  Student's academic performance prediction empowers educational technologies
including academic trajectory and degree planning, course recommender systems,
early warning and advising systems. Given a student's past data (such as grades
in prior courses), the task of student's performance prediction is to predict a
student's grades in future courses. Academic programs are structured in a way
that prior courses lay the foundation for future courses. The knowledge
required by courses is obtained by taking multiple prior courses, which
exhibits complex relationships modeled by graph structures. Traditional methods
for student's performance prediction usually neglect the underlying
relationships between multiple courses; and how students acquire knowledge
across them. In addition, traditional methods do not provide interpretation for
predictions needed for decision making. In this work, we propose a novel
attention-based graph convolutional networks model for student's performance
prediction. We conduct extensive experiments on a real-world dataset obtained
from a large public university. The experimental results show that our proposed
model outperforms state-of-the-art approaches in terms of grade prediction. The
proposed model also shows strong accuracy in identifying students who are
at-risk of failing or dropping out so that timely intervention and feedback can
be provided to the student.
