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.

Teachers’ AI digital competencies and twenty-first century skills in the post-pandemic world

“…AI assists teachers in designing instructional content that suits students’ needs to personalize students’ learning through task automation. For example, Yang et al ( 2022 ) embedded a predictive model into the AI-driven system to facilitate teachers’ assessment and analysis of learners’ performance to detect the potential at-risk learners at early stages. It offers timely cognitive support in the course and effectively enhances students’ academic performance.…”

“…Demchenko et al ( 2021 ) identified a digital transformation in legal education that teachers need to equip their students with AI competencies. There is a need for law schools to innovate and form stronger interdisciplinary collaboration with AI experts to enhance their effective professional and everyday activities such as using AI-based tools to aid human judgement and identifying algorithmic bias (Yang et al, 2022 ). In business education, AI has a wide range of use in business to complete authentic tasks such as aggregating business data, managing customer relationships and predicting future trends.…”

Prediction of Graduation Development Based on Hypergraph Contrastive Learning With Imbalanced Sampling

“…Social relationships among students are vital for graduation development choice. Yang et al [7] embedded social relationships based on students' elective and compulsory courses and balanced the weights of student characteristics by Transformer to predict students' graduation development; Cheng et al [8] collected data on academic performance, English proficiency, and other activities to predict college students' employment outcomes using deep neural networks, providing guidelines for college students' career development.…”

“…The virtual minority class samples generated by the sampling methods of the above studies may be of low quality, leading to the model's bias toward the lesser number of non-employed students' characteristics being poorly learned, which leads to lower model accuracy. In contrast, existing studies on graduation development prediction [7] preprocess the dataset to alleviate the imbalance problem.…”

On Social Development and Natural Environment Protection Method Research

“…Domestic scholars started relatively late in the study of the relationship between environment and economy. Guangze Yang selected 12 environmental indicators, including the discharge of industrial three wastes and their comprehensive utilization rate, urban greening, and 12 economic indicators, including per capita GDP and per capita industrial and agricultural output value, to build an evaluation system for the CD of economy and environment [2].…”