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CHAPTER 4

LEARNING STYLES ASSESSMENT IN E-LEARNING

4.1 COMMON MODELS OF LEARNING STYLE

IDENTIFICATION

E-learning system is based on the common rules and features of the

learners to be engaged in the learning process. Therefore, the success of

e-learning environments is greatly influenced by the factors like learning

objects, content delivery, relevant information retrieval, performance

evaluation and the maintenance of the psychological level through

identification of the individual learning styles of the learners. This work

depicts the different learning styles which are available in the literature and

provides a comparative analysis among them. In addition, it suggests the use

of fuzzy logic for handling uncertainty in Felder–Silverman learning style

model.

A learning style is “a particular way in which an individual learns”

(Butler 1986). Numerous measures and instruments including questionnaires,

interviews and profile information have been used in the past to identify the

learning styles of the learners efficiently. These metrics are labeled as explicit

information provided to describe the characteristics of the learners during the

assessment procedure. Therefore, the main objective of this work is to analyze

the related works for identifying the individual learning style based on the

learners’ behavior. This will be helpful to provide a suitable e-content to the

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learners based on their learning styles in an e-learning environment. The

psychological level of the learners in an e-learning environment is greatly

attributed by the learning styles of the learners involved in learning. However,

in most of the existing e-learning frameworks, the psychological level

between the learners and e-learning contents is not well balanced. This is due

to the fact that the learning styles of the individuals vary from one person to

another and hence if the same kind of e-contents is provided to all the

learners, the success of the e-learning system degrades. Therefore, the

contents developed for the e-learning system could be modified based on the

learners learning styles in such a way that all the learners could be well

benefited so that the objective of the e-learning system could be satisfied.

Table 4.1 Learning styles Models – Metrics and Dimensions

Nature of Metrics

Inclusive Dimensions

Underlying Learning Theory

Learning Style Models Addressed

Static Personality Type Educational Specialization Professional career choice Job role Adaptive competencies Environmental factors Emotional factors Sociological needs Physical needs

Experiential theory model

Kolb Model

Behavioral theory model

Honey and Mufford Model

Cognitive theory model

Gregorc model

Psychological theory model

Felder-Silvermann model

Meta-learning theory model

Fleming VAK model

Dynamic Intelligence quotient factorsBiological factors Inherent interests

Personality model Carl and Myers Brigg indicator model

Intelligence theory model

Howard Gardner

Table 4.1 depicts the general metrics that are used for identifying

the learning style of a learner which includes the factors such as personality

types, early educational specialization, professional career choice, adaptive

competencies and gender (Boyatzis and Kolb 1997). When considering the

above metrics, the learners can be identified as belonging to any one of the

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styles of learning (Butler 1986). However, these existing metrics are suitable

only in identifying the learning styles based on questionnaires, interviews and

profile information. However, the results obtained through these measures

may not be accurate in the current e-learning scenario. Since, the learner is

independent of a tutor in e-learning, the learning style is absolutely vague in

nature. Hence, a deep understanding of the learners is necessary in predicting

their learning style in E learning environment.

A deep understanding of one’s own way of learning can lead to a

great personal empowerment and self confidence. This kind of deep

understanding can be known by analyzing the behavior of the learners

involved in an e-learning environment. One of the techniques employed for

identifying the behavior of the learners in e-learning is their web browsing

pattern since clear and deep identification of the learning style of the learner

through the browsing pattern increases the measures for restructuring the

design of an e-learning content (Melody Siadaty, Fattaneh Taghiyareh 2007).

This work focuses on the analysis of learning styles of different models and to

enhance Sanders et al model to handle uncertainty present in their model

(Sanders and Bergasa-Suso 2010). According to Sanders et al model, learners

are classified into three styles namely active, reflective and unknown.

However, the unknown category can be further investigated so that a suitable

content can be provided to these categories of learners as well. In this work,

fuzzy logic based learning style model is proposed that extends the

classification provided by Sanders et al in which the learners are classified

efficiently into active, medium active, medium reflective, reflective. For this

purpose, Gaussian membership function is suggested in this work for

effective classification of learners in an e-learning environment.

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The major contributions of this work are the provision of

1. Ability to predict the learning styles based on the web usage

information and also on their original profile information,

2. The proposed model makes use of fuzzy membership

functions. Therefore, precise reasoning in assessing the

learners based on their learning styles is achieved and

3. It proves a facility to assess the learners accurately based on

their learning styles. For this purpose, the learners are

classified into four categories namely active, medium active,

medium reflective and reflective using fuzzy rules.

4.2 INFERENCES OF LEARNING STYLES IN EDUCATIONAL

SYSTEMS

The past literatures in the area of learning styles identification had a

great impact on the static behavior of the learners. There were some hidden

traits present within the learners which are not usually considered for learning

styles assessments. Most of the learning styles discussed in the literature

assess the learning style of a particular individual based on the profile

information given by the learners themselves. On further analysis, it was

identified that the explicit information given by the learners are alone not

enough in identifying the learning style correctly. In the proposed work,

greater importance is given to the hidden nature of the learners which could

aid in assessing the learning style of an individual efficiently.

The next category of learning style prediction systems could

effectively make use of internet education systems and are very suitable to

many courses in the internet. However, these systems fail to consider the

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learners capacity or provide any intelligent advice on potential websites.

