Spatiotemporal Link Formation Prediction in Social Learning Networks Using Graph Neural Networks

TL;DR

This paper introduces a GNN-based framework for predicting future student interactions in social learning networks, considering temporal and spatial dynamics to improve accuracy over traditional methods.

Contribution

The work presents a novel GNN approach that jointly models temporal evolution and spatial aggregation in SLNs, outperforming baseline methods in link prediction accuracy.

Findings

Significant improvement in link prediction as courses progress over time.

Aggregating data from multiple classrooms enhances prediction performance.

Joint modeling of temporal and spatial factors outperforms analyzing classrooms separately.

Abstract

Social learning networks (SLNs) are graphical representations that capture student interactions within educational settings (e.g., a classroom), with nodes representing students and edges denoting interactions. Accurately predicting future interactions in these networks (i.e., link prediction) is crucial for enabling effective collaborative learning, supporting timely instructional interventions, and informing the design of effective group-based learning activities. However, traditional link prediction approaches are typically tuned to general online social networks (OSNs), often overlooking the complex, non-Euclidean, and dynamically evolving structure of SLNs, thus limiting their effectiveness in educational settings. In this work, we propose a graph neural network (GNN) framework that jointly considers the temporal evolution within classrooms and spatial aggregation across classrooms to perform link prediction in SLNs. Specifically, we analyze link prediction performance of GNNs over the SLNs of four distinct classrooms across their (i) temporal evolutions (varying time instances), (ii) spatial aggregations (joint SLN analysis), and (iii) varying spatial aggregations at varying temporal evolutions throughout the course. Our results indicate statistically significant performance improvements in the prediction of future links as the courses progress temporally. Aggregating SLNs from multiple classrooms generally enhances model performance as well, especially in sparser datasets. Moreover, we find that jointly leveraging both the temporal evolution and spatial aggregation of SLNs significantly outperforms conventional baseline approaches that analyze classrooms in isolation. Our findings demonstrate the efficacy of educationally meaningful link predictions, with direct implications for early-course decision-making and scalable learning analytics in and across classroom settings.