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Learning style automatic detection

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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.

Published in: Education
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Learning style automatic detection

  1. 1. Learning Style Detection using UI interaction eLearning Personalization Student: Soliman ElSaber Advisor: Dr. Tomas Maul
  2. 2. Agenda Problem Definition Introduction Related Work Methodology Results Conclusion Future work
  3. 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. 4. User interact with the UI all the time
  5. 5. User interact with the UI all the time
  6. 6. Our approach Predicting the Learning Style (LS) based on the interaction with the User Interface (UI)
  7. 7. Agenda Problem Definition Introduction Related Work Methodology Results Conclusion Future work
  8. 8. Framework of Learning Style Models Murrell and Claxton 1987 Personality characteristics Information processing Social interaction Instructional preference
  9. 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. 10. VARK model V – Visual A – Auditory R – Read/Write K – Kinesthetic M – Multimodal
  11. 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. 12. What is your learning Style? Automatic Learning Style Detection ◦ How? ◦ When? ◦ Accuracy?
  13. 13. Agenda Introduction Problem Definition Related Work Methodology Results Conclusion Future work
  14. 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. 15. Detect and Adapt Apply different techniques to predict Learning Style ◦ Inference System ◦ Bayesian Network ◦ Artificial Neural Networks ◦ Social bookmarking ◦ Recommender System ◦ …….
  16. 16. What is the problems? ◦Continues changing in the learning style ◦Online/Offline education ◦Learner without profile
  17. 17. Agenda Introduction Problem Definition Related Work Methodology Results Conclusion Future work
  18. 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. 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. 20. Educational content with smart UI
  21. 21. Collect Dataset
  22. 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. 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. 24. Clean Dataset
  25. 25. Train the Classifier ➢Decision Tree ➢K-Nearest Neighbor ➢Logistic Regression ➢Naïve Bayes ➢Random Forest ➢Support Vector Machine
  26. 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. 27. Agenda Introduction Problem Definition Related Work Methodology Results Conclusion Future work
  28. 28. Strong Single! Multi!
  29. 29. Agenda Introduction Problem Definition Related Work Methodology Results Conclusion Future work
  30. 30. UI detecting the Strongest Preference Strong
  31. 31. Agenda Introduction Problem Definition Related Work Methodology Results Conclusion Future work
  32. 32. Future Work ◦ LMS integration ◦Mapping the media and the modes ◦VARK scoring algorithm
  33. 33. Questions?
  34. 34. Thank YOU!

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