AN ADAPTIVE E-LEARNING SYSTEM BASED ON USERS' LEARNING STYLES

Author:

Phạm Quang Dũng

Outline

        Introduction Learning objects and Learning styles Ontologies and intelligent agents in education Incorporation of learning styles in a learning management system Automatic detection of learning styles in LMSs Conception of an adaptive e-learning system POLCA – Implementation and results Conclusion 2

Outline

       

Introduction

Learning objects and Learning styles Ontologies and intelligent agents in education Incorporation of learning styles in a learning management system Automatic detection of learning styles in LMSs Conception of an adaptive e-learning system POLCA – Implementation and results Conclusion 3

Introduction

Motivation and problem statement

    Each learner has his own individual needs and characteristics Most of LMSs do not consider learners’ needs and preferences  the need for providing learners with adaptive courses While adaptive systems support adaptivity, they support only few functions of web-enhanced education, and the content of courses is not available for reuse.

In contrast, LMSs focus on supporting teachers and help to make online teaching as easy as possible.

 use an adaptive learning management system

Introduction

Research issues

1. How can learning styles be identified?

 Find a literature-based method for automatic identifying learners’ learning styles   based on their behaviour and actions on learning objects in online courses using LMSs suitable for LMSs in general

2. How can adaptive courses be provided in LMSs?

  which types of learning objects their order

Outline

        Introduction

Learning objects and Learning styles

Ontologies and intelligent agents in education Incorporation of learning styles in a learning management system Automatic detection of learning styles in LMSs Conception of an adaptive e-learning system POLCA – Implementation and results Conclusion 6

Learning object?

  any digital resource that can be reused to support learning (D.A. Wiley, 2000)  digital images or photos, video or audio snippets, small bits of text, animations, a web page Characterstics     Share and reuse Digital Metadata-tagged  Description information: title, author, format, content description, instructional function Instructional and Target-Oriented

Learning style models

 To classify and characterise how students receive and process information.  Refer to fundamental aspects:   cognitive style learning strategy  Well-known models: Myers-Briggs, Kolb, Felder-Silverman

Learning style models

Felder–Silverman Learning Style Model

Each learner has a preference on each of the four dimensions:      

Active – Reflective

learning by doing – learning by thinking group work – work alone

Sensing – Intuitive

    concrete material – abstract material more practical – more innovative and creative patient / not patient with details standard procedures – challenges

Visual – Verbal

 learning from pictures – learning from words

Sequential – Global

  learn in linear steps – learn in large leaps good in using partial knowledge – need “big picture”

Learning style models - FSLSM (cont’)

Types of combination of LS dimensions

1. active/sensing/visual/sequential 2. active/sensing/visual/global 3. active/sensing/verbal/sequential 4. active/sensing/verbal/global 5. active/intuitive/visual/sequential 6. active/intuitive/visual/global 7. active/intuitive/verbal/sequential 8. active/intuitive/verbal/global 9. reflective/sensing/visual/sequential 10. reflective/sensing/visual/global 11. reflective/sensing/verbal/sequential 12. reflective/sensing/verbal/global 13. reflective/intuitive/visual/sequential 14. reflective/intuitive/visual/global 15. reflective/intuitive/verbal/sequential 16. reflective/intuitive/verbal/global 10

FSLSM (cont’)

 Index of Learning Style (ILS) questionnaire  44 questions, 11 for each LS dimensions  Scales of the dimensions: 11

A reductive questionnaire

      Based on FSLSM To be used for collecting initial learning style information of students Aims at saving time for students to answer Contains of 20 questions   some from the ILS questionnaire, the rest from us 5 questions for each LS dimension ACT 5 4 GLO SNS The questionnaire 3 2 Graphical presentation: VRB -5 -4 -3 -2 -1 1 0 1 2 3 4 5 VIS -1 -2 INT -3 -4 -5 REF SEQ

Implications of LSs in education

 make learners aware of their learning styles and show them their individual strengths and weaknesses  students can be supported by matching the teaching style with their learning styles

Outline

        Introduction Learning objects and Learning styles

Ontologies and intelligent agents in education

Incorporation of learning styles in a learning management system Automatic detection of learning styles in LMSs Conception of an adaptive e-learning system POLCA – Implementation and results Conclusion 14

