Analysis of Dynamic Resource Access Patterns in Online Courses

Abstract

This paper presents an analysis of resource access patterns in two recently conducted online courses. One of these is an master level university lecture taught in form of a blended learning course with a wide range of online learning activities and materials, including collaborative wikis, self-tests and thematic videos. The other course on psychological aspects of computer mediated communication has been offered in the form of a MOOC. The specialty of this course was that master level students from two different universities could participate in the MOOC as a regular university class and receive credits for successful completion.

In both courses, online learning resources such as videos, scientific literature and wikis played a central role. In this context, the motivation for our research was to investigate characteristic patterns of resource usage of the learners. To gain deeper insights into the usage of learning materials we have extracted dynamic bipartite student - resource networks based on event logs of resource access. These networks have been analyzed using methods adapted from social network analysis. In particular we detect bipartite clusters of students and resources in those networks and propose a method to identify patterns and traces of their evolution over time.