Visual Analytics for Session-based Time-Windows Identification in Virtual Learning Environments

Due to the flexibility of online learning courses, students organise and manage their own learning time by deciding when, what, and how to study. Each individual has distinctive learning habits that identify their behaviours and set them apart from others. To explore how students behave over time, in this work we seek to identify adequate time-windows that could be used to investigate the temporal behaviour of students in online learning environments. We first propose a novel perspective to identify various types of sessions based on individual requirements. Most of the works in the literature address this problem by setting an arbitrary session timeout threshold. In this paper we propose an algorithm that helps us in determining the most suitable threshold for the session. Then, based on the identified sessions, we determine time-windows using data-driven methods. To this end, we created a visual tool that assists data scientists and researchers in determining the optimal settings for the session identification and locating suitable time-windows.