  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 R^2GCN, 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 4000 students on 1631 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.