WebCT is the first internet educational system that could provide learning

course materials through web but cannot provide any intelligent technique

which could consider the learners capacity (Sanders 1993). The next suite of

internet educational systems like INSPIRE (Papanikolaou et al 2002),

ARTHUR (Gilbert and Han 1999), AES-CS (Triantafillou et al 2002),

Tangow (Paredes and Rodriguez 2002), could predict the learning styles of

the learners effectively, but content adaptation was made based on previous

knowledge, but these systems needed to assess the learners learning styles

offline and questionnaires evaluation.

The system developed by Bergasa (Bergasa-Suso et al 2005) called

AHA which is a set of tools termed iLessons that overcame the difficulties of

the earliest systems. The model proposed by him considered Felder Silverman

learning style preferences as a base model to categorize the learners into any

one of the four dimensions namely active/reflective, sensing/intuitive,

visual/verbal, and sequential/global. Therefore, iLessons is an important

contribution for e-Learning by identifying the learners as either active or

reflective for the first dimension. This model was satisfactory when the

learners tend towards a particular dimension most of the time. However, this

scenario is not true always. This condition was rectified subsequently, by the

model developed by Sanders and Bergasa-Suso (2010) known as new

intelligent system to categorize the learners into any one of the four

dimensions described in Felder Silverman learning style model which had an

effective user interface. According to this system, the learners were classified

into three categories for the first dimension (active/reflective) as active,

reflective, or unknown. The accuracy of classifying the learners in Sanders

and Bergasa-Suso (2010) has increased to 81% compared to the earlier work

done by Bergasa-Suso et al (2005). However, this model has to be enhanced

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further by resolving the unknown category of the learners in order to improve

the classification accuracy. This kind of uncertainty in the inference found in

the unknown category can be handled by introducing fuzzy rules for effective

classification of the learners learning through web environments.

From the analysis made, there were many internet educational

systems which could effectively predict the learning styles through offline or

questionnaires or through online activity information. In spite of good

contributions offered by various researchers in predicting the learners learning

styles through web usage information, it will be worthier if some machine

learning algorithms are used for effective classification. Queries based on the

degree of activeness or reflectiveness is not addressed in any of the existing

recent models for accurate prediction of the learners. Hence, it is necessary to

provide a rule-based approach like neural networks or fuzzy logic for

effective classification of the learners’ learning through web environments

(Shyi-Ming Chen and Yu-Chuan Chang 2011). The model proposed in this

thesis makes use of fuzzy membership functions which helps in identifying

the degree of activeness or reflectiveness and also categorizes the learners in

the unknown category of the existing models into active, medium active,

medium reflective and reflective types of learners.

4.3 FUZZY LOGIC BASED LEARNING STYLE PREDICTION

IN E-LEARNING

In this work, a new learning style identification system based on

fuzzy logic has been proposed and the subsystem is shown in Figure 4.1. The

proposed model is based on Felder Silverman learning style model that relies

on psychological theory. The model is tested for the learners who are learning

‘C’ programming language through e-learning environments. The proposed

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system uses the learners’ web interface information and Felder Silverman

learning style preferences as inputs.

Figure 4.1 Learning Style Identification Subsystem

The major features of this proposed model are the introduction of

metrics analyzer and fuzzy inference engine for effective learning style

identification.

4.3.1 Prediction System Input Setting

The proposed model uses mediawiki e-Learning servers for the

e-Learning contents to be posted in various formats. This e-learning server

consists of C programming language course contents in textual, audio, and

video formats. The learner after proper authentication could access any type

of contents that are available in mediawiki e-Learning server. The target

learners in this proposed model tend towards textual format of the course

contents, and therefore this model was evaluated and tested for identifying the

Felder Silverman Learning Styles

Models

Learner’s Web Interface

Information

Learning Style Identification Subsystem

Fuzzy Inference Engine

Metrics Analyzer

Metrics Meta Data Student Profile

information

Rule Base

Media Wiki E-Learning Server

Content

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first dimension of Felder Silverman learning style preferences. The learners’

during the authentication are asked to provide their original profile

information for the purpose of learning style identification. Table 4.2 depicts

the characteristics of the earlier learning style models that are used in various

application domains.

Table 4.2 Parameters of Learners Web Interface

Parameters List Parameters Included

Deadband model

Proposed Fuzzy Logic based model

Number of mouse movement in the y-axis

Yes Yes

Ratio of document length to the time spent on a page

Yes Yes

Ratio of images area to document length and scroll distance

Yes Yes

Number of visits to a document No Yes

Fuzzy Rules No Yes

Therefore, the learners are accurately classified based on their

learning styles using their own profile information and their online web usage

activity. To facilitate the experimental evaluation, a rule base has been

constructed which is fully loaded with input and output fuzzy rules. The

learners’ activities were carefully monitored and recorded for the Learning

Styles prediction. These activities were recorded for analysis with respect to

the parameters described in Table 4.2. According to the evaluation, the

learners were classified into four kinds of learning styles namely active,

medium active, medium reflective, and reflective.

