Self-paced Graph Memory Network for Student GPA Prediction and Abnormal Student Detection

Student learning performance prediction (SLPP) is a crucial step in high school education. However, traditional methods fail to consider abnormal students. In this study, we organized every student’s learning data as a graph to use the schema of graph memory networks (GMNs). To distinguish the students and make GMNs learn robustly, we proposed to train GMNs in an “easy-to-hard” process, leading to self-paced graph memory network (SPGMN). SPGMN chooses the lowdifficult samples as a batch to tune the model parameters in each training iteration. This approach not only improves the robustness but also rearranges the student sample from normal to abnormal. The experiment results show that SPGMN achieves a higher prediction accuracy and more robustness in comparison with traditional methods. The resulted student sequence reveals the abnormal student has a different pattern in course selection to normal students.