  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.

Machine Learning