Session-Based Time-Window Identification in Virtual Learning Environments

Students organize and manage their own learning time, choosing when, what, and how to study due to the flexibility of online learning. Each person has unique learning habits that define their behaviours and distinguish them from others. To investigate the temporal behaviour of students in online learning environments, we seek to identify suitable time-windows that could be used to investigate their temporal behaviour. First, we present a novel perspective for identifying different types of sessions based on individual needs. The majority of previous works address this issue by establishing an arbitrary session timeout threshold. In this paper, we propose an algorithm for determining the optimal threshold for a given session. Second, we use data-driven methods to support investigators in determining time-windows based on the identified sessions. To this end, we developed a visual tool that assists data scientists and researchers to determine the optimal settings for session identification and locating suitable time-windows. Notes for Research • In this paper we tackle the problem of identifying appropriate time-windows that could be used to investigate the temporal behaviour of students in online learning environments and to better adapt analysis techniques to a given dataset. • Previous research has often identified time-windows intuitively or based on personal experience and viewpoints. In contrast to previous research, we propose a method to support investigators in identifying time-windows objectively using a data-driven approach based on the concept of session, which we have reformulated in three different forms to meet various individual requirements. • We also introduce an algorithm for estimating the duration of inactivity, i.e., off-task activity, during online learning. • To identify time-windows, we developed a visual tool, whose whole source code is freely available, to assist data scientists and researchers in determining the optimal settings for the session identification and locating suitable time-windows.