Download Learning objects and Learning styles

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
9.
reflective/sensing/visual/sequential
2. active/sensing/visual/global
10. reflective/sensing/visual/global
3. active/sensing/verbal/sequential
11. reflective/sensing/verbal/sequential
4. active/sensing/verbal/global
12. reflective/sensing/verbal/global
5. active/intuitive/visual/sequential
13. reflective/intuitive/visual/sequential
6. active/intuitive/visual/global
14. reflective/intuitive/visual/global
7. active/intuitive/verbal/sequential
15. reflective/intuitive/verbal/sequential
8. active/intuitive/verbal/global
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
 The questionnaire
 Graphical presentation:
4
GLO
SNS
3
2
1
VRB
-5
-4
-3
-2
-1
0
1
2
3
4
-1
-2
-3
INT
-4
-5
REF
SEQ
5
VIS
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
Inferring learning styles from behaviour
LMS
database
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
Data-driven
approach
Literature-based
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
Selfassessment
exercises,
multiplequestionguessing
exercises
Reflective
Sensing
Intuitive
Examples,
outlines,
summaries
, result
pages
Examples,
explanation
, facts,
practical
material
Definitions
,
algorithms
Visual
Verbal
Images,
Text,
graphics,
audio
charts,
animations
, videos
Sequential
Global
Step-by-step
exercises,
constrict link
pages
Outlines,
summaries
, all-link
pages
38
Methodology
Estimating 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.
RTLS _ element 
Time
spent
Time
expected _ stay
 Ratios for number of visits with respect to each LS
element
RVLS _ element
LOs


 LOs
visited
39
Methodology
Estimating learning styles (cont’)
Ravg
LS Preference
0 – 0.3
Weak
0.3 – 0.7
Moderate
0.7 – 1
Strong
 An example
Ravg
ACT
REF
SNS
INT VIS
0.5
0.6 0.25 0.2
VRB
SEQ
GLO
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
Learning objects
Learning styles
LO
1_1
Active
LO
1_2
Reflective
Topic 1
LO
2_1
Topic 2
Learner 1
Sensing
Intuitive
LO
2_2
Course
Learner 2
Visual
LO
n_1
Topic n
LO
n_2
LO
n_3
x
Verbal
Sequential
Global
Learner n
System’s domain ontology
takes
hasConcept
Course
Ability
abilityName: String
abBelongsTo: Course
abHasObjective: Competence
isSupportedBy: Resource
Concept (Knowledge)
ccBelongsTo
hasAbility
abBelongsTo
courseName: String
courseDescription: String
csHasObjective: Competence
hasConcept: Concept
hasAbility: Ability
hasResoure: Resource
csHasObjective
abHasObjective
hasResource
nextConcept
previousConcept
hasRequisite
isPrequisiteFor
consistsOf
similarTo
oppositeOf
includedIn
ccHasObjective
Competence
(Objective)
objective: String
supports
describes
isSupportedBy
isDescribedBy
Resource (Learning Object)
lnHasObjective
helpsTo
AchieveAbility
includedIn: Course
describes: Concept
supports: Ability
hasDescription: ResourceDescription
helpsTo
AchieveKnowledge
hasDescription
Learner
fullName: String
dateOfBirth: Date
sex: Boolean
phone#: String
email: String
levelOfStudy: String
yearOfStudy: Integer
workStatus: String
performance: String
lnHasObjective: Competence
takes: Course
lnHasLearningStyle: LearningStyle
conceptName: String
ccBelongsTo: Course
ccHasObjective: Competence
consistOf: Concept
similarTo: Concept
oppositeOf: Concept
nextConcept: Concept
previousConcept: Concept
hasRequisite: Concept
isPrerequisiteFor: Concept
isDescribedBy: Resource
ResourceDescription
createdBy: String
hasKeyword: String
helpsToAchieveKnowledge: Concept
helpsToAchieveAbility: Ability
type: String
language: String
difficultLevel: String
rdHasLearningStyle: LearningStyle
lnHasLearningStyle
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
Learning style
monitoring agent
Advice agent
Login service
Learners with
different learning styles
Personal agents
of learners
Inter-agent
communication
Other services
Chat/Analyse
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
Choosing the topic
that learning
object belongs to
Annotating the
learning object
with LS properties
Control menu for
teachers
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
n
Precision 
 Sim( LS
determined
, LS ILS )
1
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