Textbook Enhanced Student Learning Outcome Prediction

Tianqi Wang, Fenglong Ma, Tang Tang, Longfei Zhang, Jing Gao

Abstract: Online learning platforms that can recommend tailored materials for different students have become increasingly popular recently. To enable personalized learning, it is critical and essential to automatically estimate the mastery levels of students, which motivates a new task in the education field, i.e., the student learning outcome prediction. Although several models have been proposed, most of them ignore the relations between questions and knowledge concepts. However, manually labeling the relations among questions only by experts is inefficient and impractical, due to the large volume of questions in the online learning platforms. Thus, an automatic inference of such relations is needed. In addition, different students may use different concepts when answering the same question, which makes the inferred relation between a question and concepts differ among students. To address these challenges, we propose to leverage information from a textbook to link questions with knowledge concepts that a student may retrieve. Correspondingly, we propose a novel framework named TESLOP, which can effectively utilize both textual and structural information in the textbook for student learning outcome prediction. The model simulates the process of a student picking an answer by recalling the knowledge obtained from the textbook and utilizing the knowledge to pick a correct answer. Experimental results show that the proposed TESLOP framework outperforms state-of-the-art baselines, which confirms the importance of leveraging textbook information in the student learning outcome prediction task. It also demonstrates that the proposed way of integrating such information is effective.