

5.8.3 Achievement Level Descriptors for Learning Efficiency 61

5.9 LEMOn – THE LEARNING EFFICIENCY COMPUTATION MODEL 61

5.9.1 The Temporal facet of LEMOn 62

5.9.2 The Retention facet of LEMOn 63

5.9.3 The Performance facet of LEMOn 64

5.10 FEATURES OF THE LEARNER CONTEXT MODEL 65

5.11 ADVANTAGES OF THE LEARNER CONTEXT MODEL 66

5.12 SUMMARY 67

6. LeMoNAdE – THE ADAPTATION STRATEGY 68

6.1 NEED FOR AN EFFECTIVE ADAPTATION STRATEGY 68

6.2 A SOCIO-CONSTRUCTIVIST APPROACH TO ADAPTATION

DESIGN 68

6.2.1. Identification of the Right Zone of Context to Adaptation 69

6.2.2. Identification of the Right Element to Adaptation 73

6.3 FEATURES OF THE ADAPTATION STRATEGY 77

6.4 COMPLEMENTING THE ADAPTATION STRATEGY 77

6.5 SUMMARY 77

x

7. LeMoNAdE: SEMANTIC MEASURES 78

7.1. THE TOPIC-CONCEPT-INSTANCE STRUCTURE 78

7.2. SEMANTIC MEASURES 80

7.2.1 Presumptions on the Proposed Semantic Measures 82

7.3. THEMATIC DISTANCE MEASURE 83

7.3.1. Formulation of Thematic Distance Measure 83

7.4 THEMATIC PROXIMITY MEASURE 85

7.4.1 Formulation of Thematic Proximity Measure 85

7.5 FEATURE SIMILARITY MEASURE 86

7.5.1 Formulation of Feature Similarity Measure 86

7.6 SUMMARY 87

8. EVALUATION OF THE LeMoNAdECOMPONENTS 88

8.1 EVALUATION OF THE LEARNER CONTEXT MODEL 89

8.1.1 Completeness Analysis 89

8.1.2 CARE: Context Aware Recommendation for E-Learning 96

8.2 EVALUATION OF THE ADAPTAT ION STRATEGY 105

8.2.1 SAFE: Scaffolded E-Learning 105

8.2.2 Description of the English Spelling Teaching Tool 105

8.2.3 The SAFE Components 105

8.2.4 Evaluation of the SAFE System 109

8.3 EVALUATION OF THE SEMANTIC MEASURES 111

8.3.1 Efficacy of the TCI Structure, TPM and FSM 111

8.3.2 Realization of TPM 113

8.3.3 Realization of FSM 121

8.3.4 Axiomatic Evaluation of the Distance Measures 125

8.4 SUMMARY 127

xi

9. CONCLUSION AND FUTURE ENHANCEMENTS 128

9.1 CONCLUSION 128

9.2 FUTURE ENHANCEMENTS 131

REFERENCES 132

LIST OF PUBLICATIONS 146

VITAE 148

xii

LIST OF TABLES

TABLE No TITLE PAGE No

1.1 Organizations Providing Adaptive Learning Solutions 12

6.1 Adaptation Strategy based on Conformance to

Comprehensive LE Condition

74

6.2 Adaptation Strategy for Case 2 and Case 3 Conditions 76

7.1 Description about the Measures and Parameters used 82

8.1 Relevancy Analysis of Actual Components of LEMOn 94

8.2 Interdependency Analysis between Components of

LEMOn (Δ)

96

8.3 Interdependency Analysis between η and other

Components of LEMOn Variant (χ)

96

8.4 Interdependency Analysis between σ and other

components of LEMOn variant (χ)

96

8.5 Confusion Matrix 102

8.6 Weights assigned to the Different Relations between the

Concepts

116

8.7 Best path, TDM and TPM between Concept Pairs 119

8.8 Comparison of the performance of TPM based LSG and

standard LSG

120

8.9 Values assigned for Instances of Feature Classes 122

8.10 Ordinal Values of LO Ratings by Learners 124

8.11 Efficiency Analysis of FSM based LO Ranking

124

xiii

LIST OF FIGURES

FIGURE No TITLE PAGE No

1.1 Dynamics of Context Management 3

1.2 Taxonomy of Learner Context 4

1.3 Zone of Proximal Development 9

2.1 Overview of the Literature Survey Topics 16

2.2 Classification of Context 19

2.3 Taxonomy of Context by Example 20

2.4 Levels of Adaptation in Context Aware E-learning 31

2.5 Types of Scaffolds 37

2.6 The IEEE LOM Metadata Categories 39

2.7 Classification of Semantic Relatedness Measures 41

3.1 Conceptual Research Framework 46

4.1 The LeMoNAdE Framework

49

5.1 Dimensions of the Proposed Context Model 57

6.1 Different Zones of Development 71

6.2 Dynamic Adaptation Strategies for different Zones of

Development

72

7.1 Topic-Concept-Instance Structure 80

7.2 Elements of the Structure Ontology 81

7.3 An Example of Concepts and Weighted Relations 84

8.1 Screen Shot of the Data Structure Course Web Page 91

xiv

8.2 Standard Collaborative Filtering Recommendation

Algorithm

97

8.3 Proposed Collaborative Filtering Recommendation

Algorithm

97

8.4 Prediction Accuracy of Top-5 Recommendations 103

8.5 Prediction Accuracy of Top-10 Recommendations 104

8.6 Prediction Accuracy of Top-15 Recommendations 104

8.7 Prediction Accuracy of Recommendation Algorithms

w.r.t. MPE

104

8.8 Screen shots of the English Spelling Teaching Tool 106

8.9 Extended IEEE LOM Metadata 109

8.10 Comparison of the Scaffolded and Non Scaffolded

Systems w.r.t. Completion Time

110

8.11 Comparison of the Scaffolded and Non Scaffolded

Systems w.r.t. Assessment Score

110

8.12 Partial Owl Representation 114

8.13 Visualization of Data Structures Ontology 115

8.14 Path Weight Computation Algorithm 117

8.15 TDM Computation Algorithm 118

8.16 Learning Sequence Generation Algorithm 119

8.17 Graph Representing the FSM Value of Feature Sets

Relative to the Desired Feature Set Positioned at

Origin

123

8.18 Comparison of Recommendation Efficiency of FSM

(NDCG-P) Against Standard Mechanism (NDCG)

125

xv

8.19 Conformance to the Triangular Inequality Axiom by

TDM

126

8.20 Conformance to the Triangular Inequality Axiom by

FSM

126

xvi

LIST OF ABBREVIATIONS

Abbreviation Expansion

ADL Advance Distributed Learning Initiative

ALD Achievement Level Descriptors ANN Artificial Neural Networks

BYOD Bring Your Own Device CARE Context Aware Recommendation to E-learning

CZD Comfort Zone of Development

DCG Discounted Cumulative Gain ECCAM Expert Control Content Adaptation Model

EZD Expert Zone of Development

FCF Fletcher’s LE based Collaborative Filtering

FPR False Positive Rate

FSM Feature Similarity Measure

GPS Global Positioning System HCI Human Computer Interaction IC Information Content ICT Information and Communication Technology

