Click-Based Student Performance Prediction: A Clustering Guided Meta-Learning Approach
This addresses the problem of enhancing learning analytics for educators and eLearning platforms by providing a more accurate method to predict student knowledge acquisition from in-video activity, though it is incremental as it builds on existing clickstream analysis with novel training techniques.
The paper tackles predicting student quiz performance from clickstream data in online courses by modeling raw click sequences with time-series architectures, self-supervised pre-training, and clustering-guided meta-learning, achieving substantial improvements over baselines on three real-world datasets.
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.