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4.3.2 Overview of Felder-Silverman Learning Style Model

This learning style model is often used in technology-enhanced

learning. This learning style model is usually preferred in web-based learning

environment. The model is very useful because of the four dimensions for

learning style preferences which considers the technological aspects of the

e-learning learners. The four dimensions for categorizing the learners are as

follows:

A. Active/Reflective: Active learners are active by themselves and their user

activity across the web environments are very fast, whereas the reflective

learners slowly think about concepts and the user activity across the web

environments will be usually very slow.

B. Sensing/Intuitive: Sensing learners try to learn the underlying facts

whereas intuitive learners prefer to identify relationships existing among the

concepts.

C. Visual/Verbal: Visual learners prefer to learn through pictures, flow

charts, and cartoons whereas verbal learners tend towards learning through

textual representations

D. Sequential/Global: Sequential learners learn through incremental learning

whereas global learners learn at a single scratch of time. The proposed model

concentrates on the first dimension of active/reflective since the learners

learning through e-learning environments usually fall in either of the

categories mentioned.

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4.3.3 Metric Analyzer

The Learning style prediction of the proposed model is analyzed

using various metrics described in Table 4.3. These metrics includes both the

learners online web activity and some of their original profile information.

Using the information from Table 4.3, the metric analyzer present in the

proposed system analyzes the learners’ behavior and aids in classifying the

learners with respect to their learning styles.

Table 4.3 Metrics for Learning Styles Prediction

Online Web Information Offline Profile InformationNumber of mouse movement in the y-axis Domain of Interest Ratio of document length to the time spent on a page

Educational Background

Ratio of images area to document length and scroll distance

Professional Career

Number of visits to a document

4.3.4 Rule Base

A rule based approach is proposed in this work for the effective

classification of learners which can handle uncertain information as well is

used. Moreover, this model aids in recommending and providing suitable e-

learning materials based on the identification of learning styles. The

knowledge editor rule base consists of approximately 30 fuzzy rules for the

prediction of learners based on their learning styles using online web activity

information and offline profile information. The set of rules used in this

proposed work are shown in Table 4.4.

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Table 4.4 Fuzzy Rules in the Proposed System

Rule No Fuzzy Rules

1 if(mousemove is more) and (doclentime is more) and (imagedoclen is more) and (visitsdoc is more) then (student is reflective)

2 if(mousemove is less) and (doclentime is lesss) and (imagedoclen is less) and (visitsdoc is less) then (student is active)

3 if(mousemove is less) and (doclentime is less) and (imagedoclen is less) and (visitsdoc is more) then (student is mactive)

4 if(mousemove is less) and (doclentime is less) and (imagedoclen is more) and (visitsdoc is less) then (student is mactive)

5 if(mousemove is less) and (doclentime is more) and (imagedoclen is less) and (visitsdoc is less) then (student is mactive)

6 if(mousemove is more) and (doclentime is less) and (imagedoclen is less) and (visitsdoc is less) then (student is mactive)

7 if(mousemove is more) and (doclentime is more) and (imagedoclen is more) and (visitsdoc is less) then (student is mreflective)

8 if(mousemove is more) and (doclentime is more) and (imagedoclen is less) and (visitsdoc is more) then (student is mreflective)

9 if(mousemove is more) and (doclentime is less) and (imagedoclen is more) and (visitsdoc is more) then (student is mreflective)

10 if(mousemove is less) and (doclentime is more) and (imagedoclen is more) and (visitsdoc is more) then (student is mreflective)

11 if(mousemove is less) and (doclentime is less) then (student is active)

12 if (imagedoclen is more) and (visitsdoc is more) then (student is active)

13 if(mousemove is more) and (doclentime is more) then (student is reflective)

14 if(imagedoclen is more) and (visitsdoc is more) then (student is reflective)

15 if(mousemove is less) and (doclentime is more) then (student is mreflective)

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Table 4.4 (Continued)

Rule No Fuzzy Rules

16 if(mousemove is more) and (doclentime is less) then (student is rmactive)

17 if(imagedoclen is less) and (visitsdoc is more) then (student is mactive)

18 if(imagedoclen is more) and (visitsdoc is less) then (student is mreflective)

19 if(mousemove is less) and (doclentime is less) and (imagedoclen is more) and (visitsdoc is more) then (student is mreflective)

20 if(mousemove is more) and (doclentime is less) and (imagedoclen is less) and (visitsdoc is more) then (student is mreflective)

21 if(mousemove is more) and (doclentime is more) and (imagedoclen is less) and (visitsdoc is less) then (student is mactive)

22 if(mousemove is less) and (doclentime is more) and (imagedoclen is more) and (visitsdoc is less) then (student is mactive)

23 if(mousemove is less) and (visitsdoc is less) then (student is active)

24 if(mousemove is less) and (visitsdoc is more) then (student is mreflective)

25 if(mousemove is more) and (visitsdoc is less) then (student is mactive)

26 if(mousemove is more) and (visitsdoc is more) then (student is reflective)

27 if (doclentime is less) and (imagedoclen is less) then (student is mreflective)

28 if(doclentime is more) and (imagedoclen is more) then (student is mactive)

29 if(doclentime is more) and (imagedoclen is less) then (student is active)

30 if(doclentime is less) and (imagedoclen is more) then (student is reflective)

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4.3.5 Fuzzy Inference Engine

The learners membership degrees for active/reflective type of

dimensions were studied using Engineering students of Anna University. On

successfully providing the e-learning course materials of ‘C’ programming

language, the symmetric Gaussian membership function for a fuzzy set A,

that represents the learning styles of the learners is represented by,

) = ( ) (4.1)

The parameters (width) and c (center) alter the width of the

membership function curve of the fuzzy set A, based on the input value x.

