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Traditional grade-centered evaluation models are inadequate for high-quality software engineering talents in the digital and AI era.This study develops an academic development monitoring system to address shortcomings in dynamics,interdisciplinary integration,and industry adaptability.It builds a multi-dimensional dynamic model covering seven core dimensions with quantitative scoring,non-linear weighting,and DivClust grouping.An intelligent platform with real-time monitoring,early warning,and personalized recommendations integrates AI like multi-modal fusion and large-model diagnosis.The “monitoring-warning-improvement” loop helps optimize training programs,support personalized planning,and bridge talent-industry gaps,enabling digital transformation in software engineering education evaluation.
Abstract：Traditional grade-centered evaluation models are inadequate for high-quality software engineering talents in the digital and AI era.This study develops an academic development monitoring system to address shortcomings in dynamics,interdisciplinary integration,and industry adaptability.It builds a multi-dimensional dynamic model covering seven core dimensions with quantitative scoring,non-linear weighting,and DivClust grouping.An intelligent platform with real-time monitoring,early warning,and personalized recommendations integrates AI like multi-modal fusion and large-model diagnosis.The “monitoring-warning-improvement” loop helps optimize training programs,support personalized planning,and bridge talent-industry gaps,enabling digital transformation in software engineering education evaluation.
[1]CPC Central Committee,the State Council.Deepening the reform of educational evaluation in the new era[EB/OL].(2020-10-13)[2025-06-25].http://www.moe.gov.cn/jyb_xxgk/moe_1777/moe_1778/202010/t20201013_494381.html?utm_source=chatgpt.com.
[2]European Network for Accreditation of Engineering Education.EUR-ACE® framework standards and guidelines[EB/OL].[2025-07-03].https://www.enaee.eu/wp-content/uploads/2022/03/EAFSG-04112021-English-1-1.pdf.
[3]Accreditation Board for Engineering and Technology.Criteria for accrediting engineering programs (2023–2024)[EB/OL].[2025-07-03].https://www.abet.org/wp-content/uploads/2023/01/23-24-EAC-Criteria_FINAL.pdf.
[4]China Association for Engineering Education Accreditation.China engineering education accreditation standards (2024 Edition)[EB/OL].[2025-07-08].https://meea.cmes.org/api/File/DownloadFile?fileToken=4c079832d4aea422d7ff69af26a2a36b.
[5]Metaxas I M,Tzimiropoulos G,Patras I.DivClust:Controlling diversity in deep clustering[C]//2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).Vancouver,BC,Canada:IEEE,2023:3418-3428.
[6]Rossi E,Chamberlain B,Frasca F,et al.Temporal graph networks for deep learning on dynamic graphs[EB/OL].[2025-07-10].https://arxiv.org/abs/2006.10637.
[7]He X N,Liao L Z,Zhang H W,et al.Neural collaborative filtering[C]//Proceedings of the 26th International Conference on World Wide Web.Perth:International World Wide Web Conferences Steering Committee,2017:173-182.
基本信息:
DOI：10.16512/j.cnki.jsjjy.2026.03.041
中图分类号:G642;TP311.5-4
引用信息:
[1]Kun Niu,Kaiyang Zhang,Tan Yang,等.Research and Implementation of the Academic Development Monitoring System for High-quality Software Engineering Talents[J].计算机教育,2026,No.375(03):199-209.DOI:10.16512/j.cnki.jsjjy.2026.03.041.
基金信息:
supported by the Research Funding Project for Graduate Education and Teaching Reform of Beijing University of Posts and Telecommunications (No. 2024Y036); the Postgraduate Education and Teaching Reform Research Fund Project of Beijing University of Posts and Telecommunications (No. 2024Z007); the Postgraduate Education and Teaching Reform Project of Beijing University of Posts and Telecommunications (2025)
2025-10-14
2025
2025-11-03
2025
1
2026-03-09
2026-03-09
