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ABSTRACT

 

Many Institutions offer on-line courses to people from all around the world. Learning
Management Systems provides a convenient learning environment for on-line courses.
Each learner has individual needs and characteristics such as, personality and learning
styles. These investigations are supported by learning theorists who argue that, these
differences affect individuals learning process, and that is why learners progress better
under certain circumstances. Learning style is the outlined method used to place learners
in certain criteria based on the way they process and perceive information.
Felder-Silverman’s learning style model is the implemented model because it combines
major learning style models like, Kolb’s, Pask, as well as the Myers-Briggs Type
Indicator.
In this thesis the adaptive learning framework is created using a Java Spark web
framework. MySQL database is used to store data and Java Database Connectivity is used
to interface Java with MySQL. Static and automatic student modelling are implemented.
Learners learning styles are inferred and the framework suggests, learning materials for
learners that corresponds to their learning preferences.

TABLE OF CONTENTS

 

Declaration of Authorship …………………………………………………………………i
Abstract……………………………………………………………………………………iii
Acknowledgements……………………………………………………………………….iv
List of Figures……………………………………………………………………………vii
List of Tables…………………………………………………………………………….viii
Chapter One: Introduction
1.1 Adaptive Learning Systems & Adaptive Learning Framework ………………….1
1.1.1 Learning management system……………………………………………1
1.2 Motivation & Problem Statement…………………………………………………2
1.3 Aims, Goals & Direction Of Research………………………………………….2
1.4 Thesis Structure …………………………………………………………………..4
Chapter Two: Literature Review
2.1 Learning Management Systems …………………………………………………6
2.1.1 Common Terms & Definitions Regarding Adaptive Educational
Systems………………………………………………………………..7
2.1.2 Student Modelling ………………………………………………………7
2.2 Student Modelling & Adaptation In Educational System……………………….7
2.3 Static Student Modelling & Automatic Student Modelling……………………..8
2.4 Common Learning Style Models………………………………………………..9
2.4.1 Examples of Adaptive Educational Systems………………………..11
2.4.1.1 CS383……………………………………………………11
2.4.1.2 IDEAL………………………………………………….12
2.4.1.3 MASPLANG……………………………………………12
2.4.1.4 LSAS…………………………………………………….13
2.4.1.5 INSPIRE………………………………………………..13
2..4.1.6 TANGOW………………………………………………..13
2.4.1.7 AHA!………………………………………………………………………….14
Chapter Three: Adaptive Learning Framework
3.1 TOOLS……………………………………………………………………….16
3.1.1 Database……………………………………………………………..16
3.2 Felder-Silverman Learning Style Model(FSLSM)…………………………..17
3.2.1 Index Of Learning Style Questionnaire……………………………..18
3.2.2 Learning Style Questionnaire Evaluation …………………………..18
3.3 Behavior Of Students In A Learning System Considering Learning Styles…19
3.3.1 Adaptive Learning Framework Working Scenario………………….22
3.4 Static & Automatic Learning Style Detection………………………………24
3.5 Features Of Adaptive Learning Framework…………………………………24
Chapter Four: Implementation
4.1 Http Server & MySQL Database……………………………………………32
4.2 Role Of Users……………………………………………………………….33
4.3 PROPOSED FRAMEWORK………………………………………………35
4.3.1 LEARNERS PROFILE……………………………………………36
4.4 General Features Of Adaptive Learning Management Systems……………38
Chapter Five: Conclusion
5.1 Summary & Contribution …………………………………………………40
5.2 Limitations………………………………………………………………41
5.3 Future Work……………………………………………………………..42
Appendix A………………………………………………………………………43
Bibliography……………………………………………………………………45

 

CHAPTER ONE

 

