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.
Learning Style Detection using UI
Student: Soliman ElSaber
Advisor: Dr. Tomas Maul
◦ Massive Open Online Courses…..
◦ One course fit all!
◦ Learning Style!
◦ Different Preferences?
◦ User Interface!
Predicting the Learning Style (LS) based on the interaction with
the User Interface (UI)
Framework of Learning Style Models
Murrell and Claxton 1987
◦You are different!
◦Kolb’s model - 1984
◦Honey and Mumford's model – 1992
◦Felder-Silverman – 1988/2002
◦Neil Fleming's VARK model – 1987- 2006 - 2012
V – Visual
A – Auditory
R – Read/Write
K – Kinesthetic
M – Multimodal
How to know your learning Style?
◦ Honey and Mumford
◦ Felder Silverman ILS
Style changed all the time
What is your learning Style?
Automatic Learning Style Detection
Collect data from the environment
Interaction with different elements
◦ Log files data
◦ Page visited
◦ Time spent
◦ Tasks completed
Detect and Adapt
Apply different techniques to predict Learning Style
◦ Inference System
◦ Bayesian Network
◦ Artificial Neural Networks
◦ Social bookmarking
◦ Recommender System
What is the problems?
◦Continues changing in the learning style
◦Learner without profile
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?
Develop educational content with smart UI
◦ User interaction with the UI
◦ Learners VARK values (Questionnaire)
◦Analyze and prepare the stored data
Train/ Test the Classifier