Elective future: The influence factor mining of students’ graduation development based on hierarchical attention neural network model with graph

Abstract

The graduation development such as employment and graduate school admission of college students are important tasks. However, mining the factors that can affect the development of graduation remains challenging, because the most important factor “course” is not independent and inequality, which are always ignored by previous researchers. Furthermore, traditional structured methods cannot handle the complex relationships between courses, and attention networks cannot distinguish the weights of compulsory and elective courses with different distributions. Therefore, we present a Graph-based Hierarchical Attention Neural Network Model with Elective Course (GHANN-EC) for the prediction of graduation development in this study. Specifically, we use graph embedding that captures the unstructured relationships between courses and hierarchical attention that assigns the importance of the courses to excavating course information that represent students’ independent interests, and can more accurately understand the relationship between graduation development and academic performance. Experimental results on the real-world datasets show that GHANN-EC outperforms the existing popular approach.