High resolution temporal network analysis to understand and improve collaborative learning

Abstract

There has been significant efforts in studying collaborative and social learning using aggregate networks. Such efforts have demonstrated the worth of the approach by providing insights about the interactions, student and teacher roles, and predictability of performance. However, using an aggregated network discounts the fine resolution of temporal interactions. By doing so, we might overlook the regularities/irregularities of students' interactions, the process of learning regulation, and how and when different actors influence each other. Thus, compressing a complex temporal process such as learning may be oversimplifying and reductionist. Through a temporal network analysis of 54 students interactions (in total 3134 interactions) in an online medical education course, this study contributes with a methodological approach to building, visualizing and quantitatively analyzing temporal networks, that could help educational practitioners understand important temporal aspects of collaborative learning that might need attention and action. Furthermore, the analysis conducted emphasize the importance of considering the time characteristics of the data that should be used when attempting to, for instance, implement early predictions of performance and early detection of students and groups that need support and attention.

Exploring the Relationship Between Social Interaction, Cognitive Processing and Learning Achievements in a MOOC Discussion Forum

“…Learners often experience periods of burnout during the learning process. Saqr and Nouri (2020) found that there was a trough period in each week, which indicated a time of little or no activity. The present study revealed that the middle stage of the course was important, suggesting that instructors should take pedagogical initiatives to increase learners' motivation to participate in discussions.…”

“…Exploring dynamic networks not reveals who interaction in MOOCs is occurring between but also, and more importantly, the development and change of these interaction processes over time. Saqr and Nouri (2020) selected a one-day time scale to obtain a high-resolution view of network dynamics, calculated density, mutuality, simmelian ties, and mean degree, and found that there was a trough period in the last two or three days of each week denoting a time of slow or no activity. SNA reveals insights into the processes of network formation (Ga sevi c et al, 2019).…”

Networks in Education: A Travelogue Through Five Decades

“…In parallel to these more conventional applications, recent work has started to extend inquiry that is typical of social science and complex network research to educational settings, particularly those that are digitally mediated. Examples include research focused on network mechanisms (i.e., why digital networks form; [37], (identification of network measurements that properly account for time in relational processes [38], and network approaches for the analysis of multivariate psychological survey data [39], [40]. Routinely collected digital data of student location, such as WiFi, have also been analyzed via network approaches to understand student collocation in face-to-face settings in relation to performance [41].…”

Understanding Distributed Tutorship in Online Language Tutoring

“…The justifications of the calculation are as follows. First, by surveying previous research, we found that entropy is commonly used to calculate diversity and regularity of learning activities [3,13,16,17,25,28]. Therefore, entropy itself can represent the diversity of the tutorship.…”