Peer-inspired student performance prediction in interactive online question pools with graph neural network

• Main Authors: LI, Haotian, WEI, Huan, WANG, Yong, SONG, Yangqiu, QU, Huamin.
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2020
• Subjects: Student performance prediction; graph neural networks; online question pools; Digital Communications and Networking; OS and Networks; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/5345; https://ink.library.smu.edu.sg/context/sis_research/article/6349/viewcontent/peer_inspired___pv.pdf
• Institution: Singapore Management University

Student performance prediction is critical to online education. It can benefit many downstream tasks on online learning platforms, such as estimating dropout rates, facilitating strategic intervention, and enabling adaptive online learning. Interactive online question pools provide students with interesting interactive questions to practice their knowledge in online education. However, little research has been done on student performance prediction in interactive online question pools. Existing work on student performance prediction targets at online learning platforms with predefined course curriculum and accurate knowledge labels like MOOC platforms, but they are not able to fully model knowledge evolution of students in interactive online question pools. In this paper, we propose a novel approach using Graph Neural Networks (GNNs) to achieve better student performance prediction in interactive online question pools. Specifically, we model the relationship between students and questions using student interactions to construct the student-interaction-question network and further present a new GNN model, called R2GCN, which intrinsically works for the heterogeneous networks, to achieve generalizable student performance prediction in interactive online question pools. We evaluate the effectiveness of our approach on a real-world dataset consisting of 104,113 mouse trajectories generated in the problem-solving process of over 4,000 students on 1,631 questions. The experiment results show that our approach can achieve a much higher accuracy of student performance prediction than both traditional machine learning approaches and GNN models.