Social-path embedding-based transformer for graduation development prediction

Abstract

As the education of students attracts more and more attention, the task of graduation development prediction has gradually become a hot topic in academia and industry. The task of graduation development prediction aims to predict the employment category of students in advance via academic achievement data, which can help administrators understand students' learning status and set up a reasonable learning plan. However, existing research ignores the potential impact of social relationships on students' graduation development choices. To fully explore social relationships among students, we propose a Social-path Embedding-based Transformer Neural Network (SPE-TNN) for the task of graduation development prediction in this paper. Specifically, SPE-TNN is divided into the Social-path selection layer, the Social-path embedding layer, the Transformer layer, and the Multi-layer projection layer. Firstly, the Social-path selection layer is designed to find social relationships that impact graduation development and embed them into the student's performance features through the Social-path embedding layer. Secondly, the Transformer layer is adopted to balance the weights of the students' features. Finally, the Multi-layer projection layer is used to achieve the student graduation development prediction. Experimental results on the real-world datasets show that SPE-TNN outperforms the existing popular approaches.

Keywords: GCN; Graduation development; Social relationship; Transformer.