IDCG Ideal Discounted Cumulative Gain

KPA Knowledge level, Preferences, Cognitive Ability

LA Learning Achievement

LBD Lower Bound Descriptor LCM Learning Context Model LCS Least Common Subsumer LE Learning Efficiency

LED Learning Efficiency Descriptor

LEMOn Learning Efficiency Computation Model

LeMoNAdE Learner Context Modelling and Adaptation in E-learning

LMS Learning Management Systems

LO Learning Objects LOCH Language Learning Outside the Classroom with Handhelds

LOM Learning Object Metadata

LOR Learning Object Repository LR Learning Rate

LRD Learning Resource Descriptor

xvii

LSG Learning Sequence Generation

MPE Mean Percentage Error

MVC Model-View-Controller

NDCG Normalized Discounted Cumulative Gain PD Performance Descriptors

RD Retention Descriptors SAFE Scaffolded E-learning SCF Standard Collaborative Filtering

SCORM Sharable Content Object Reference Model

TCI Topic-Concept-Instance

TD Temporal Descriptors

TDM Thematic Distance Measure TPM Thematic Proximity Measure TPR True Positive Rate

UBD Upper Bound Descriptor

ZOD Zone of Development ZPD Zone of Proximal Development

1

CHAPTER 1

INTRODUCTION

It is not the strongest of the species that survives, nor the most intelligent, but the one

most responsive to change.

- Charles Darvin

Internet is one of the technological innovations that have resulted in major paradigm

shifts facilitating people with the ease of communication and business embellishment.

Another remarkable paradigm shift brought by Internet is in the field of education.

Evolving Internet technologies redefine the way people teach and learn. The inclusion

of Information and Communication Technology (ICT) and electronic media in

education is termed as E-learning.

E-learning as per the European e-learning Action Plan is defined as the “the use of

new multimedia technologies and the Internet to improve the quality of learning by

facilitating access to resources and services as well as remote exchanges and

collaboration” (Holmes and Gardner, 2006). E-learning has become a popular web

application due to the increased number of high performance computers at homes and

work places, the availability of mobile devices, the improvements in network

technologies, the facility of high speed access to Internet and the acceptance of

electronic commerce (Dietinger, 2003). E-Learning aims to give students a greater

autonomy regarding the point in time, the content and the method by which they learn

by providing on-demand-learning, that eliminates the barriers of time and distance

(Tavangarian et al., 2004). Thus, E-learning focuses on the learners and learning

rather than teachers and teaching (Williams and Goldberg, 2005).

One of the significant research avenues of e-learning is adaptation. An adaptive

e-learning environment provides an alternative to the traditional “one-size-fits-all”

approach focusing on understanding the needs of the learner and tailoring the

e-learning services according to this understanding. A two-fold argument can be laid

in support of adaptation becoming the need of the hour. The former is to avoid the

disorientation, disinterest and frustration of the learner originating due to the massive

size of the accessible resources and the latter is to focus on one-to-one marketing

2

(Cho et al., 2002). Numerous approaches to adaptation have been reported in the

literature and different factors like learner preference (Anagnostopoulos and

Bielikova, 2010), learning style (Popescu, 2010, Samia and Abdelkrim, 2012), learner

interactions (Hwang et al., 2008) and learning outcome (Shi et al., 2013) form the

basis of adaptation in e-learning; the common classification being the learner

information and learner context.

This thesis aims at providing an effective learner context aware adaptation strategy

that is supported by a dynamic learner context model and supplemented by the

semantic metadata of learning objects. The conceptual framework of the solutions is

termed as LeMoNAdE (Learner Context Modelling and Adaptation in E-learning).

The primary elements focused in this research on context aware adaptive e-learning

have been introduced in this chapter.

1.1 CONTEXT AWARE COMPUTING

There are certain things people do and require when they are in a situation (Schilit et

al., 1994) which is predictable. Context aware computing aims at exploiting this

situational knowledge and serves the need arising due to it. Context is generally

associated with Ubiquitous Computing, wherein computation is distributed and

embedded and interaction happens through the environment rather than a desktop

computer. Yet context is conceived as a significant factor in other areas of Human

Computer Interaction (HCI) research also (Dourish, 2001). Context, the core of

context aware systems is defined using the interaction of observable and relevant

attributes of the user with other entities and the environment at an instance of time.

Context aware systems are those that react based on their sensed environmental data

(Hoyos et al., 2013). These systems examine the context of the user and react to the

change in the user’s context. The need enthused by the new context is assessed and

the services of the application are adapted according to the current need without

user’s intervention. The dynamics of context aware applications with respect to

context management has been illustrated in Figure 1.1.

The major differences between context aware applications and personalization based

adaptive applications are factors triggering adaptation and the level of user

involvement in adaptation.

3

Figure 1.1 Dynamics of Context Management

An adaptive application aiming at personalization, models the user explicitly or

implicitly, statically or dynamically. On the contrary a context aware adaptive

application, models the situation of the user which also includes the understanding of

the user himself and other dynamic changing factors that surround him/her and this

understanding is normally built with less user intervention.

Thus context aware adaptation necessarily depends on two aspects. The former being

the identification of information that defines the context of the user and the latter

being the formulation of a mechanism to reason out context and to trigger relevant

services aka adaptation. The identified context information is used (Dey, 2001) by

the context aware applications to

Present the relevant information and services

Automatically trigger execution of relevant services

Tag the context information for later retrieval

In this thesis, all the three usages of context information listed have been employed.

The next section presents an overview of context aware adaptive e-learning systems.

1.2 CONTEXT AWARE ADAPTIVE E-LEARNING SYSTEMS

A context aware adaptive e-learning system adjusts its behavior and functionality

according to the learner’s educational needs like learning goals and interests, personal

characteristics of the learner like learning style, knowledge level, the physical

elements of the environment like light, sound and other information like location, time

and delivery medium requiring less learner attention and fewer learner interactions.

The heart of context aware e-learning is the learner context model.

Context Elicitation

Sense context

Context Representation

Understand context

Context Processing

Interpret, Aggregate, Infer

context

4

1.2.1 Context Modelling

Context modelling is the process of selecting, describing, acquiring and representing

the elements and behavior of the learner and the enclosing environment, defined as

context. The fundamental notion to learner context modelling is the understanding of

learner context. According to (Siadaty et al., 2008) learner context is divided into

learning context and mobile context wherein the former deals with the learner

himself, learning resources, learning activities, instructional design and the latter deals

with the instruction delivery medium and characteristics of the physical environment.

