Soliman ElSaber MSc presentation. The presentation summarizes the research done for verifying the quality of using Machine Learning algorithms in detecting the learner style based on his interaction with the educational content UI. MSc degree received from The University of Nottingham.

1. Learning Style Detection using UI
interaction
eLearning Personalization
Student: Soliman ElSaber
Advisor: Dr. Tomas Maul

2. Agenda
Problem Definition
Introduction
Related Work
Methodology
Results
Conclusion
Future work

3. Problem definition
◦ MOOCs
◦ Massive Open Online Courses…..
◦ One course fit all!
◦ Learning Style!
◦ Different Preferences?
◦ Adaption?
◦ User Interface!
Personality
characteristics
Information
processing
Social
interaction
Instructional
preference

4. User interact with the UI all the time

5. User interact with the UI all the time

6. Our approach
Predicting the Learning Style (LS) based on the interaction with
the User Interface (UI)

7. Agenda
Problem Definition
Introduction
Related Work
Methodology
Results
Conclusion
Future work

8. Framework of Learning Style Models
Murrell and Claxton 1987
Personality characteristics
Information processing
Social interaction
Instructional preference

9. Learning Style
◦You are different!
◦Different Models
◦Kolb’s model - 1984
◦Honey and Mumford's model – 1992
◦Felder-Silverman – 1988/2002
◦………
◦Neil Fleming's VARK model – 1987- 2006 - 2012

10. VARK model
V – Visual
A – Auditory
R – Read/Write
K – Kinesthetic
M – Multimodal

11. How to know your learning Style?
Questionnaires
◦ Honey and Mumford
◦ 40/80
◦ Felder Silverman ILS
◦ 44
◦ VARK
◦ 16
Time consuming
Style changed all the time

12. What is your learning Style?
Automatic Learning Style Detection
◦ How?
◦ When?
◦ Accuracy?

13. Agenda
Introduction
Problem Definition
Related Work
Methodology
Results
Conclusion
Future work

14. Collect data from the environment
Mouse movement
Navigation style
Interaction with different elements
LMS
◦ Log files data
◦ Page visited
◦ Time spent
◦ Tasks completed

15. Detect and Adapt
Apply different techniques to predict Learning Style
◦ Inference System
◦ Bayesian Network
◦ Artificial Neural Networks
◦ Social bookmarking
◦ Recommender System
◦ …….

16. What is the problems?
◦Continues changing in the learning style
◦Online/Offline education
◦Learner without profile

17. Agenda
Introduction
Problem Definition
Related Work
Methodology
Results
Conclusion
Future work

18. User Interface tracking
Can we use just the User Interface
tracking to predict the Learning Style?
Which VARK edition can be effectively
used for prediction?
Which approach the ML Classifier can
deliver the most accurate results for it?

19. Solution development
Develop educational content with smart UI
Collect Dataset
◦ User interaction with the UI
◦ Learners VARK values (Questionnaire)
◦Analyze and prepare the stored data
Train/ Test the Classifier

20. Educational content with smart UI

21. Collect Dataset

22. VARK model, Again!
Single
• V ● A
• R ● K
Tri
• VAR
• VAK
• RAK
• VRK
Bi
• VA ● VR
• VK ● AR
• AK
• RK Multi
• VARK
M
V A R K

23. Highest, Strongest, and VARK2012
V A R K Highest Strongest VARK2012
13 9 8 11 V M VARK
10 6 4 12 K M VK
3 2 1 9 K K K

24. Clean Dataset

25. Train the Classifier
➢Decision Tree
➢K-Nearest Neighbor
➢Logistic Regression
➢Naïve Bayes
➢Random Forest
➢Support Vector Machine

26. Test the Classifier
Classifier Single or Multi VARK 2012
(Highest value) (Strong Single)
Decision Tree 25.0% : 30.8% 42.3% : 47.1% 12.5%: 18.3%
k-NN 30.8% 60.6% 11.5%
Logistic Regression 29.8% 60.6% 15.4%
Naïve Bayes 26.0% 51.9% 13.5%
Random Forest 32.7% : 41.3% 57.7% : 61.5% 8.7% : 13.5%
SVM 36.5% 64.4% 19.2%

27. Agenda
Introduction
Problem Definition
Related Work
Methodology
Results
Conclusion
Future work

28. Strong Single! Multi!

29. Agenda
Introduction
Problem Definition
Related Work
Methodology
Results
Conclusion
Future work

30. UI detecting the Strongest Preference
Strong

31. Agenda
Introduction
Problem Definition
Related Work
Methodology
Results
Conclusion
Future work

32. Future Work
◦ LMS integration
◦Mapping the media and the modes
◦VARK scoring algorithm

33. Questions?

34. Thank YOU!