Roles in Social Interactions: Graphlets in Temporal Networks Applied to Learning Analytics

Abstract

There is a growing interest in how data generated in learning platforms, especially the interaction data, can be used to improve teaching and learning. Social network analysis and machine learning methods take advantage of network topology to detect relational patterns and model interaction behaviors. Specifically, small induced subgraphs called graphlets, provide an efficient topological description of the way each node is embedded in the meso-scale structure of a network. Here we propose to detect the roles occupied by the different participants, students and teachers, in the successive phases of courses modeled by a sequence of static snapshots. The detected positions, obtained thanks to graphlet enumeration combined with a clustering method, reveal the different roles observed in each snapshot. We also track the role changes through the overall sequence of snapshots. We apply our method to the Sqily platform and describe the mutual skill validation process. The detected roles, the transitions between roles and a overall visualization through Sankey diagrams help interpreting the course dynamics. We found that some roles act like necessary steps to engage students within an active exchange process with their classmates.

BEAM: A Network Topology Framework to Detect Weak Signals

“…Indeed, graphlets present characteristics generally associated with weak signals. According to [25] and [26] they are:…”

“…We noticed that all these users belong to the same community. We therefore allow business experts to give their final assessment regarding these results and the type/location of the users, since the shape of this graphlet can put into evidence an important social structure [26].…”

Analysing Peer Assessment Interactions and Their Temporal Dynamics Using a Graphlet-Based Method

“…By counting the positions in which the nodes appear (position enumeration), the graphlets offer a way to compare their topological role inside a social network. A previous study on the Sqily platform data [4] showed the relevance of the graphlet-based approach to detect roles. However, the limited number of graphlets used did not allow to differentiate the behaviors of students and teachers and thus to highlight statistically significant changes in behavior.…”