An evaluation of learner clustering based on learning styles in MOOC course

Abstract

This article presents an approach for the automatic detection of learners' learning styles from their traces when they interact with a MOOC environment. The approach in question has been evaluated in particular to identify learners' learning styles associated with the active/reflective dimension, with reference to the Felder-Silverman model (FSLSM), which is one of the most popular models in the learning technology.