Ontology in education

    Ontology represents domain knowledge by defining terminology, concepts, relations, and hierarchies Ex. of educational ontology: OntoEdu It enables education applications to share and reuse educational content Ontology is machine-readable and reasonable:   Suitable for description of learning objects It will be faster and more convenient to query and retrieval educational material

Intelligent agents in education

  how to provide adaptive teaching which is suitable to each student?

the use of Artificial Intelligence (AI) techniques such as Multi Agents or Agent Society-based architectures   intelligence may be applied through user models to make assumptions about the user’s state of knowledge, which may in turn help determine the user’s learning needs may enable the system to dynamically personalise applications and services to meet user preferences, goals and desires

Outline

        Introduction Learning objects and Learning styles Ontologies and intelligent agents in education

Incorporation of learning styles in a learning management system

Automatic detection of learning styles in LMSs Conception of an adaptive e-learning system POLCA – Implementation and results Conclusion 17

Introduction to LMSs

 Developed for teachers to create and manage their courses.

 Can be built based on pedagogical strategies: more learner-centered or more teacher-centered  The applied strategies focus mainly on how to teach learners from a general point of view, without considering the individual needs of learners.

18

Adaptivity in LMSs



Adaptivity indicates all kinds of automatic adaptation to individual learners’ needs.

  Course’s content Personal annotations 19

Benefits from using the Felder-Silverman learning style model in LMSs

  FSLSM describes learning style in more detail, represents also balanced preferences allows providing more accurate adaptivity   FSLSM considers learning styles as “flexibly stable” LSs might change over time. An adaptive system can adjust to the change.

  FSLSM considers learning styles as tendencies a student might act differently from his LS tendency. An adaptive system should consider also exceptions and extraordinary situations.

20

Behaviour of learners in LMSs with respect to learning styles

 Active/Reflective dimension   Active learners:   do exercise first then look at examples perform more self-assessment questions Reflective learners:    visit examples first then perform exercises spend more time on examples and outlines performed better on questions about interpreting predefined solutions 21

Behaviour of learners

Benefits

 Make teachers and course developers aware of the different needs, different ways of learning of their students.

 Should provide courses with many different learning materials that support different learning styles.

 Might present learning materials in different orders corresponding to different preference for LSs.

22

Providing adaptive courses in LMSs



Course elements



Adaptation features

23

Providing adaptive courses in LMSs

Course elements

 A course consists of several chapters, where for each chapter, adaptivity can be provided.

 Each chapter includes:     An outline Content objects  definitions, algorithms, graphics, etc.

Examples Self-assessment tests   Exercises A summary 24

Providing adaptive courses in LMSs

Adaptation features



Indicate how a course can change for students with different learning styles.



Include:

 the sequence of LOs and their positions.

 the number of presented examples and exercises 25

Adaptation features (cont’)

 For active learners:  outlines are only presented once before the content    objects the number of exercises is increased a small number of examples is presented self-assessment tests are presented at the beginning and at the end of a chapter  a final summary is provided in order to conclude the chapter 26

Adaptation features (cont’)

 For reflective learners:  the number of exercises and self-assessment tests is    decreased content objects are presented before examples outlines are additionally provided between the topics a conclusion is presented straight after all content objects 27

Methodology of incorporating LSs in a LMS

   Creating adaptive course   Course structure Learning objects with learning style properties  enough interchangeable LO?

Student modelling   A LS questionnaire for initialising An automatic approach for revising Providing adaptive course  Combination of selecting and ordering learning objects

Outline

        Introduction Learning objects and Learning styles Ontologies and intelligent agents in education Incorporation of learning styles in a learning management system

Automatic detection of learning styles in LMSs

Conception of an adaptive e-learning system POLCA – Implementation and results Conclusion 29

Problems with collaborative student modelling that use a questionnaire

 Uncertainty because of:   a lack of students’ motivation a lack of self-awareness about their learning preferences   the influence of expectations from others Questionnaires are static and describe the learning style of a student at a specific point of time  The result depends much on students’ mood

Benefits of using automatic student modelling

 does not require additional effort from students  is free of uncertainty  can be more fault-tolerant due to information gathering over a longer period of time  can recognise and update the change of students’ learning preferences

