TOWARDS A PERSONALISED STUDY GUIDE BASED ON BEHAVIOUR ANALYTICS

Abstract

Personalized learning may help students achieve better learning outcomes. To provide personalization, the student preferences must be known. Learning styles determine learning preferences and detecting learning style automatically rather than using a questionnaire is the topic of much research. This research proposes another technique to non-invasively elicit learning styles. The proposed technique will construct a network graph from the learning objects in a course, where each node in the graph represents a learning object. The nodes will be positioned using the similarity between learning object contents as the distance between nodes. Then, student behaviour in the form of log traces will be added as links to the graph, producing a visualization of student behaviour, named Learning Object Graph (LOG). Each student will have their behaviour compressed into a centroid point for clustering. The resulting student groups will be classified according to their learning style so that study guide personalization can be done in accordance with the correspondent learning style.