融合DeepSeek的《高级语言程序设计》实践教学创新探索
Innovative Exploration of Practical Teaching in “Advanced Programming Languages” through the Integration of DeepSeek
DOI: 10.12677/ae.2025.15101822, PDF,    科研立项经费支持
作者: 冯丙文, 张继连, 刘志全, 耿光刚:暨南大学网络空间安全学院,广东 广州;张晓倩*:暨南大学信息科学技术学院,广东 广州
关键词: 高级语言程序设计DeepSeek编程教育High-Level Language Programming DeepSeek Programming Education
摘要: 随着人工智能技术的快速发展,DeepSeek大模型凭借其强大的推理能力与多模态交互功能,为编程教育提供了新的解决方案。本文以《高级语言程序设计》实验教学为例,探讨DeepSeek如何通过代码纠错、个性化学习支持、智能题库生成等核心功能,重塑实验教学模式,提升学生编程能力与教学效率。
Abstract: With the rapid development of artificial intelligence, the DeepSeek large-scale model, leveraging its powerful reasoning capabilities and multimodal interaction functions, offers new solutions for programming education. Using the laboratory teaching of “High-Level Language Programming” as an example, this paper explores how DeepSeek, through core functions such as code error correction, personalized learning support, and intelligent question-bank generation, can reshape laboratory teaching models and enhance students’ programming proficiency and instructional efficiency.
文章引用:冯丙文, 张晓倩, 张继连, 刘志全, 耿光刚. 融合DeepSeek的《高级语言程序设计》实践教学创新探索[J]. 教育进展, 2025, 15(10): 208-219. https://doi.org/10.12677/ae.2025.15101822

参考文献

[1] Xu, J,. Fu, Y., Tan, S.H., et al. (2024) Aligning the Objective of LLM-Based Program Repair. arXiv: 2404.08877.
[2] Yang, B., Tian, H., Pian, W., Yu, H., Wang, H., Klein, J., et al. (2024) CREF: An LLM-Based Conversational Software Repair Framework for Programming Tutors. Proceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis, Vienna, 16-20 September 2024, 882-894. [Google Scholar] [CrossRef
[3] Chen, Y., Hu, Z., Zhi, C., Han, J., Deng, S. and Yin, J. (2024) ChatUniTest: A Framework for LLM-Based Test Generation. Companion Proceedings of the 32nd ACM International Conference on the Foundations of Software Engineering, Porto de Galinhas, 15-19 July 2024, 572-576. [Google Scholar] [CrossRef
[4] Zhao, S., Zhu, A., Mozannar, H., Sontag, D., Talwalkar, A. and Chen, V. (2025) CodingGenie: A Proactive LLM-Powered Programming Assistant. Proceedings of the 33rd ACM International Conference on the Foundations of Software Engineering, Clarion Hotel Trondheim, 23-28 June 2025, 1168-1172. [Google Scholar] [CrossRef
[5] Nam, D., Macvean, A., Hellendoorn, V., Vasilescu, B. and Myers, B. (2024) Using an LLM to Help with Code Understanding. Proceedings of the IEEE/ACM 46th International Conference on Software Engineering, Lisbon, 14-20 April 2024, 1-13. [Google Scholar] [CrossRef
[6] Kazemitabaar, M., Ye, R., Wang, X., Henley, A.Z., Denny, P., Craig, M., et al. (2024) CodeAid: Evaluating a Classroom Deployment of an LLM-Based Programming Assistant That Balances Student and Educator Needs. Proceedings of the CHI Conference on Human Factors in Computing Systems, Honolulu, 11-16 May 2024, 1-20. [Google Scholar] [CrossRef
[7] 李永智, 曹培杰, 武卉紫, 等. 基于教学思维链的教育大模型推理显化研究[J]. 开放教育研究, 2025, 31(4): 4-11.
[8] Dickey, E., Bejarano, A., Kuperus, R., et al. (2025) Evaluating the AI-Lab Intervention: Impact on Student Perception and Use of Generative AI in Early Undergraduate Computer Science Courses. arXiv: 2505.00100.
[9] Prather, J., Leinonen, J., Kiesler, N., Benario, J.G., Lau, S., MacNeil, S., et al. (2024) How Instructors Incorporate Generative AI into Teaching Computing. Proceedings of the 2024 on Innovation and Technology in Computer Science Education V. 2, Milan, 8-10 July 2024, 771-772. [Google Scholar] [CrossRef
[10] Pereira Cipriano, B., Silva, M., Correia, R. and Alves, P. (2024) Towards the Integration of Large Language Models and Automatic Assessment Tools: Enhancing Student Support in Programming Assignments. Proceedings of the 24th Koli Calling International Conference on Computing Education Research, Koli, 12-17 November 2024, 1-2. [Google Scholar] [CrossRef
[11] 董珊珊, 郭燕, 杨铁林. 生成式人工智能辅助生物类专业Python课程的项目式教学[J]. 生物学杂志, 2025, 42(4): 27-30.
[12] Choi, S. and Kim, H. (2024) The Impact of a Large Language Model-Based Programming Learning Environment on Students’ Motivation and Programming Ability. Education and Information Technologies, 30, 8109-8138. [Google Scholar] [CrossRef
[13] 穆玲玲, 张行进, 职为梅, 等. 大模型背景下面向系统观的实验教学改革初探[J]. 计算机教育, 2025(7): 116-121.
[14] 王佳, 张谦, 邱爽, 等. 大模型背景下个性化的操作系统实践教学[J]. 计算机教育, 2025(8): 109-115.
[15] 刘雪峰, 陈玮, 韩旭, 等. 大模型时代计算机实验教学的挑战与机遇[J/OL]. 实验技术与管理: 1-12.
https://kns.cnki.net/kcms2/article/abstract?v=SzYTR_MU8MA7hpDmyP70-x7lbDp3smxchuiBDcYqZyviaOfy8-Y-OpwuwVT0cVJ-mewNqkV5tcEP_ImW-WG9EwITvdqsAX4g34l9KMkmIappTw8-h6DDhQhfnvaU4OCfGxYSnJLwSe30ksH_U3SoloO5CZXMuEJNxK9PVQ3VcRhNkPIu9V21Ew==&uniplatform=NZKPT&language=CHS, 2025-08-21.