This fuzzy set A represents the learning style, and the parameter c denotes the

mean of the membership function curve in this function

(Shyi-Ming Chen and Yu-Chuan Chang 2011). The symmetric Gaussian

fuzzy membership function used in the proposed model for the four categories

of the learners namely active, medium active, medium reflective and

reflective is shown in Figure 4.2.

Figure 4.2 Symmetric Gaussian Fuzzy Function

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Based on the rule base developed in the proposed model, and on the

application of the symmetric Gaussian fuzzy membership function described

in equation (4.1), the learners could be predicted on their suitable learning

styles.

4.4 PERFORMANCE EVALUATION

The applicability of this model is tested in various online

e-learning environments. Based on the authors’ domain of interest, the

proposed model is tested for learners interested in learning ‘C’ programming

language course with varying learning styles. The main objective of the model

is to handle the uncertainty in the learners’ behavior and to classify them

accurately into four categories namely active, medium active, medium

reflective, and reflective as present in the first dimension of Felder Silverman

Learning Style model preferences. The described objective was tested and

compared with the other existing algorithms and the results are shown in this

section.

4.4.1 Experimental Set-Up

The learners during authentication procedure are solicited to

provide their original profile information including their age, gender,

educational background, domain of interest, professional career and hobbies.

Subsequently, the learners are provided with various ‘C’ programming

language course contents from any of the e-learning servers like mediawiki,

moodle, and joomla. However, these experiments were based on the

e-contents posted in mediawiki e-learning servers. The experiments were

carried out using 120 numbers of students of Anna University from various

branches namely Civil Engineering, Mechanical Engineering, Computer

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Science and Engineering, and Electronics and Communication Engineering.

The rule base included a set of 30 fuzzy rules for Learning Styles prediction.

In this work, the inference for Learning Styles was done using Matlab R2009a

7.8.0.347. The experiments were repeated, analyzed and compared with the

other existing models provided by Bergasa-Suso et al (2005), Sanders and

Bergasa-Suso (2010). The proposed model is also compared with the

traditional Bayesian Classification algorithm to test the significant difference

in the accuracy of the proposed model. In all the repeated experiments, the

Learning Styles of the learners were predicted based on their e-learning of ‘C’

programming language course contents. However, this work is an ongoing

work. The proposed work actually had its base from Felder Silverman

learning style model preferences for Learning Style prediction and this work

is limited to predicting the first dimension of learning styles only

(Active/Reflective).

4.4.2 Results and Discussion

The experimental evaluation results shown in Figure 4.3 are

obtained from the experiments conducted in this work on predicting the

learning styles of the learners through offline profile information and online

web activity information. The learners provide their complete information

through a web interface. Subsequent, they e-learn the ‘C’ programming

language course contents in the textual format available in mediawiki

e-learning servers. On successfully collecting and recording this information,

the metric analyzer analyzes for the available metrics to be used for learning

styles prediction.

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Figure 4.3 Three Dimensional Views of Learners X = Number of mouse movement in the y-axis

Y = Ratio of document length to the time spent on a page Z = Ratio of images area to document length and scroll distance

In this experiment, the metrics include both the profile information

and the online web activity information. With the help of these metrics, the

fuzzy inference engine classifies the learners into four categories namely

active, medium active, medium reflective and reflective. This fuzzy inference

engine makes use of a rule base fully loaded with 30 input and output fuzzy

rules. The evaluation of the proposed model was estimated in terms of

percentage of accuracy. Figure 4.3 shows the three dimensional view of the

different kinds of the categorized learners namely active, medium active,

medium reflective, and reflective. The X, Y, Z axis represents the first three

parameters described in Table 4.3.

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4.4.3 Inference

In this research work, Felder-Silverman learning style model is

chosen for learning style prediction especially in web learning environments.

Moreover, fuzzy rules derived from Gaussian membership functions are used

in this work to handle the uncertainty in Learning Styles prediction based on

the learners’ membership degree for active/reflective type of dimensions in

Felder–Silverman dimensions of learning style model. To facilitate this, the

learners’ web usage activities are recorded and some of the parameters in such

activities of the learners are considered for learning styles identification.

Table 4.5 shows the results obtained by applying various classification

algorithms used in learning styles identification. This experiment considered

120 learners for classification. From the table 4.5, it can be observed that the

proposed fuzzy logic based classifier provides accurate results when it is

compared with the existing techniques namely Bayesian, Bergasa and

Deadband. The graphical representation of the classified and unclassified

learners is shown in Figure 4.4.

Table 4.5 Classified and Unclassified Learners

Algorithms Number of Users (120)

Classified UnclassifiedBayesian 73 47

Bergasa 88 32

Deadband 94 26

Fuzzy logic 120 0

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Figure 4.4 Analysis of Classified Vs Unclassified Learners

The psychology of the learners may change periodically. Moreover,

the learners tend towards a particular dimension depending on circumstances,

mood and need. Therefore, tight coupling of learners to any of the two

dimensions namely active or reflective may not be complete in e-contents

recommendation to the learners. In such a scenario, the learners can be

loosely categorized as more active and less reflective known as medium

active and more reflective and less active known as medium reflective.