In this thesis, the description of learner context phase of learner context modelling

process is considered and learner context refers only to the learning context and does

not include the mobile context of the learner.

1.2.1.1 Definition of Context

Context, a fundamental notion of psychology is the vital component of context aware

systems. Context, though an overused terminology in psychology and other

disciplines, is not well-defined (Bazire and Brézillon, 2005). Oxford dictionary

defines context as “the circumstances that form the setting for an event, statement, or

idea, and in terms of which it can be fully understood”. The widely used definition of

context is of (Dey, 2001) who states “Context is any information that can be used to

characterize the situation of an entity. An entity is a person, place, or object that is

considered relevant to the interaction between a user and an application, including

the user and applications themselves”. In e-learning, several factors constitute the

definition of learner’s context including the parameters of mobile context. Figure 1.2

illustrates the widely used taxonomy of learner’s context.

Figure 1.2 Taxonomy of Learner Context

Learner context

Physical context

Environmental context

Computational context

Personal context

Activity context Social context

5

1.2.1.2 Context Model

An ideal context model is said to be a simple and an explicit model managing

ambiguity and incompleteness, with automatic learning ability and possibly multi-

context modelling (Bolchini et al., 2007). The significant types of formalisms used for

representing context information are key-value, markup scheme, graphical, object-

oriented, logic based and ontology based models (Strang and Linnhoff-Popien, 2004).

This thesis concentrates on identifying the dimensions of learner context first by

delineating learner context. A multi-context modelling approach, using a

comprehensive set of parameters has been used to model learner context. The thesis

does not focus neither on the acquisition mode of context nor on the representation of

context.

1.2.1.3 Applications of Context Information

The inferred learner context can be used in an e-learning application in the following

ways

Recommendation of relevant information and services

Adaptation of relevant information and services

In this research, the learner context information has been used for recommendation

and adaptation of learning resources. The subsequent section introduces the use of

context information in recommendation generation.

1.3 CONTEXT AWARE RECOMMENDATION IN E-LEARNING

Recommender systems assist and augment the natural social process of

recommendation through analysis of data. E-learning recommender systems facilitate

a learner with directions (Zakrzewska, 2011) based on a group of learners sharing

similar features to improve personal competence development plans.

There are three different approaches (Adomavicius and Tuzhilin, 2005) to design

recommendation systems which have been adopted by different application domains

including information dissemination, e-commerce, search engines and e-learning

(Adomavicius and Tuzhilin, 2005) (García-Crespo et al., 2011).

6

They are

Content based filtering

Collaboration based filtering

Hybrid approach

The next section discusses the application of learner context in the adaptation of

e-learning services.

1.4 CONTEXT AWARE ADAPTATION IN E-LEARNING

E-learning systems evolve with the aim of enriching contents using multimedia

technology, but as e-learning matures in industry and research streams, the focus is to

enrich learning outcomes rather on developing infrastructure for mere instruction

provision (Shute and Towle, 2003) (Brusilovsky and Millán, 2007) (Jevremović and

Vasić, 2010). The first generation of e-learning systems which employed the “one-

size-fits-all” philosophy for learning solutions are grieved by cognitive overload,

distraction, frustration and drop outs in learning (Rumetshofer and Wob, 2003). Thus

the compulsion of realizing the relation between individual differences and learning

environments (Cronbach and Snow, 1981) arouses and hence the current E-learning

systems aims at accommodating individuality in pedagogical procedures through the

diffusion of sophisticated techniques (Anagnostopoulos and Bielikova, 2010). Apart

from handling diversity of learners who are engaged in learning activities, the

proliferous growth of educational resources, the diversity in access media and

modalities also urge flexibility aka adaptivity in e-learning (Brusilovsky, 2001).

An e-learning environment is adaptive if it can follow activities of its learners;

interpret the activities and model them; understand learner needs and represent them

adequately in the said models; and finally, serve learners with the available

knowledge models through dynamically managed learning process (Jevremović and

Vasić, 2010).

1.4.1 Factors for Adaptation

The prominent factors for context aware adaptation (Cronbach and Snow, 1981; Shute

and Psotka, 1994; Snow and Lohman, 1989) in e-learning are

7

Learning context parameters

o Psychological factors

o Cognitive abilities and styles

o Learning styles

o Knowledge and Skills

o Interests

Mobile context parameters

o Physical characteristics of environment

o Infrastructure characteristics

1.4.2 Elements of Adaptation

The objective of adaptation in e-learning is to reduce the disorientation, cognitive load

and information overload. Accordingly the major components or services of

e-learning that are subjected to context aware adaptation (Paramythis and Loidl-

Reisinger, 2003) are

Content

Navigation

Learning Object

Learning sequence

Course Structure

Assistance

Assessments

Collaborative support

1.4.2.1 Learning Object Adaptation

Learning Objects (LOs) are the building blocks of e-learning. Learning objects have

been designed as stand-alone educational resources meant to satisfy a specific

objective. Learning objects may differ in complexity, granularity, type of media etc.

and may possess semantic or didactic dependency with one another. Learning object

adaptation facilitates the adaptation of learning object provision matching the need of

the learner characterized by his/her context and the characteristics of the learning

objects.

8

1.4.2.2 Learning Sequence Adaptation

Learning sequence adaptation aka learning path adaptation is yet another prevalent

component of adaptation. The learning sequence is the organization of learning

activities or arrangement of learning resources according to the learner’s objectives.

The sequencing of instruction is significant because sequencing influences the

understanding and retention of the learner (Rumetshofer and Wob, 2003) and is

affected by the learner’s individual cognition.

1.4.3 Approaches to Adaptation

Adaptation is the process of adjusting the learning services based on various factors

related to learners with the objective of improving learner’s educational experience.

There are a number of approaches to model the process of adaptation which can be

classified as

Adaptation rules

Adaptation algorithms

The adaptive mechanism proposed in this thesis is rule-based and built using a

standard learning theory. The next section presents the discussion about the learning

theories used in formal education whose application in e-learning enriches learner

performance.

1.5 LEARNING THEORIES

Learning Theories are conceptual frameworks that describe how information is

absorbed, processed, and retained during learning. There are three predominant

learning theories viz. Behaviorism, Cognitivism and Constructivism (Mergel, 1998).

Behaviorism is based on observable changes in behavior and

focuses on a new behavioral pattern being repeated until it becomes

automatic.

Cognitivism is based on the thought process behind the behavior

using the changes in behavior as indicators to what is happening

inside the learner's mind.

file:///K:/research/synopsis/synopsis%20-%20desktop/references/Learning%20Theories%20of%20Instructional%20Design.htm%23The%2520Basics%2520of%2520Behaviorismfile:///K:/research/synopsis/synopsis%20-%20desktop/references/Learning%20Theories%20of%20Instructional%20Design.htm%23The%2520Basics%2520of%2520Cognitivism

9

Constructivism is based on the premise that one’s own perspective

of the world is constructed through his/her own individual

experiences and schema. Constructivism focuses on preparing the

learner to problem solving in ambiguous situations.

