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.

大学生的就业发展和研究生院的毕业发展是重要的任务。但是，挖掘可能影响毕业发展的因素仍然具有挑战性，因为最重要的因素“过程”不是独立的和不平等的，这是以前的研究人员总是忽略的。此外，传统的结构化方法无法处理课程之间的复杂关系，注意力网络无法区分具有不同分布的必修和选修课程的权重。因此，我们提出带有选修课的基于图的分层注意力神经网络模型（GHANN-EC）用于预测本研究中的毕业发展。具体来说，我们使用图嵌入来捕获课程与层次注意之间的非结构化关系，从而将课程的重要性分配给代表学生独立兴趣的课程信息，并可以更准确地理解毕业发展与学习成绩之间的关系。在真实数据集上的实验结果表明，GHANN-EC的性能优于现有的流行方法。