Therefore, this work aims at resolving this ‘unknown’ category to be

classified into any one of the four categories namely active, medium active,

medium reflective and reflective, thereby increasing the accuracy percentage

in predicting the learners based on their learning styles. The evaluation

measure called accuracy is defined as the percentage of classified users

belonging to a particular dimension. Table 4.6 shows the accuracy percentage

of the various algorithms present in the literature.

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Table 4.6 Accuracy of the classified Users

Algorithms Accuracy (%)

Active Reflective Medium Active

Medium Reflective

Bayesian 53 47 0 0Bergasa 61 39 0 0

Deadband 65 35 0 0Fuzzy logic 40 33 14 13

From the Table 4.6 the accuracy in classifying the learners is found

where the proposed fuzzy logic based algorithm identifies four categories of

learners namely active, medium active, medium reflective and reflective since

fuzzy membership functions are used for refined classification. However, the

other algorithms present in the literatures could identify only active and

medium type of learners. The accuracy percentage of the other algorithms in

comparison with the proposed algorithm is shown in Figure 4.5.

Figure 4.5 Learning Style Identification

0

20

40

60

80

100

Active Reflective Medium Active Medium Reflective

Learning Style Dimension

Learning Style Identification

Bayesian

Bergassa

Deadband

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Hence, the objective of obtaining fine accuracy in the Learning Styles prediction of the uncertain learners who are learning through web

environments has been achieved. The accuracy of the proposed fuzzylogic algorithm is validated using precision performance measure.

4.5 AGENT BASED PAIR PROGRAMMING

Several factors affect the performance in learning a programming course (White and Sivitanides 2002) and research is under progress to identify

why most of the students drop out from learning the programming courses. Learning a programming course is comparatively difficult than learning any

other course in e-learning since, programming knowledge requires cognitive abilities, logical thinking and mastering the abstract concepts (Van Gorp and

Grissom 2001). Therefore, this situation is a significant problem in the field of Computer Science and Engineering. There are several studies (Han et al 2010,

Palmieri 2002, Ueno 2000, Woods and Warren 1995, De Azevedo and Scalabrin 2005, Devedzic 2004, Sklar and Richards 2006, Fleming 2001,

Boyatzis and Kolb 1997, Cohen 1988, Martinez 2001, Myller et al 2002 and Chen et al 2000) which addresses these problems by providing many methods

of educational systems. Prior research works have shown that effective factors like improving self-efficacy and good metal effort can increase the

performance of the students, especially when learning a programming course (Vennila Ramalingam et al 2004).

Several factors influence the success of novice programmers namely

Prior Computing Experiences (Bunderson and Christensen 1995, Taylor and Mounfield 1994), e-learning, Computer Entertainment during Training

(Potosky 2002), Self-Efficacy (Byrne and Lyons 2002), Collaborative learning Environments (De Azevedo and Scalabrin 2005), Learning Styles

(Thomas et al 2002, Butler 1986) and Students Mental Effort (Soloway and Ehrlich 1984, Wiedenbeck et al 1999). Among the above mentioned factors,

providing a collaborated learning environment with increased self-efficacy of

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the students is the main focus of this work which leads to the success of the students in learning programming courses.

Pair programming is a term used in learning environments where two programmers work together at one workstation on the same design,

algorithm or code (McDowell et al 2006). Since, at least two people are involved, this is a form of collaborated learning environment. This kind of

collaboration is found beneficial to the learners especially when learning a programming course.

Self-efficacy is a key component in learning activities since

learning involves more than just acquiring skills. Bandura (1977) defines self-efficacy as people’s own judgment of their capabilities to organize and

execute course of actions required to achieve a specific goal. Educational researchers recognize that, because skills and self-beliefs are so intertwined,

one way of improving learners performance is to improve learners self-efficacy (Compeau and Higgins 1995). Attempts have also been made,

with some success, to increase self-efficacy in learning by peer modeling of tasks, verbal persuasion, or other types of social influences, such as

cooperative learning environments (Bandura 1977, Compeau and Higgins 1995, Canas et al 1994, Gist et al 1989). Persons who possess higher

self-efficacy belief show more effort and resistance for completing tasks and therefore have better and more effective task-fulfillment compared with

individuals who have weak self-efficacy (Gist et al 1989). However, increasing the self-efficacy by the above methodologies is found to be

inefficient for the learners learning through web. According to Bandura's theory, self-efficacy belief is under the influence of the following elements

namely personal experience which leads to success or failure, observing the behavior of the model, the substitute, or the example, vocal encouragement

and considering the physiological conditions

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In such a scenario, the self-efficacy of the learners can be increased

if they are allowed to judge their own abilities, before they could pair to learn

with appropriate agents to master a programming course. Moreover, as

analyzed before the students could outperform well in learning a

programming course, if they had prior programming experiences by learning

the basic concepts (Kinnunen and Malmi 2006). This is achieved, by allowing

the learners to self learn the e-contents from e-Learning servers based on their

choice of learning materials available in different formats namely documents,

audio and video lectures. This increases the level of self-efficacy because the

target learners acquire some basic knowledge about the programming course.