Automatic student modelling approaches

Determining relevant behaviour Selecting features and patterns Classifying the occurance of behaviour Defining patterns for each dimentions LMS database Inferring learning styles from behaviour Preparing input data Data-driven approach OR Literature-based approach Predicted learning style preferences

Automatic student modelling approaches

data-driven vs. literature-based

Felder-Silverman learning style model Index of Learning Style questionnaire Literature-based approach Data-driven approach

Automatic student modelling

The data-driven approach

    uses sample data in order to build a model for identifying learning styles from the behaviour of learners aims at building a model that imitates the ILS questionnaire Advantage: the model can be very accurate due to the use of real data Disadvantage: the approach strictly depends on the available data and is developed for specific systems

Automatic student modelling

The literature-based approach

    uses the behaviour of students in order to get hints about their learning style preferences then applies a rule-based method to calculate LSs from the number of matching hints Advantage: generic and applicable for data gathered from any course Disadvantage: might have problems in estimating the importance of the different hints

Outline

        Introduction Learning objects and Learning styles Ontologies and intelligent agents in education Incorporation of learning styles in a learning management system Automatic detection of learning styles in LMSs

Conception of an adaptive e-learning system

POLCA – Implementation and results Conclusion 36

Methodology for implementing adaptation



Annotating learning objects



Estimating learning styles



Providing adaptivity

37

Methodology

Annotating learning objects

 Each learning object is annotated with one subtype of any element in the set of 16 types of combination  E.g: Annotation of an example LO is RefSen

Active

Self assessment exercises, multiple question guessing exercises

Reflective

Examples, outlines, summaries , result pages

Sensing

Examples, explanation , facts, practical material

Intuitive

Definitions , algorithms

Visual

Images, graphics, charts, animations , videos

Verbal

Text, audio

Sequential

Step-by-step exercises, constrict link pages

Global

Outlines, summaries , all-link pages 38

Methodology Estimating learning styles

   Expected time spent on each learning object,

Time expected_stay

, is determined.

The time that a learner actually spent on each learning object, Time

spent

, is recorded.

RT LS

_

element

  

Time spent Time exp ected

_

stay

Ratios for number of visits with respect to each LS element

RV LS

_

element

 

LOs



visited LOs

39

Methodology

Estimating learning styles (cont’)

R avg

0 – 0.3

0.3 – 0.7

0.7 – 1

LS Preference

Weak Moderate Strong  An example

R avg ACT REF SNS INT VIS VRB SEQ GLO

0.5

0.6 0.25 0.2 0.8 0.15 0.8

0.9

 Learning style: moderate Active/Reflective, and strong Visual. 40

Methodology

Providing adaptivity

    Assumption: interchangeable learning objects are sufficient for each learning content.

The LMS automatically delivers suitable LOs for each learner based on:   What learning content he choses His learning style that has been identified Previous example: only LOs with Act/Ref/Vis annotations.

Combined with changing their appearance order 41

System’s adaptation

Course Topic 1 Topic 2 Topic

n

Learning objects LO 1_1 LO 1_2 LO 2_1 LO 2_2 LO

n

_1 LO

n

_2 LO

n

_3 Learning styles Active Reflective Sensing Intuitive Visual Verbal Sequential Global x Learner 1 Learner 2 Learner

n

System’s domain ontology

takes Ability abilityName: String abBelongsTo: Course abHasObjective: Competence isSupportedBy: Resource abHasObjective hasConcept Course hasAbility abBelongsTo courseName: String courseDescription: String csHasObjective: Competence hasConcept: Concept hasAbility: Ability hasResoure: Resource csHasObjective included

