Predicting Learning Interactions in Social Learning Networks: A Deep Learning Enabled Approach

TL;DR

This paper introduces a deep learning approach for predicting link formation in social learning networks, leveraging spatial and temporal features to improve accuracy over traditional models.

Contribution

The authors develop autonomous, spatial-temporal network architectures that incorporate multiple feature types for improved link prediction in SLNs.

Findings

Achieved AUCs above 0.91, reaching 0.99 on some datasets.

Outperformed Bayesian models, linear classifiers, and graph neural networks.

Neighborhood and path-based features are most influential for predictions.

Abstract

We consider the problem of predicting link formation in Social Learning Networks (SLN), a type of social network that forms when people learn from one another through structured interactions. While link prediction has been studied for general types of social networks, the evolution of SLNs over their lifetimes coupled with their dependence on which topics are being discussed presents new challenges for this type of network. To address these challenges, we develop a series of autonomous link prediction methodologies that utilize spatial and time-evolving network architectures to pass network state between space and time periods, and that models over three types of SLN features updated in each period: neighborhood-based (e.g., resource allocation), path-based (e.g., shortest path), and post-based (e.g., topic similarity). Through evaluation on six real-world datasets from Massive Open Online Course (MOOC) discussion forums and from Purdue University, we find that our method obtains substantial improvements over Bayesian models, linear classifiers, and graph neural networks, with AUCs typically above 0.91 and reaching 0.99 depending on the dataset. Our feature importance analysis shows that while neighborhood and path-based features contribute the most to the results, post-based features add additional information that may not always be relevant for link prediction.