Click-Based Student Performance Prediction: A Clustering Guided Meta-Learning Approach

• URL: http://arxiv.org/abs/2111.00901v1
• Date: Thu, 28 Oct 2021 14:03:29 GMT
• Title: Click-Based Student Performance Prediction: A Clustering Guided Meta-Learning Approach
• Authors: Yun-Wei Chu, Elizabeth Tenorio, Laura Cruz, Kerrie Douglas, Andrew S. Lan, Christopher G. Brinton
• Abstract summary: We study the problem of predicting student knowledge acquisition in online courses from clickstream behavior. Our methodology for predicting in-video quiz performance is based on three key ideas we develop.
• Score: 10.962724342736042
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We study the problem of predicting student knowledge acquisition in online courses from clickstream behavior. Motivated by the proliferation of eLearning lecture delivery, we specifically focus on student in-video activity in lectures videos, which consist of content and in-video quizzes. Our methodology for predicting in-video quiz performance is based on three key ideas we develop. First, we model students' clicking behavior via time-series learning architectures operating on raw event data, rather than defining hand-crafted features as in existing approaches that may lose important information embedded within the click sequences. Second, we develop a self-supervised clickstream pre-training to learn informative representations of clickstream events that can initialize the prediction model effectively. Third, we propose a clustering guided meta-learning-based training that optimizes the prediction model to exploit clusters of frequent patterns in student clickstream sequences. Through experiments on three real-world datasets, we demonstrate that our method obtains substantial improvements over two baseline models in predicting students' in-video quiz performance. Further, we validate the importance of the pre-training and meta-learning components of our framework through ablation studies. Finally, we show how our methodology reveals insights on video-watching behavior associated with knowledge acquisition for useful learning analytics.

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