1.5.1 Zone of Proximal Development

Socio-constructivism is a subset of constructivism conceptualized by a Russian

psychologist Vygotsky who claimed that social interaction plays a fundamental role in

the development of cognition which in turn depends upon one’s Zone of Proximal

Development (ZPD) (Vygotsky, 1978). ZPD specifies the difference between what

learners can do with and without help (of more capable peers). Figure 1.3 illustrates

the concept of ZPD.

Figure 1.3 Zone of Proximal Development

The ZPD (Vygotsky, 1978) is defined as

“The distance between the actual developmental level as determined by independent

problem solving and the level of potential development as determined through

problem solving under adult guidance or in collaboration with more capable peers”

Scaffolding (Wood et al., 1976) is an associated terminology which indicates the

dynamic support given to the learner during the learning process.

This thesis employs ZPD as the theoretical basis for the proposed adaptation strategy

which has been realized in an adaptive scaffold rendering system. The following

section discusses about learning objects and metadata standards.

file:///K:/research/synopsis/synopsis%20-%20desktop/references/Learning%20Theories%20of%20Instructional%20Design.htm%23The%2520Basics%2520of%2520Constructivism

10

1.6 LEARNING OBJECTS AND METADATA STANDARDS

Learning Objects are the building blocks of e-learning. (Committee, 2005) defines

learning objects as “any entity, digital or non-digital, which can be used, re-used or

referenced during technology supported learning”. Learning objects have been

designed as standalone educational resources meant to satisfy a specific objective.

Examples of learning objects are instructional contents, tools, multimedia content,

software, people, events and organizations1. Learning objects may possess educational

information like type, content, complexity, granularity, semantic or didactic

dependency with one another etc. This information is tagged as a set of metadata

along with the LOs. In order to enable the reuse and sharing of LOs across different

Learning Object Repositories (LORs) and Learning Management Systems (LMS) it is

necessary to associate LOs with metadata standards. The common metadata standards

are Dublin Core, IMS, Cancore, IEEE LOM, SCORM and ADL.

The next section introduces the role of ontology in representing LO descriptions and

ontology based semantic measures.

1.7 ONTOLOGY AND SEMANTIC MEASURES

Ontology facilitates the formal description of a shared meaning of a set of vocabulary.

In e-learning context, ontology can provide the visual presentation of conceptual

structure about the course topics as quoted by (Wang et al., 2002) which helps in

getting the orientation of learning task as stated by (Aroyo et al., 2002).

The domain knowledge can be well represented using ontology wherein the learning

objects, their attributes and the relationship between the learning objects can be

explicitly defined. The use of ontologies provides models that could easily be

implemented. Hence ontologies not only enable modelling of domain knowledge but

also consider the usage and interaction of ontology among heterogeneous applications

(Qin and Hernández, 2004).

Semantic measures are quantification techniques that are used to evaluate the strength

of the semantic link or relationship between two concepts in an ontology. There are

three different semantic measures viz. semantic distance measures, semantic

1 http://www.ibm.com/developerworks/library/x-think21.html

11

similarity measures and semantic relatedness measures (Gracia and Mena, 2008).

Given an ontological representation of LO metadata, this thesis focuses on the

formulation of a semantic relatedness measure and two distance measures.

1.8 MOTIVATION

The following factors serve as inspiration to this research.

The tremendous growth of the E-learning industry

The phenomenal increase of mobile devices

The heterogeneity of E-learners

E-learning has become an inevitable web application with a vast diversity in its

learner community. According to the April 2013 issue of E-learning! Magazine2 the

Global LMS Market has been furnished as 1,939 million dollars. The issue also

highlights the focus of the next generation LMS as extended hours of work and

academic learning, creation of adaptive models and a comprehensive storage of

individual’s learning experiences.

Mobiles have enabled ubiquitous learning and new models of learning like “BYOD”

(Song, 2014) to emerge. An interesting statistics by the Super Monitoring3 on the

growth of mobile devices in May 2013 indicate that 56% of people own a smart

phone and 50% of mobile phone users, use mobile as their primary Internet source

and 80% of time on mobile is spent on the applications.

Embedding smartness in applications has become active and extensive field of

research in the development of modern applications (Vlasveld, 2007). E-learning

characterized by heterogeneous learners is a good candidate for being a smart

application. In spite of extensive research carried on adaptation of E-learning services

and a number of commercial systems offering adaptive learning solutions as shown in

Table 1.1, an alarming statistics indicating a maximum dropout rate of 80% of

e-learners has been reported in (Rostaminezhad et al., 2013) . Thus with ubiquitous

facility of learning for a highly diversified learner community with varied needs, it

becomes preordained to design an effective adaptation mechanism to improve learner

satisfaction and learning performance.

2 http://elmezine.epubxp.com/title/55545/28

3 http://www.supermonitoring.com/

12

Table 1.1 Organizations Providing Adaptive Learning Solutions4

Name of the

Organization

Adaptive service offered

ALEKS Corporation Question Generation

Carnegie Learning Math software

Cengage Learning Developmental English

Dreambox Learning Path

eSpindle Learning Spelling coaching

Knewton Online test – preparatory

courses

Pearson Education Reading and Mathematics

PrepMe Test preparation

CCKF Learning material

Sherton Software Learning platform

Smart Sparrow Content

1.9 CONTRIBUTIONS OF THE THESIS

In this research, a novel definition to context in milieu of e-learning has been

provided. A dynamic learner context model based on the proposed definition of

learner context has been designed. Based on the dynamic learner context model an

effective dynamic adaptation mechanism has been devised. A suite of three semantic

measures based on the semantic metadata of learning objects has been formulated.

The three semantic measures are Thematic Proximity Measure, Thematic Distance

Measure and Feature Similarity Measure.

The proposed solutions are evaluated through appropriate evaluation metrics and

mechanisms.

1.10 ORGANIZATION OF THE THESIS

This thesis aims at designing an effective adaptation strategy based on a dynamic

learner context model supplemented by semantic metadata of learning objects with

the aim of providing adaptive learning solutions that increases learner satisfaction and

learner performance. The proposed solutions have been incorporated in a conceptual

framework termed as LeMoNAdE. The rest of the thesis is organized as follows.

In Chapter 2, the overview of the state-of-the-art in context aware adaptive e-learning

systems has been discussed elaborately. The review of literature on the definitions of

4 2013 Gates Foundation Report

13

context, dimensions of learner context, context aware adaptive mechanisms, context

aware recommendation approaches, learning theories, semantic measures have been

presented elaborately. In Chapter 3, the discussion about the motivation drawn from

literature survey and problem statement of the thesis outlining the research issues

have been investigated.

