Pattern discovery in e-learning courses: a timebased approach

This work relies on connectivism and focuses on the interactions between learners and resources of e-learning materials aiming to discover patterns [13]. The theory of connectivism mainly tells, that knowledge is available through a network of connections. Based on Social Network Analysis, our Time-graph representation uses temporal metrics [12]. Even though longitudinal networks are the most widely used representations of temporal factors, here we considerer time-distribution criterion within a single graph. Path following techniques are not new, but the Time-graph configuration makes it in a specific fashion, that orders resources over a timeline. A resource has not the same impact at the start or at the end of a course. Hence given a specific instant, the Time-graph can inform learners, about important resources.