Introduction
This chapter gives general introduction to the Learning Management System (LMS),
Motivation and Problem statement. Research aim, goals and direction are also presented.
Later on, the structure of the thesis is described.
1.1 Adaptive Learning Systems & Adaptive Learning Framework
An Adaptive learning system is a platform for teaching, learning,managing courses, and
storing user data. In addition, it applies users data to adapt various visible aspects of the
system for the user. Adaptive learning systems tailor learners experience to suit individual
needs.
Adaptive learning frameworks provide an environment where adaptation and
personalization are achieved in order to improve the learning process. Basically, the
adaptive learning framework extends the benefits derived from traditional Learning
Management Systems (LMS), and incorporates the idea of offering learners personalized
support in a distance learning setting.
1.1.1 Learning management system
The Learning management system (LMS) is a platform for teaching, learning and
managing on-line courses. The core objective of a LMS is to improve usability and
learning. Many institutions make use of learning management systems to offer on-line
courses, thereby allowing people to enroll in courses irrespective of physical barriers.
Common examples of LMS are Moodle [Moo13], OpenOlat [Ope11] and Blackboard
[Bla13]. Although LMS’s provide immense support for teachers, they however are
limited in addressing learning differences in individuals. An important factor to consider
is to, extend the benefits derived from traditional LMS’s and incorporate the idea of
offering learners personalized support in a distance learning setting.
1.2 Motivation & Problem Statement
The innovation of the internet led to advances in communication and technology. Many
educational institutions offer online-courses. E-learning has gained massive popularity
because learners from all around the world can participate irrespective of their physical
distance. Although LMS’s provide a convenient learning environment, LMS’s do not
address individual differences in learning. An important issue arises here as highlighted
by Jonassen [JG93], individuals differ in their ability to process information, construct
meaning from it, or apply it to new situations.
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The motivation behind this work is to improve learners learning process by, outlining
individual differences in terms of learning style, or preferences, and matching these with
suitable learning material.
The major drawback of learning management systems lies in its inability to personalize to
the system for learners [GL05]. Jonassen further stated that, it is possible to adapt
instructions in order to accommodate differences in ability, style, or preferences among
individuals to improve the learning outcome.
Therefore a conclusion can be drawn that, adaptive learning systems address exactly this
issue, matching learning contents with learner’s learning preferences.
1.3 Aims, Goals & Direction of Research
The aim of the project is to investigate methods of detecting learning styles and provide
learning material. This will involve, investigating the behavior of students while using the
system, and creating software programs that will implement their learning styles. Using
these programs, data is stored in student models, and analysis will be carried out on these
data. In summary the project aims are given below:
1. To understand and investigate the behavior of users in the system, infer student’s
learning style and the method used in detecting the learning style.
2. To write a program using suitable programming software that will provide a
suitable learning environment and infer student’s learning style, and suggest
learning material.
3. To discuss implementation of the framework.
In order to realize the goal of this thesis, investigations regarding the following research
questions have been conducted:
1. How can learning styles be identified?
In order to personalize the LMS, students learning styles need to be identified. Learning
style models are used as a basis for classifying learners based on learning styles. Various
learning styles models are discussed subsequently.
2. How can learning material be determined?
Based on the inferred learning style, learning materials are proposed that suit each
learning style. According to learning theorists, addressing individual differences in terms
of learning, improves learning progress.
In Figure 1.1, the learner accesses the ILS questionnaire from his/her computer or smart
device (such as a smart phone or tablet) through the predictive model. The predictive
model uses feedback from students to generate learning styles. During the learning
3
process, the current student data is passed to the predictive model that can help to build
the learners profile. The predictive model sends the learning styles to the dashboard and
the adaptive engine. The adaptive engine suggests learning contents to learners. Teachers
and administrators can monitor the learners’ performance through the dashboard and can
provide timely feedback to the learners through the intervention engine. Figure 1.1
displays the adaptive learning framework with the following components; adaptive
engine, intervention engine, dashboard and predictive model. In addition to students
information, the system also contains all the students data.
The role of every component is simply described as follows:
Predictive model: to evaluate students learning styles and learner profiles.
Adaptive engine: suggests learning contents based on generated learning styles.
Dashboard: allows teachers and administrators to monitor the students learning styles.
Intervention engine: allows instructors to send appropriate feedback to the learner based
on their observation of the learner’s information on the Dashboard.
Figure 1.1: Adaptive Learning Framework.
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1.4 Thesis Structure
This thesis is outlined in 5 chapters.
Chapter 1 introduces the Adaptive learning framework, learning management system,
motivation and problem statement are discussed, and research aims, goals and directions
are also outlined.
Chapter 2 deals with Literature Review. Common learning style models are described,
and examples of adaptive educational systems are discussed.
Chapter 3 defines the tools used in developing the adaptive learning framework, student’s
navigational behavior considering learning styles, and methods of detecting learning
styles. Proposed framework and working scenarios of Adaptive learning framework are
explained.
Chapter 4 Implementation and features of the system are discussed in detail.
Chapter 5 concludes the thesis by discussing major contributions and limitations. Future
work is also highlighted.

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