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.
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
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?
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
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