J* E* C* N* U* N* S* ›› 2025, Vol. 2025 ›› Issue (5): 87-98.doi: 10.3969/j.issn.1000-5641.2025.05.009

• Innovative Practices of Open Source and AI in Education • Previous Articles     Next Articles

Student employment prediction for digital jobs based on behavior in open-source communities

Linna XIE, Xuesong LU*()   

  1. School of Data Science and Engineering, East China Normal University, Shanghai 200062, China
  • Received:2025-06-27 Accepted:2025-08-06 Online:2025-09-25 Published:2025-09-25
  • Contact: Xuesong LU E-mail:xslu@dase.ecnu.edu.cn

Abstract:

Accurately predicting students’ post-graduation career paths plays a vital role in talent development in higher education and in refining recruitment strategies in industry. Most existing employment prediction research relies heavily on academic or campus-related data, while overlooking the role of students’ open-source contributions in the process of securing digital-related positions. This study addresses employment prediction for digital roles by analyzing students’ behaviors in open-source communities. We construct a heterogeneous graph comprising student nodes, code repository nodes, and various semantic relationships to model students’ expertise. To enhance prediction performance, we propose two strategies that integrate large language model (LLM) with graph neural networks: LLM-as-Encoder and LLM-as-Explainer. Experiments on our curated dataset show that the proposed approach outperforms baseline methods, achieving improvements of 7.71% in accuracy and 9.19% in Macro-F1. By leveraging open-source activity, this study supports data-driven decision-making for university career services, aids enterprises in identifying technical talent, and provides students with actionable insights for career planning.

Key words: open-source community behavior, student employment prediction, heterogeneous information network, graph neural network, large language model

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