Considerable increase in the self-efficacy can make them to outperform well

in the subsequent learning with peer-learning agents in a pair programming

strategy (Han et al 2007).

4.6 AGENT BASED PAIR PROGRAMMING MODEL

The use of Internet in education has provided a new revolution

known as e-learning or web-based learning. In spite of all the important

contributions provided by various researchers in the area

e-learning described above, it will be useful if machine learning techniques

are used to further improve the self-efficacy of the students involved in

learning. Moreover, it is proved that the exchange of roles and the meaningful

feedback between the peer-learning agent and the learners have a positive

effect on learning (Han et al 2007 and Han et al 2010). Moreover, the

peer-learning agents enhance the effect of learning by determining the

learners’ level of understanding. However, increasing the self-efficacy of the

learners can still enhance the performance of the learners (Vennila

Ramalingam et al 2004). Prior literatures has suggested several factors of

increasing the self-efficacy of the learners in terms of providing previous

computing experiences through short lectures or self learning, computer

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entertainment during training, motivations, learning styles and assessing the

learners mental model during learning.

The first methodology of learning focuses on the recommendations

of suitable e-learning contents available in e-learning servers to self learn. This is

done in order to facilitate the learners with previous computing experiences to

understand the basic concepts in a particular programming course. Moreover,

suitable e-learning contents are provided based on identifying their learning

styles using Felder Silverman learning style model (Felder and Silverman

1988) as a base for identifying the learning styles. Subsequently, the learners

are paired with peer-learning agents for mastering the programming courses.

The second groups of learners have a different methodology of

learning where the learners learning through web environments did not have

any prior learning experiences. These groups of learners are directly paired

with peer-learning agents where the learners learn through pair programming

strategy. The learners and the agents alternate roles of the tutor and tutee in

order to learn a programming course.

The third methodology of learning is based on software agents

where the agent system is based on pedagogical learning. The agent in this

system assumes the instructor role and the learner assumes the tutee role. The

agent and the learners interact once in a while on the target e-course. After a

certain period of time, they are paired with peer-learning agents where the

learners and the agents learn using pair programming strategy. The agent and

the learner alternate roles of the tutor and tutee and learn a target course as

described earlier. The learners belonging to the different methodologies of

learning are assessed for their performances using posttest simple multiple

choice questionnaires. The proposed agent based model architecture is shown

in Figure 4.6.

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Figure 4.6 Agent Based Pair Programming Model

4.7 COMPONENTS OF AGENT BASED MODEL

4.7.1 Learning Style Identification

The students involved in e-learning develop high interest in

learning web contents posted in any e-learning servers. A wide variety of

e-learning servers are available like moodle, mediawiki and joomla. Media

wiki server was used for testing the proposed system because the user

interface is simple in nature. The learners are identified for their individual

Self-Learned Group

Self Learning using Recommended e-contents

Pair Programming Learning with Peer-Learning Agent

Comparative performance Evaluation using multiple-choice

questionnaire

Learning Style Identification

Peer Learned Group

Conversation Agent Learned

Group

Felder Silverman Learning Style

model Recommended

E-Contents

Recommendations and the methodology of

learning

PeerLearned Group

Conversation Agent Learned

Group

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learning styles based on Felder Silverman learning style model inventory

(Felder and Silverman 1988). According to the model, the four dimensions for

categorizing the learners are Active/Reflective, Sensing/Intuitive,

Visual/Verbal and Sequential/Global.

The target course of learning in this work is ‘C’ programming

language. The ‘C’ programming language contents are posted in various

formats like documents, audio files and video lectures. These contents are

made available in mediawiki e-learning server. The students are now analyzed

for their learning styles using the online usage activities of the learners

described in Section 4.3. Accordingly, the learners are classified into four

categories namely active, medium active, medium reflective and reflective.

In such a scenario, suitable recommendations are provided to the learners on

the choice of e-learning contents for self study based on their learning styles.

For instance, students with active learning style can be recommended to learn

the basic concepts of ‘C’ programming language available in three or more

documents, students with reflective learning style can be recommended to

learn concepts available in single document and students with either medium

active or medium reflective learning styles can be recommended to learn the

contents available in one or two documents and the corresponding hands-on

experiences.

4.7.2 Self-Learning

The learners during authentication procedure are solicited to

provide their original profile information including their age, gender,

educational background, domain of interest, professional career, and hobbies.

Subsequently, the learners are provided with various ‘C’ programming

language course contents from any of the e-learning servers like mediawiki,

moodle, and joomla. However, our experiments were based on the e-contents

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posted in mediawiki e-learning servers. The experiments were carried out

using 120 numbers of students of Anna University from various branches

namely Civil Engineering, Mechanical Engineering, Computer Science and

Engineering, and Electronics and Communication Engineering. The rule base

included a set of 30 fuzzy rules for Learning Styles prediction. The learners

are given 4 weeks of time in order to learn the basic concepts of ‘C’

programming language like datatypes, operators, functions and looping

statements. After 4 weeks of self–learning, the students gain some knowledge

on the basic concepts of ‘C’ language. This helps in improved self-efficacy in

order to master ‘C’ programming language course. The learners are tested for

their learning experiences, but however, this test is not treated to be

mandatory.