Chapter 4 gives an introduction to the conceptual framework LeMoNAdE that

provides a class of solutions. All the individual elements of the framework are

introduced in this chapter.

Chapter 5 deals with the learner context model component of LeMoNAdE. The

discussion about the proposed learner context model including the discussion about

the proposed mathematical model namely LEMOn (Learning Efficiency computation

Model), the rationale behind the design and development of the model have been

discussed elaborately in this chapter.

Chapter 6 presents the discussion about the adaptation component of LeMoNAdE. A

socio-constructivist approach based solution to achieve dynamic adaptation has been

presented in this chapter.

Chapter 7 discusses the formulation of the different semantic measures based on the

ontological representation of LO. Three different semantic measures namely,

Thematic Distance Measure (TDM), Thematic Proximity Measure (TPM) and Feature

Similarity Measure (FSM) have been defined and mathematical formulations for the

measures have been provided.

Chapter 8 presents the discussion on the experiments conducted to check the

efficiency of the proposed solutions. The results have been analyzed using different

metrics and the discussions on them have been presented in this chapter.

Chapter 9 lists out the conclusion of the thesis, giving a summary of the main

contributions, discussing the limitations and pointing towards future research

directions.

14

CHAPTER 2

LITERATURE SURVEY

This chapter presents a detailed review of literature on learner context modelling

emphasizing on the different definitions to context and parameters that constitute the

learner context model. Learner’s context is used for recommendation and adaptation

in e-learning. A detailed discussion about the major approaches to the design of

context aware e-learning recommender systems has been presented. The approaches

in the design of adaptation mechanism and the different elements of adaptation have

been also been discussed. The theoretical underpinnings of formal learning have been

presented with a focus on the Zone of Proximal Development, a socio-constructive

concept and its related concept namely scaffolding. The study on learning object

metadata standards and ontological representation of metadata along with the

discussion about the different works related to the computation of ontology based

semantic measures have been presented. Figure 2.1 consolidates the details about the

concepts that are related to this thesis about which the literature survey is carried out.

2.1 CONTEXT AWARE E-LEARNING

E-learning is an excellent candidate as a context aware application. E-learning

systems have risen above the “one-size-fits-all” approach, tailoring services according

to the needs of the individual learners and have changed the present day model of

learning such as “BYOD” (Song, 2014). Context aware e-learning facilitates the

provision of services adapted to an individual learner by understanding his/her context

unobtrusively that very much matches with the traditional class room teaching where

the teacher observes and understands the situation of students and teaches him/her

accordingly. Thus the augmentation of context awareness in technology enhanced

E-learning facilitates learners with appropriate resources as well as with the flexibility

that will imitate the real class room experience.

The basic component of context aware e-learning is the learner context. This section

explores the review of literature in terms of the definitions to context, classification of

context, elements of learner context and approaches to model context.

15

2.1.1 Definition of Context

Although a lot of research have been conducted on different issues related to context

aware systems, there is no agreeable universal definition of context, as the definition

of context, the core concept of any context aware application is solely dependent on

the objectives of the application and hence has an ad hoc definition across different

projects.

The term “context aware” has been first used by (Schilit and Theimer, 1994). In this

work, the location of the user, the entities such as people and object that surround the

user and changes to those objects have been considered in defining the user’s context.

The location element is considered the primary element of context and the latter as the

secondary element.

A formal approach to context definition has a conceptual viewpoint in which the

relationships and structure of contextual information is focused. Such a viewpoint has

been provided by (Schmidt et al., 1999) in which context is defined as “knowledge

about the user’s and IT device’s state”.

(Abowd et al., 1999) states that “Context is any information that can be used to

characterize the situation of an entity. An entity is a person, place, or object that is

considered relevant to the interaction between a user and an application, including the

user and the application themselves.”

(Brézillon and Pomerol, 1999) argue that context itself is contextual i.e. there is no

special type of information that can be objectively called context; rather advocates

that the knowledge that qualifies to be "contextual" depends on the context.

Based on the concept of relevancy, (Chen and Kotz, 2000) states that context is “the

set of environmental states and settings that either determines an application’s

behavior or in which an application event occurs and is interesting to the user”.

Dourish states that context is not a set of stable features characterizing events rather it

is an emergent property of interaction of people with the environment (Dourish,

2004).

16

Figure 2.1 Overview of the Literature Survey Topics

Literature Survey

Context Aware E-learning

Defintion of Context

Classification of Learner Context

Parameters of Learner Context

Representation of Context

Context aware Elearning Applications

Context Aware Recommendation

Content Based Filtering

Collaborative Filtering

Hybrid Filtering

Context Aware Adaptation

Elements of Adaptation

Levels of Adaptation

Adaptation approaches

Learning Theories

Zone of Proximal Development

Scaffolding

Semantic Measures

LO Metadata Standards

Semantic Relatedness Measures

17

This can be further supplemented by Dey’s (Dey, 2001) definition of context wherein

he quotes that apart from considering the entities that are relevant to the interaction

between the user and the applications as context, the user himself has to be considered

as a part of context.

(Maalej, 2010) defines context as the “set of all events and information, which can be

observed or interpreted in the course of the work, except those events and pieces of

information that constitute the change (i.e. the main output of the work)”.

2.1.2 Classification of Context

A learner’s context can be well understood by a set of elements that define the

learning situation and such elements play a role in achieving the learning objectives or

in maximizing learner satisfaction. Study of literature reveals different aspects that

have been concentrated in handling context in e-learning applications. Research on

context aware e-learning systems focus on the selection of elements that define the

learner context, on context management techniques, involving acquisition,

representation and reasoning and application of context information. With respect to

the deployment of e-learning applications, modern e-learning can be classified as

ubiquitous learning, mobile learning, web based e-learning and computer aided

instruction (Cheng et al., 2005). A context aware adaptive e-learning system,

accordingly assumes different contextual parameters. The learner’s context can be

classified in varied perspectives as illustrated in Figure 2.2. In this thesis, the focus is

on the web based e-learning. The following sub sections discuss the various

perspectives in context classifications.

2.1.2.1 Context Classification in Application Point of View

In this perspective of context classification, context is classified as low-level context

and high-level context based on how the application processes the contextual

information. The low-level context is the raw contextual information which is directly

obtained from the sensor be it hardware or software. This low-level context

information is subjected to preprocessing, fusing and reasoning in order to build the

higher level context which is taken by the middleware (Capra et al., 2003; Gu et al.,

2005; Martin et al., 2009).

18

2.1.2.2 Context Classification in Acquisition Point of View

Based on the modes of acquisition, the factors determining the learner context can be

classified as

Direct vs. Indirect Context

Physical vs. Virtual Context

Direct vs. Indirect Context

Context processing is a knowledge intensive task of varying complexity. If the data

acquired from hardware sensor or from the software listeners is directly used as a

contextual clue, it is classified as direct context. On the other hand, if the application

engages a mapping process to map the cues into a new context, then the acquired

context is termed as an indirect context. An ontology based approach combined with

logical reasoning (Bettini et al., 2010) or a probabilistic approach like variants of

Bayesian networks (Gu et al., 2004) can be used to handle the challenges of the

mapping process.

