  As student failure rates continue to increase in higher education, predicting
student performance in the following semester has become a significant demand.
Personalized student performance prediction helps educators gain a
comprehensive view of student status and effectively intervene in advance.
However, existing works scarcely consider the explainability of student
performance prediction, which educators are most concerned about. In this
paper, we propose a novel Explainable Student performance prediction method
with Personalized Attention (ESPA) by utilizing relationships in student
profiles and prior knowledge of related courses. The designed Bidirectional
Long Short-Term Memory (BiLSTM) architecture extracts the semantic information
in the paths with specific patterns. As for leveraging similar paths' internal
relations, a local and global-level attention mechanism is proposed to
distinguish the influence of different students or courses for making
predictions. Hence, valid reasoning on paths can be applied to predict the
performance of students. The ESPA consistently outperforms the other
state-of-the-art models for student performance prediction, and the results are
intuitively explainable. This work can help educators better understand the
different impacts of behavior on students' studies.