4.7.3 Learning with Peer-Learning Agents

The learners have some basic knowledge about the ‘C’

programming language course. This aids in increasing their self-efficacy,

since they have been given self learning experiences of learning contents

based on the identified learning styles. The peer-learning agents and the

learners switch between the roles of tutor and tutee. Since, the learners have

obtained the basic knowledge about ‘C’ programming language, they learn

with the peer-learning agents on advanced programming tested this proposed

system for teaching and learning ‘C’ programs like Armstrong number,

Factorial number and Fibonacci series. The learners initiate the system by

providing the logic of the program and the peer-learning agent helps the

students in writing the syntactically correct program. In this case, the learner

assumes the role of tutee and the agent assumes the role of a tutor. In the next

time, the peer-learning agents explain the logic of the code to the learners and

they teach the agents in writing the syntactically correct program. In this case,

the learners and agents assume the roles of tutor and tutee respectively. In this

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kind of mechanism, the learners and the agents alternate the roles of tutor and

tutee and master the ‘C’ programming language course. This teaching-

learning process is done for 4 weeks of time as given for self-learning. After 4

weeks of time, the learners are again assessed for their performances using a

test consisting of multiple-choice questions. Their performances are evaluated

for comparison.

4.7.4 Learning through Conversational Agents

This type of learning is the traditional pedagogical learning where

the agents assume the instructor role. This is an automated tutoring

application where the agent interacts with one learner at a time. This is a form

of collaborative learning environment where the agent teaches the learners on

the basic concepts of the target course and the learner interacts with the agents

occasionally. The learners progress is monitored once in a while by the agents

by posting simple queries and their returned feedbacks. These types of

learners learn through such conversational agents and then after a period of

time they are paired with peer-learning agents. Now, the learners learn

through Pair programming strategy as earlier. The progress of these learners is

also taken into account for comparing the group performances.

Incorporating self learning experiences in pair programming using

peer learning agents is helpful for enhancing the learning performance. In this

research work, the self-efficacy which is the belief on one’s own capabilities

is increased by providing prior-learning experiences. Moreover, the different

direct learning methodologies namely self-learning following by pair

programming, learning through peer-learning agents and pedagogical learning

assuming the instructor role are compared with the help of the post-test

questionnaires mean score values are considered in this work. The

self-learning group is assumed to have prior learning experiences. The other

two groups have either a direct interaction with peer-learning agents through

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pair programming strategy or pedagogical agent learning in the form of

instructor. The results of the experiments conducted in this research work are

shown in Tables 4.7.

Table 4.7 Groups Performances

Number of Users

Performance (Mean Score)

Self-Learning

Peer-Learning

Pedagogical Learning

15 24 17 14

30 37 18 14

45 47 24 16

60 49 34 19

75 52 37 23

90 55 42 27

105 59 44 31

120 60 46 34

4.8 SYSTEM EVALUATION

This study sought to determine whether the impact of giving self-

learning experiences by providing suitable e-learning contents based on their

learning styles was beneficial to the learners for subsequent learning with

peer-learning agents. The self-efficacy of the learners is judged by their

learning styles (Boyatzis and Kolb 1997). This efficacy is increased by

providing suitable e-contents to the learners for self learning based on their

learning styles. The system was evaluated using 129 learners from four

Engineering Departments of Anna University and this is given by the variable

‘N’ in Tables 4.8 and 4.9. All these learners were interested in learning ‘C’

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programming language course. Forty-three learners from the departments

were assigned to a group called prior-learned group, where these learners

were provided with self-learning e-contents recommended based on their

learning styles. The remaining 43 learners from the other departments were

assigned to a group called experimental group. The other 43 learners

belonging to different departments were assigned to a group called

pedagogical group and these learners learn through conversational agents

before they are paired with peer-learning agents.

The self-learned group had a self-learning experience through

recommended learning using e-contents posted in e-learning servers based on

their learning styles. The peer-learned group did not have self-learning

experience and had no choice of assessing their self-efficacy and learnt ‘C’

programming language directly using peer-learning agents. The performance

assessments of both the groups were tested for knowledge retention and

knowledge transfer, eight questions for each of the category. The pre test and

post test consisted of 50 questions (25 on retention and 25 on transfer) which

were reviewed for effectiveness by a professor of Anna University considered

as the domain expert in ‘C’ programming language course.

4.9 RESULTS AND DISCUSSIONS

4.9.1 Learning Styles Identification

The learners are varied in nature and same kind of e-content

delivery will not have any impact of subsequent learning. Their self-efficacy

is judged using their learning styles identification. It is a proven fact that

increasing the self-efficacy helps in creating a positive atmosphere in learning

which increases the performance of the students exponentially. The proposed

system aims at increasing the self-efficacy of the students by providing

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suitable ‘C’ programming e-contents available in documents, audio and video

lectures in mediawiki e-learning server. The learning styles are identified

using Felder Silverman learning style model using the learners’ web usage

activity. Subsequent to the identification of learning styles, they are

recommended to learn the e-contents available in e-learning servers based on

their learning styles. This increases the self-efficacy of the students

substantially which helps in mastering the ‘C’ programming course when

learning with peer-learning agents subsequently.