Physical vs. Virtual Context

This classification of context is obtained by characterizing the elements of context by

their tangibility. Those elements of the environment whose presence can be sensed are

classified under physical context such as the objects, devices, places that surround a

user. Those elements viz. user’s interests (Rakotonirainy and Lehman, 2004) etc.

whose values are computed and not directly measured from physical entities is

defined as virtual context.

2.1.2.3 Context Classification in Temporal Point of View

The temporal perspective of context classifies context as static and dynamic context

based on the nature of the changeability of contextual information (Choi et al., 2012)

(Henricksen and Indulska, 2004). The elements of context whose value does not

change across the learner’s session are characterized as static contextual information.

The dynamic contextual information is the value of those elements of context that

change with time across learner’s session.

19

Figure 2.2 Classification of Context

2.1.2.4 Context Classification by Example

This classification of context is defined by listing the elements that make up the

learner context. The major contextual elements considered in context aware e-learning

are spatio-temporal context that include the time of learning and location of learning,

environmental context that captures users surroundings, such as things, services, light,

people, and information accessed by the user, the learner information context that

includes activity context user’s tasks, goals, activities, etc., social context, and

personal context incorporating mental and physical information about the user, such

as mood, expertise, disabilities, height and weight as illustrated in Figure 2.3. Other

secondary contexts are examples of digital, world and physical context (Derntl and

Hummel, 2005).

In this thesis, the learner’s profiled context, sensed context viz. the interaction

context, the profiled digital context and the computed context of the learner have been

used in consolidating learner context.

Context

Application View Low level Vs. High level

Acquisition View

Direct Vs. Indirect

Physical Vs. Virtual

Temporal View Static Vs Dynamic

Example View

Computing

User

Physical

20

Figure 2.3 Taxonomy of Context by Example

2.1.3 Parameters of Learner Context

The contextual parameters defined and used in the literature can be classified as

learning context parameters, mobile context parameters (Siadaty et al., 2008) and

hybrid context parameters based on the design and deployment of the e-learning

application.

2.1.3.1 Learning Context Parameters

The learning context parameters are defined by the static and dynamic attributes

pertaining to the learners, the educational resources provided and the activities of the

learners corresponding to the pedagogical strategy. The work carried out in this line of

research is presented in this section.

Three levels of contextualization are considered viz. learning process level,

organization level and individual learner level in (Nabeth et al., 2004). These

multilevel contexts are utilized in their project ‘Learning In Process’ to provide

highly contextualized e-learning experience for the adult learners of knowledge

intensive organizations. The learner context includes previous knowledge, goals,

interactions and preferences. Organizational context is defined using organizational

unit, roles, business process and task. The technical context is formulated using the

Context

Environmental

People

Light, sound, noise

Services

Devices

Personal /Identity

Mental factors

Physical factors

Activity

Goals

Tasks

Social

Acquaintance

Spatio-Temporal

Time

Date

Time of day

Season

Space

location

Orientation

Speed

21

characteristics of learner’s platform such as operating system, browser, plugins and

network bandwidth.

(Baylari and Montazer, 2009) proposed an agent based architecture for context aware

e-learning. In this work the learner’s activities have been considered in order to

determine the learner’s context. The systems makes use of the item response theory

and artificial neural network to recommend learner specific resources.

In (Tankeleviciene and Damasevicius, 2009), the conceptual model of the learning

situation known as the Learning Context Model (LCM) is built using the

technological, pedagogical, psychological aspects to implement an adaptive,

personalized learning environment.

In (Yaghmaie and Bahreininejad, 2011) the focus is on context management. A

multi-agent system forms the basis of context management. The Sharable Content

Object Reference Model (SCORM) standard and semantic Web ontology is used for

learning content description, storage, sequencing and adaptation.

2.1.3.2 Mobile Context Parameters

The mobile context is applicable to ubiquitous learning enabling anywhere, anytime,

any device learning where the learner’s context is captured mainly with regard to the

location, physical environment and the delivery medium aka the device context.

In (Derntl and Hummel, 2005), a hierarchical schema defining the learning context

has been proposed which includes the physical context that describes the learning

resources (attributes like resource identifier, category, name, and capabilities), people

(attributes like identification, name, and role), digital context (e-books, e-papers,

simulators, and Web-based learning services), device context (hardware, the software,

and the network connectivity of the e-learning device) and learner information context

(name, expertise, or interests, learning status, team membership).

In (Yang, 2006), attempt has been made to find the right collaborator, right

information and right learning services in the right place at the right time in order to

provide context aware content access for learners with portable devices.

22

A context model has been proposed (Gómez et al., 2009) to structure the semantics of

contextual relations and concepts in various contexts, related to device, place, time

and physical environment.

The aspects of mobility of devices such as Global Positioning System (GPS)

receivers, motion sensors, etc. have been used to characterize the context of the

learner in the development of a middleware for m-learning applications by

(Martin et al., 2009).

2.1.3.3 Hybrid Context Parameters

The hybrid approach to context modelling incorporates the parameters of learning

context and mobile context. This section presents the discussion about such works

available in literature.

The learner’s contextual profile built using the learner’s personal information,

preferences and his learning activities have been considered in (Martin et al., 2006).

This e-learning architecture is designed for supporting mobile learning.

(Yau and Joy, 2007) have proposed a layered architectural style including two

adaptation layers for context sensitive adaptation in e-learning wherein one is an

adaptable layer which considers the learner’s information context that includes the

static contextual information such as learner’s preference viz. learning style and

knowledge level and the other layer is an adaptive layer which considers the dynamic

contextual information wherein the location and time are considered contextual

elements.

(Das et al., 2010) have proposed a context aware e-learning system that is based on

the learner’s profile context such as personal information and expertise level,

infrastructure context which includes the network and device context and learning

context which includes the learning pace, state and comprehension level for dynamic

composition of relevant learning objects.

(Sudhana et al., 2013) has proposed a context aware architecture for e-learning

systems that follow the Model-View-Controller (MVC) design pattern. The learner’s

context is defined by the learning style, and the device characteristics parameters. The

learner’s context is used in the adaptation of the course structure and contents.

23

2.1.3.4 Learner Performance as a Parameter of Adaptation

In this thesis, a computed context parameter termed as learning efficiency is used as

the dynamic contextual parameter. The proposed learning efficiency parameter

measures the performance of the learner in a multifaceted dynamic way. Study of

literature reveals different formulations for measuring the learner’s performance. But

learner’s performance has been used as an assessment parameter and but not as a

contextual cue to adaptation.

