Personalized Thread Recommendation for MOOC Discussion Forums

TL;DR

This paper introduces a probabilistic model for MOOC discussion forums that combines topic, temporal, and user interest modeling to improve thread recommendation and provide course analytics.

Contribution

It presents a novel integrated probabilistic model that captures multiple aspects of forum activity for personalized thread recommendation in MOOCs.

Findings

Model outperforms baselines in thread recommendation accuracy.

Successfully infers learner interests and topic timescales.

Scales to large MOOC datasets with thousands of users.

Abstract

Social learning, i.e., students learning from each other through social interactions, has the potential to significantly scale up instruction in online education. In many cases, such as in massive open online courses (MOOCs), social learning is facilitated through discussion forums hosted by course providers. In this paper, we propose a probabilistic model for the process of learners posting on such forums, using point processes. Different from existing works, our method integrates topic modeling of the post text, timescale modeling of the decay in post activity over time, and learner topic interest modeling into a single model, and infers this information from user data. Our method also varies the excitation levels induced by posts according to the thread structure, to reflect typical notification settings in discussion forums. We experimentally validate the proposed model on three real-world MOOC datasets, with the largest one containing up to 6,000 learners making 40,000 posts in 5,000 threads. Results show that our model excels at thread recommendation, achieving significant improvement over a number of baselines, thus showing promise of being able to direct learners to threads that they are interested in more efficiently. Moreover, we demonstrate analytics that our model parameters can provide, such as the timescales of different topic categories in a course.