AN ADAPTIVE E-LEARNING SYSTEM BASED ON USERS' LEARNING STYLES Author: Phạm Quang Dũng.
AN ADAPTIVE E-LEARNING SYSTEM BASED ON USERS' LEARNING STYLES
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Transcript of AN ADAPTIVE E-LEARNING SYSTEM BASED ON USERS' LEARNING STYLES
AN ADAPTIVE E-LEARNING SYSTEM BASED ON USERS' LEARNING STYLES
Author: Phạm Quang Dũng
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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
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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
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
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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
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”
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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
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Index of Learning Style (ILS) questionnaire
44 questions, 11 for each LS dimensions
Scales of the dimensions:
FSLSM (cont’)
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
The questionnaire Graphical presentation: VIS
ACT
SNSGLO
SEQ
REF
INT
VRB0 1 2 3 4 5-1-2-3-4-5
1
2
3
4
5
-1
-2
-3
-4
-5
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
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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
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
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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
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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.
Introduction to LMSs
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Adaptivity in LMSs
Adaptivity indicates all kinds of automatic adaptation to individual learners’ needs. Course’s content
Personal annotations
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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.
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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
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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.
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Providing adaptive courses in LMSs
Course elements
Adaptation features
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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
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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
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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
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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
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
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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
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
Inferring learning styles from behaviour
Preparing input data
Data-driven approach Literature-based approachOR
Predicted learning style preferences
LMS database
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
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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
Methodology for implementing adaptation
Annotating learning objects
Estimating learning styles
Providing adaptivity
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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
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Active Reflective Sensing Intuitive Visual Verbal Sequential Global
Self-assessment exercises, multiple-question-guessing exercises
Examples, outlines, summaries, result pages
Examples, explanation, facts, practical material
Definitions, algorithms
Images, graphics, charts, animations, videos
Text, audio
Step-by-step exercises, constrict link pages
Outlines, summaries, all-link pages
MethodologyEstimating learning styles
Expected time spent on each learning object, Timeexpected_stay, is determined.
The time that a learner actually spent on each learning object, Timespent, is recorded.
Ratios for number of visits with respect to each LS element
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stayectedexp
spentelementLS Time
TimeRT
__
LOs
LOsRV visited
elementLS _
Methodology
Estimating learning styles (cont’)
An example
Learning style: moderate Active/Reflective, and strong Visual.
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Ravg LS Preference
0 – 0.3 Weak
0.3 – 0.7 Moderate
0.7 – 1 Strong
ACT REF SNS INT VIS VRB SEQ GLO
Ravg 0.5 0.6 0.25 0.2 0.8 0.15 0.8 0.9
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
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System’s adaptation
LO 1_1
Course
Learner 1
Topic 1
Topic 2
Topic n
Reflective
Active
Sensing
Intuitive
Visual
Verbal
Sequential
Global
Learner 2
Learner n
LO 1_2
LO n_1
LO n_2
LO n_3
LO 2_1
LO 2_2
x
Learning stylesLearning objects
System’s domain ontology
lnHasObjective
isSupportedBy
supports
nextConceptpreviousConcept
hasRequisiteisPrequisiteFor
consistsOfsimilarTo
oppositeOfabHasObjective
helpsToAchieveAbility
abBelongsTo
csHasObjective
hasAbility
isDescribedBydescribes
hasConcept
hasDescription
ccBelongsTo
lnHasLearningStyle
takes
helpsToAchieveKnowledge
rdHasLearningStyle
ccHasObjective
includedInhasResource
Concept (Knowledge)
conceptName: StringccBelongsTo: CourseccHasObjective: CompetenceconsistOf: ConceptsimilarTo: ConceptoppositeOf: ConceptnextConcept: ConceptpreviousConcept: ConcepthasRequisite: ConceptisPrerequisiteFor: ConceptisDescribedBy: Resource
Learning Style
activeReflective: IntegersensingIntuitive: IntegervisualVerbal: IntegersequaltialGlobal: Integer
Ability
abilityName: StringabBelongsTo: CourseabHasObjective: CompetenceisSupportedBy: Resource
Learner
fullName: StringdateOfBirth: Datesex: Booleanphone#: Stringemail: StringlevelOfStudy: StringyearOfStudy: IntegerworkStatus: Stringperformance: StringlnHasObjective: Competencetakes: CourselnHasLearningStyle: LearningStyle
Competence (Objective)
objective: String
Resource (Learning Object)
includedIn: Coursedescribes: Conceptsupports: AbilityhasDescription: ResourceDescription
Course
courseName: StringcourseDescription: StringcsHasObjective: CompetencehasConcept: ConcepthasAbility: AbilityhasResoure: Resource
ResourceDescription
createdBy: StringhasKeyword: StringhelpsToAchieveKnowledge: ConcepthelpsToAchieveAbility: Abilitytype: Stringlanguage: StringdifficultLevel: StringrdHasLearningStyle: LearningStyle
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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
System architecture
A multi-agent one with artificial agents
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Adaptive content agent
Learning style monitoring agent
Login service
TutorAdaptive delivery
service
Learning style testing service
Advice agent
Content management service Learning content
database
Personal agent of tutor
Learners withdifferent learning styles
User profile database
Chat/Analyse
Chat/ Analyse
Inter-agent communication
Personal agents of learners
Other services
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
Annotating the learning object
with LS properties
Choosing the topic that learning
object belongs to
Editing learning object’s contentControl menu for
teachers
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
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
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n
LSLSSimecisionPr
ILS
n
determined ),(1
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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
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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
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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.
Limitations
no incorporated communication channel
among students
the short testing time and the restricted
pools of testing students
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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
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