The parameters taken into account in the literature, to evaluate the learner

performance are, the standard knowledge test score, time taken to complete a module

or a course, ability, motivation etc. This section explores the different work reported

in formal and informal learning setup, for computing the resultant performance of the

learner which have been used as a factor of adaptation.

The standard model to evaluate the performance of the learner has been proposed by

Carroll termed as the school model (Carroll, 1993) in which the learner’s performance

is measured in terms of Learning Rate (LR). LR is computed as a function of time

and has been given in Eq. 2.1.

)___

__(

learntoneededtime

learningspenttimefLR (2.1)

(Johnston and Aldridge, 1985) has proposed Learning Achievement (LA) as a

measure of performance of learners and has defined it as an exponential function of

learner’s ability, motivation and learning time as given in Eq. 2.2

)1(100)

0( ttk

eLA

(2.2)

where k is the function of learner’s ability and motivation and t is the actual learning

time and t0 represents the prior learning time.

A variant of LA has been proposed in (Liu and Yang, 2005) by eliminating the

quantification of motivation which is given in Eq. 2.3.

)1(at

eMLA

(2.3)

24

where M is the maximum learning achievement and a represents the student’s

learning ability and t the learning time.

A seminal work on computation of learning efficiency is that of (Fletcher, 1971) who

defines learning efficiency in perspectives of learner’s performance in the test and the

learning efficiency is given by Eq. 2.4.

o

tLE (2.4)

where “t” is the number of test items answered correctly and “o” is the total number

of question items. The next section presents the discussion about the common

approaches to represent the context models.

2.1.4 Representation of Context

The context model has to be represented using appropriate representation techniques

in order to enable efficient usage of the context information. This section explores the

important techniques used in context representation. This thesis focuses on

determining the elements of context and not on the acquisition and representation of

context.

The prominent context representation techniques are

Key-Value Pairs (Schilit et al., 1994)

o The value of context information is supplied to the context

aware application as an environment variable.

Markup Scheme Models

o A hierarchical data structure consisting of markup tags

describing context with attributes and tags recursively defined

by other markup tags.

Graphical Models

o Provides a conceptual model of context helping the analysis and

specification of the context requirements of a context aware

application. Example is Context Modelling Language

(Henricksen and Indulska, 2004) based on Object Role

Modelling

25

Domain Focused Models

o Focuses on modelling the types of context information that

improves the functioning of context aware applications. One

such model is the W4 context model used for supporting

context aware browsing (Castelli et al., 2007)

Object Oriented Models

o The significant elements of object oriented approach viz.

encapsulation and reusability are employed in defining the

details of context processing

Logic based Models

o Logic based context models describe context in terms of

propositions, logical expressions and rules

Ontology based Models

o The different types of contextual parameters and their

interrelationships are expressed using ontology facilitating a

shared understanding across applications.

An efficient representation technique enables effective use of context information and

hence dependent on the objectives of the application.

The context information is used in the recommendation and adaptation of information

and services which has been elaborated in the following section.

2.2 CONTEXT AWARE E-LEARNING APPLICATIONS

Context aware e-learning systems use context information in two modalities. The

major treatment of contextual information is in the adaptive solution provisioning to

the learner. The secondary usage of the contextual information is in the

recommendation of suitable solutions to the learner. The former systems facilitate the

self-directed learner the needed focus and thereby evade learner disorientation and

frustration. The latter systems provide the self-directed learner, the liberty guaranteed

by e-learning yet helps him in orienting his/her learning process to reach his learning

objectives. The next section discusses the work related to context aware

recommendation.

26

2.2.1 Context Aware Recommendation

Recommender systems assist and augment the natural social process of

recommendation through analysis of data. E-learning recommender systems facilitate

a learner with directions (Zakrzewska, 2011) based on a group of learners sharing

similar features to improve personal competence development plans. The generated

recommendation depends on the context of use of the e-learning environment (Ghauth

and Abdullah, 2010) (Liang et al., 2006). The different learner contextual cues are

cognitive traits like learning style dimensions, learner information etc. and the e-

learning components suggested are support, learning objects (Jie, 2004), (Krištofič,

2005), learning sequence (Klasnja-Milićević et al. 2011). There are three approaches

to recommendation generation.

2.2.1.1 Content based Filtering Approach to Recommendation

Content based filtering approach to recommendation matches the learner’s preference

with the features of the available learning resources. Recommendations are generated

by analyzing the relationship between the ratings associated with the learning

resource and its associated features. The efficiency of recommendation in this

approach depends upon the scale of learner’s learning history.

(Kerkiri et al., 2007) proposed a framework that exploits both description and

reputation metadata to recommend personalized learning resources. Their experiment

proves that the use of reputation metadata augments learner's satisfaction by retrieving

those learning materials which are evaluated positively by the learner.

(Liu and Aberer, 2013) has proposed a content based collaborative approach to

recommendation by combining the learner ratings and the learner context information

for recommending learning items. In order to ameliorate the prediction of the learner

preference the matrix factorization technique is combined with a social regularization

factor.

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2.2.1.2 Collaborative based Filtering Approach to Recommendation

The collaborative filtering approach to recommendations is promising wherein the

recommendation for a learner is generated based on the opinion provided by other

learners. A group of similar learners are formed through the aid of statistical

techniques. The popular approaches to evaluate the similarity between learners are the

correlation based approach and cosine based approach. The correlation based

approach calculate similarity score between two learners x,y as shown in Eq. 2.5.

xy xy

xy

Ss Ssysyxsx

Ssysyxsx

rrrr

rrrryxsim

2

,

2

,

,,

)()(

))((),(

(2.5)

The learner similarity can be calculated by cosine based approach as given in Eq. 2.6.

xyxy

xy

Sssy

Sssx

Sssysx

rr

rrYXyxsim

2

,

2

,

,,

),cos(),( (2.6)

The collaborative filtering algorithm is defined using three important steps;

representation, neighborhood formation and recommendation generation wherein the

Top-n learning resources are recommended from the neighborhood of learners. The

efficiency of this category of recommendation is affected by the dynamicity of

learning resources management.

(Chen et al., 2005) has used the concept of item response theory for collaborative

filtering approach for recommending course materials based on the estimate of

learner’s ability and the complexity of the learning resource with the objective of

accelerating the learning efficiency and effectiveness.

(Tang and McCalla, 2005) has proposed an evolving web-based learning system that

is able to find relevant content on the web, personalize and adapt the content based on

the system's observation of its learners and the accumulated ratings given by the

learners, without the need for learners to directly interact with the open web. The

learners are clustered according to their learning interest before using collaborative

filtering to calculate learners' similarities for content recommendation.

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(Soonthornphisaj et al., 2006) applied the collaborative filtering approach to predict

the most suitable documents for the learner. This is achieved by aggregating the

recommended materials from other e-learning web sites and predicting the more

suitable materials for learners. New learning materials are recommended to learners

with a high degree of accuracy using this approach.