4.9.2 Pre-Test

The system conducted a pre-test for both self-learned group and

experimental group. At the initial stage, this test was conducted to see if the

first two groups were homogeneous in terms of the levels of achievement.

The test consisted of 50 questions (25 on knowledge retention and 25 on

knowledge transfer). The t-test results of the pre-test are shown in Table 4.8.

The results found in Table 4.8 indicate that the two groups had some

significant differences in their performances. But according to the results of

the pre-test on retention and transfer, there was not much of a difference

statistically difference between the self-learned group and the experimental

group and the difference is found to be greater than 0.05.

4.9.3 Post-Test

After 8 weeks of time for teaching and learning ‘C’ programming

language course using self-learning and peer-learning agent strategy, the

students are evaluated for their performances. The test consisted of

questionnaires as done earlier. Now, the groups are separated widely. The

self-learned group had pre-learning experience for 4 weeks of time using the

recommended e-contents available in e-Learning servers based on their

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learning styles. They underwent pair programming learning strategy with

peer-learning agents for another 4 weeks of time. However, the experimental

group learnt with the peer-learning agents for 8 weeks of time without having

a pre-learning experience. Both the groups are evaluated using a test of

50 questions (25 on knowledge on retention and 25 on transfer). The t-test

results of the post-test are shown in Table 4.9. According to the results shown

in Table 4.9, there was a large difference which is statistically significant

between the prior-learned and experimental groups and the values are found

to be less than 0.05. The statistical difference for Knowledge Retention

between prior-learned group and experimental group is 0.0035 and the

statistical difference for Knowledge Transfer between the same groups is

0.0006. Moreover, it is also evident from the table that, the mean scores of the

prior-learned group is greater than the experimental group. Learners who had

a self-learning experience using the recommended e-contents from

e-learning servers based on their learning styles had very high performance

results compared to the learners who directly learnt with the peer-learning

agents using a pair programming strategy. The results of Table 4.9 indicate

that increased self-efficacy of the students along with pair programming

strategy using peer-learning agents had a positive effect on knowledge

retention and transfer in ‘C’ programming language.

Table 4.8 Pre-test Evaluations on the Metrics of Knowledge Retention and Transfer using t-test

Metrics Group N Mean Standard Deviation

t-testvalues

Significant difference

Knowledge Retention

Self-Learned 43 56.50 25.60 1.9215 0.416

Peer-Learned 43 55.50 25.32

Knowledge Transfer

Self-Learned 43 50.80 16.07 0.7385 0.542

Peer-Learned 43 50.40 16.67

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Table 4.9 Post-test Evaluations on the Metrics of Knowledge Retention and Transfer using t-test

Metrics Name of the Groups N Mean Standard

Deviationt-testvalues

Significant difference

Knowledge Retention

Self-Learned 43 76.10 17.943.9489 0.0034

Peer-Learned 43 69.70 14.93

Knowledge Transfer

Self-Learned 43 88.30 5.775.1604 0.0006

Peer-Learned 43 66.40 12.95

Tables 4.8 and 4.9 shows the t-test results when the target groups

are two in number. In this case, the t-test evaluations are shown for

self-learned and peer-learned groups at a single instant of time. The final

integration testing is done using ANOVA test when all the target groups are

observed for performance levels. However, when all the three groups namely

self-learned, peer-learned and conversation agents learned groups have to be

compared ANOAV test evaluation methodology is used. Table 4.10 shows the

ANOVA test evaluation for the three groups for the post-test.

Table 4.10 Post-Test Group Evaluations using ANOVA-test Method

Metrics Name of the Groups N Mean Standard

Deviation Significant difference

Knowledge Retention

Self-Learned 43 70.256 10.045

0.0148Peer-Learned 43 68.116 9.223Conversational Agent-Learned 43 64.419 8.500

Knowledge Transfer

Self-Learned 43 71.488 11.042

0.0040Peer-Learned 43 68.116 9.223Conversational Agent-Learned 43 64.419 8.500

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4.9.4 Discussions

In this work, new methods to enhance the learners self-efficacy are

proposed in which the self-efficacy of the learner is judged by identifying

their learning styles. The self-efficacy of the individual learner is increased by

recommending suitable e-contents available in e-learning servers namely

documents, audio and video lectures based on their learning styles. This kind

of recommendation facilitates the learners in pre-learning experiences.

Subsequently, the learners learn with peer-learning agents using a pair

programming strategy. Learning with peer-learning agents is a form of

collaborative learning where the learner and the agent can alternate the roles

of a tutor and a tutee. In this case, pair programming is used as a

teaching-learning strategy. Hence, such exchange of ideas between the learner

and the agent can have a positive effect on teaching and learning. Moreover,

the learners have increased self-efficacy by having pre-learning experience

based on their choice of e-contents.

According to Cohen (1988), the effect size of retention was 0.38,

which is a medium effect size, whereas the effect size of transfer was 0.79,

which is significant. The experimental results obtained from this work

indicate that the effect size of transfer exceeded that of retention. The

proposed learning system is found to have a positive effect on teaching and

learning strategies. Moreover, it is evident from Table 4.6 that self learned

group did extremely well compared to the other groups namely peer-learned

and conversational agent learned, since the self learned group consisted of

learners who had self learning experience on the basic concepts of ‘C’

programming language using the recommended e-contents available in

e-learning servers based on their learning styles.