I

n hasResource ccBelongsTo nextConcept previousConcept hasRequisite isPrequisiteFor ccHasObjective consistsOf similarTo oppositeOf Competence (Objective) objective: String supports describes isSupportedBy

l

nHasObjective helpsTo AchieveAbility Resource (Learning Object) included

I

n: Course describes: Concept supports: Ability hasDescription: ResourceDescription Concept (Knowledge) conceptName: String ccBelongsTo: Course ccHasObjective: Competence consistOf: Concept similarTo: Concept oppositeOf: Concept nextConcept: Concept previousConcept: Concept hasRequisite: Concept isPrerequisiteFor: Concept isDescribedBy: Resource isDescribedBy helpsTo AchieveKnowledge hasDescription Learner fullName: String dateOfBirth: Date sex: Boolean phone#: String email: String levelOfStudy: String yearOfStudy: Integer workStatus: String performance: String

l l

nHasObjective: Competence takes: Course nHasLearningStyle: LearningStyle ResourceDescription createdBy: String hasKeyword: String helpsToAchieveKnowledge: Concept helpsToAchieveAbility: Ability type: String language: String difficultLevel: String rdHasLearningStyle: LearningStyle

l

nHasLearningStyle rdHasLearningStyle Learning Style activeReflective: Integer sensingIntuitive: Integer visualVerbal: Integer sequaltialGlobal: Integer

Outline

         Introduction Learning objects Learning styles Ontologies and intelligent agents in education Incorporation of learning styles in a learning management system Automatic detection of learning styles in LMSs Conception of an adaptive e-learning system

POLCA – Implementation and results

Conclusion 44

System architecture

 A multi-agent one with artificial agents Personal agent of tutor Content management service Learning content database Tutor Adaptive delivery service Adaptive content agent Chat/ Analyse Advice agent Learning style monitoring agent Learners with different learning styles Chat/Analyse Login service Personal agents of learners Inter-agent communication Other services Learning style testing service User profile database 45

System interface and functionality



Administrator:

  updates personal information of teachers and students, views statistics about each individual or all of students' behaviour with respect to FSLSM  other management tasks

System interface and functionality

Teachers

     update list of his courses: subjects, chapters, sections update his learning objects: outlines, definitions, algorithms, graphics, examples, exercises, summaries, etc.

set up tests and see participated students' results accept application requests for his course from students view statistics of students' behaviour related to their learning styles

Control menu for teachers Choosing the topic that learning object belongs to Annotating the learning object with LS properties Editing learning object’s content

System interface and functionality

Students

 register for a course  take registered courses  do the tests  see the test results

System interface and functionality

System’s agents

  Learning style monitoring agent   keeps track on every student's number of and his visit spent time on learning objects of the courses stores students' learning styles and updates new estimated ones Adaptive content agent  chooses and orders the learning objects to present for each student

LS detection result

Pr ecision



n

 1

Sim

(

LS determined

,

LS ILS

)

n

 Experiment:  an Artificial Intelligence course – 9 weeks  204 learning objects – test of LS properties  44 participated students – were asked to fill in the Index of Learning Style (ILS) questionnaire  Precision: (72,73%, 70.15%, 79.54%, 65.91%) for Act/Ref, Sen/Int, Vis/Vrb, and Seq/Glo 52

Outline

        Introduction Learning objects and Learning styles Ontologies and intelligent agents in education Incorporation of learning styles in a learning management system Automatic detection of learning styles in LMSs Conception of an adaptive e-learning system POLCA – Implementation and results

Conclusion

53

Contributions

    Develop a reductive questionnaire for detecting learning styles Make a survey of students' learning styles based on the Felder-Silver learning style model Develop an agent-based architecture for building adaptive LMSs in general Propose an annotation of learning objects and a mixture method to provide adaptivity in LMSs according to users' learning styles 54

Contributions

  Propose a new automatic and dynamic approach based on literature for identifying students’ learning styles in LMSs  has a promising detection result,  is simpler than existing ones,  and can be applied for LMSs in general Develop an adaptive e-learning system incorporating above architecture and methodologies. 55

Limitations

 no incorporated communication channel among students  the short testing time and the restricted pools of testing students

Future work

   develop more system’s functions have more accurate results in LS detection:    include more students’ behaviour patterns examine more exceptions of student behaviour consider the ability of including the relationship between learning styles and cognitive skills focus on providing better adaptivity   find whether there are adaptation features which have more impact than others monitoring agent will track also their learning performance 57

Thanks for your attention!

 Summarise the most contributions - Section 10.1

 Add the reasons why to use those appendices  Add our own citations - Sections 8.1, 8.2, 9.1, 9.3.1

 Explain more clearly about literature-based approach and Graf's method (including Figure 7.2 and Table 7.1) - Section 7.2

 Make the comparison between our method with the others more clearly - Section 9.3.1