(Liu and Shih, 2007) have designed a learning resources recommendation system

using association rule mining and collaborative filtering based on the learner’s

activity context for improving learning performance.

In (Yu et al., 2007) an ontology based approach to collaborative filtering

recommendation has been proposed incorporating the learner context, content context

and domain context to generate a personalized learning path.

(Tai et al., 2008) has proposed a hybrid method incorporating the Artificial Neural

Networks (ANN) and data mining techniques to recommend e-learning course. The

classification of similar learners has been done using ANN and the learning for the

recommended course is generated using the data mining approach.

To avoid the effects of cold start problem in recommendation (Zhang et al., 2014)

have proposed a collaborative filtering algorithm assimilating the user preference

model constructed using the features of the item domain and also by using the implicit

relationships between users.

2.2.1.3 Hybrid Approach to Recommendation

The hybrid approach to recommendation is a combination of recommendation

techniques with an objective of improving the accuracy of prediction combining the

salient features of the techniques and overcoming the disadvantage of a single

recommendation technique. In general hybrid recommendation strategies use

information retrieval methods in order to learn and maintain the learner profiles based

on content analysis. These learner profiles are used to estimate the group of similar

learners to offer collaborative recommendations. Hybrid approach combines the

advantages of both the approaches by recommending items when the items are either

scoring high against a user’s profile or rated highly by a user belonging to the similar

group.

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(Liang et al., 2006) have applied a knowledge discovery technique, and a combination

of content-based filtering and collaborative filtering algorithms to generate

personalized recommendations for a courseware selection module. Their experiment

shows that the algorithm used is able to reflect learners' interests with high efficiency.

(Khribi et al., 2009) have devised an online automatic recommendation system based

on learners' navigation histories. The web usage mining technique has been used to

recommend the relevant links by computing the similarities and dissimilarities of the

learner preferences and learning resource contents.

This section provides an insight into one of the usages of learner context in e-learning,

recommendation in e-learning.

The mere determination of learner context and its application is insufficient for an

enabling efficient e-learning experience. A strategy has to be devised in order to

determine the appropriate moment to trigger either the adaption or the

recommendation. This is essential because inaccurate judgment of adaptation and

incorrect frequency may lead to learner frustration. The next section discusses the

work related to context aware adaptation.

2.2.2 Context Aware Adaptation

The primary objective of context aware e-learning is to maximize learning outcome

through adaptation without learner intervention. The process of adaptation is

concerned about the elements to be adapted and the level of adaptation to be offered.

2.2.2.1 Elements of Adaptation

The different components of e-learning that are subjected to adaptation are with

respect to web based e-learning and mobile learning are

Presentation of Content

Learning Object

Learning Path / learning sequence

Support and Assessment

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Presentation of Content

A tremendous change has been experienced in the past two decades with respect to

the increase in the number of mobile devices with innumerable technological

characteristics that facilitate seamless access to information. Mobile devices facilitate

the learners with anytime, anywhere learning termed as m-learning. The capability of

the access device, software such as browser, the Internet connection technology and

speed, time, location and other physical conditions of the environment (Gómez et al.,

2009) have become the prominent elements of learner context in m-learning.

The major component of adaption in m-learning is the content aka presentation

adaptation of the learning resources. The content has to be adapted in milieu of the

aforementioned factors viz., the device capability, network speed etc. The aspects that

are to be considered for context aware content adaptation in e-learning are the

standards, specifications and content transcoding techniques.

Learning Object and Learning Sequence Adaptation

The learning object and learning sequence are components that have been the most

attended for adaptation. Normally, a rule-based approach is employed in order to

select the suitable learning objects that incorporate the building blocks of a learning

path (Karampiperis and Sampson, 2004).

Learning object adaptation includes the selection of the learning object sensitive to the

location context (Goh et al., 2003; Gómez et al., 2013; Su et al., 2011; Syvanen et al.,

2005; Wang, 2004) in milieu of m-learning and learner information context (Sudhana

et al., 2013; Yau and Joy, 2007) in respect to web based learning.

Learning sequence adaptation aka learning path adaptation is yet another popular

component of adaptation. The learning sequence is the organization of learning

activities or arrangement of learning resources or learning objects according to the

learner’s objectives. The sequencing of instruction is significant because sequencing

influences the understanding and retention of the learner (Rumetshofer and Wob,

2003) and is affected by the learner’s individual cognition. A context aware

adaptation of learning sequence considers the learner information context viz.

learner’s objectives, performance etc. and the digital context for sequencing. The

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adaptation may be with respect to didactic linearity, complexity ranging from simple

to complex, learner’s goal, learner’s preference and interest.

Several works (Chen, 2008; Hwang et al., 2010; Shi et al., 2013) on learning path

adaptation have been reported. This thesis focuses on learning sequence generation

using semantic measures. A similar work can be found in (Sathiyamurthy et al., 2012)

wherein the LOs are assembled using the pedagogic granularity characteristics of

LOs. In (Garrido and Onaindia, 2013) , the strategy for assembling LO has been

devised in AI perspective considering the temporal and resource constraints.

Support and Assessment Adaptation

During the process of learning, dynamic support systems facilitate the learners to

reach their objectives smoothly. Every learning module is augmented with

assessments in order to evaluate the learner performance. A number of works have

been carried out, using the learner’s context defined in terms of the learner

information for the dynamic adaptation of the support systems and assessment

strategies in context aware e-learning (Al-Mekhlafi et al., 2009; Liu and Hwang,

2010; Yin et al., 2004). Support systems are termed as scaffolds in Instructional

science domain which has been elaborated in section 2.3.2.

2.2.2.2 Levels of Context Aware Adaptation

Adaptation in e-learning focuses on flexible e-learning solutions with the aim of

maximizing learner satisfaction, learning speed and learning effectiveness (Sampson

et al., 2005). Based on the level of learner’s interference, the degree of adaptation, in

context aware e-learning systems, can be classified as illustrated in Figure 2.4.

Figure 2.4 Levels of Adaptation in Context Aware E-learning

Context Aware Adaptation in

E-learning

Adapted Adaptable Adaptive

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Adapted E-learning Systems

An adapted e-learning system provides tailored solutions suitable to a specific

category of learners. Once a learner identifies himself associated to a particular

category, the solutions tailored for that category are provided to the learner. The

different categories are arrived based on the learner’s knowledge level viz. beginner

vs. expert, goal, ability, cognitive preference, learning style etc.

The attraction in this type of system is that learners belonging to a concrete category

are provided with suitable services.

The disadvantage in adapted systems is that the solutions to be offered to a specific

category of learners are hardwired at design time. Hence for a learner who is not able

to associate himself with any of these categories is still deprived of flexibility. The

other issue is that learning is a process that cannot always happen invariably. It is

affected by various factors and therefore d