1. AN ADAPTIVE E-LEARNING SYSTEM BASED ON USERS' LEARNING STYLES
Author: Phạm Quang Dũng

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. 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

4. 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

5. 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

6. 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

7. 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

8. 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

9. 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”

10. Learning style models - FSLSM (cont’) Types of combination of LS dimensions
active/sensing/visual/sequential
active/sensing/visual/global
active/sensing/verbal/sequential
active/sensing/verbal/global
active/intuitive/visual/sequential
active/intuitive/visual/global
active/intuitive/verbal/sequential
active/intuitive/verbal/global
reflective/sensing/visual/sequential
reflective/sensing/visual/global
reflective/sensing/verbal/sequential
reflective/sensing/verbal/global
reflective/intuitive/visual/sequential
reflective/intuitive/visual/global
reflective/intuitive/verbal/sequential
reflective/intuitive/verbal/global 10

11. FSLSM (cont’) Index of Learning Style (ILS) questionnaire
44 questions, 11 for each LS dimensions
Scales of the dimensions: 11

12. 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:

13. 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

14. 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

15. 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

16. 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

17. 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

18. 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.

19. Adaptivity in LMSs
Adaptivity indicates all kinds of automatic adaptation to individual learners’ needs.
Course’s content
Personal annotations

20. 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.

21. 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

22. 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.

23. Providing adaptive courses in LMSs
Course elements
Adaptation features 23

24. 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

25. 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

26. 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

27. 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

28. 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

29. 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

30. 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

31. 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

32. Automatic student modelling approaches

33. Automatic student modelling approaches data-driven vs. literature-based

34. 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

35. 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

36. 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

37. Methodology for implementing adaptation
Annotating learning objects
Estimating learning styles
Providing adaptivity 37

38. 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
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 38 38

39. 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.
Ratios for number of visits with respect to each LS element 39

40. Methodology Estimating learning styles (cont’)
An example
 Learning style: moderate Active/Reflective, and strong Visual.
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
0.9 40

41. 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

42. System’s adaptation

43. System’s domain ontology

44. 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

45. System architecture
A multi-agent one with artificial agents 45

46. 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

48. 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

50. System interface and functionality Students
register for a course
take registered courses
do the tests
see the test results

51. 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

52. 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 52

53. 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

54. 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

55. 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

56. Limitations no incorporated communication channel among students
the short testing time and the restricted pools of testing students

57. 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

58. Thanks for your attention!

59. 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