Automatic Determination of Learning Styles

Learning styles refer to various approaches of learning. Various theories regarding learning styles have been proposed and different models are available to determine learning style of an individual. However, after analyzing 176 students’ questionnaires using Felder Silverman model we observed that learning styles boundaries are not crisp. As opposed to existing automatic techniques, we propose to use non-crisp clustering algorithms to automatically de-termine overlapping studying patterns of students registered for Saint Mary’s University’s online Courses. We applied crisp as well as non-crisp (fuzzy and rough) clustering algorithms to categorize students as studious, crammers, workers according to their study patterns. Keywords-Learning Styles; Overlapping learning styles; Felder Silverman model; Non-crisp clustering; Rough K-means; FCM