Learning Student Interest Trajectory for MOOCThread Recommendation

TL;DR

This paper introduces a model to predict students' evolving interests in MOOC discussion forums, enabling better thread recommendations by capturing interest drift and content evolution.

Contribution

It proposes a novel interest trajectory prediction model using coupled RNNs with update and projection operations for MOOC forum recommendation.

Findings

Model outperforms baselines on three MOOC datasets.

Effectively captures interest drift over course progression.

Improves relevance of recommended discussion threads.

Abstract

In recent years, Massive Open Online Courses (MOOCs) have witnessed immense growth in popularity. Now, due to the recent Covid19 pandemic situation, it is important to push the limits of online education. Discussion forums are primary means of interaction among learners and instructors. However, with growing class size, students face the challenge of finding useful and informative discussion forums. This problem can be solved by matching the interest of students with thread contents. The fundamental challenge is that the student interests drift as they progress through the course, and forum contents evolve as students or instructors update them. In our paper, we propose to predict future interest trajectories of students. Our model consists of two key operations: 1) Update operation and 2) Projection operation. Update operation models the inter-dependency between the evolution of student and thread using coupled Recurrent Neural Networks when the student posts on the thread. The projection operation learns to estimate future embedding of students and threads. For students, the projection operation learns the drift in their interests caused by the change in the course topic they study. The projection operation for threads exploits how different posts induce varying interest levels in a student according to the thread structure. Extensive experimentation on three real-world MOOC datasets shows that our model significantly outperforms other baselines for thread recommendation.