AIGC赋能BOPPPS教学模型的应用型本科《计算机网络》课堂教学模式与实践
AIGC-Empowered BOPPPS-Based Classroom Teaching Model and Practice for the Application-Oriented Undergraduate Course Computer Networks
摘要: 《计算机网络》是计算机类专业的重要专业基础课程,具有理论体系复杂、知识关联性强、实践应用要求高等特点。传统课堂教学中,学生容易出现知识理解碎片化、课堂参与度不足、实践能力迁移不够和评价反馈滞后等问题。为提升课程教学质量,本文以应用型本科《计算机网络》课程为研究对象,将AIGC技术与BOPPPS教学模型相结合,构建“课前学情诊断、课中参与互动、课后拓展提升、全过程评价反馈”的课堂教学模式。课程实践中,依托学习通平台开展线上资源建设与学习过程管理,利用AIGC辅助生成教学案例、知识图谱、前测题目、实验任务和学习反馈,并通过BOPPPS六环节优化课堂教学流程。同时,课程围绕网络拓扑设计、协议分析、路由配置和故障排查等任务开展项目化教学,推动学生由知识接受者向问题分析者和实践应用者转变。实践表明,该模式有助于提升学生课堂参与度、知识理解能力和工程实践能力,对应用型本科计算机类课程教学改革具有一定参考价值。
Abstract: Computer Networks is a fundamental core course for computer-related majors, characterized by a complex theoretical framework, strong interconnections among knowledge components, and high requirements for practical application. In traditional classroom teaching, students often encounter fragmented knowledge acquisition, insufficient classroom engagement, limited transfer of practical skills, and delayed assessment and feedback. To improve the quality of course instruction, this study focuses on the Computer Networks course in application-oriented undergraduate education and integrates artificial intelligence-generated content (AIGC) technology with the BOPPPS instructional model. Accordingly, a classroom teaching model comprising pre-class learning diagnosis, in-class participatory interaction, post-class extension and enhancement, and whole-process assessment and feedback are developed. In practice, the Chaoxing Learning Platform is employed to support the development of online learning resources and the management of the learning process. AIGC is used to assist in generating teaching cases, knowledge graphs, pre-assessment questions, experimental tasks, and learning feedback, while the six stages of the BOPPPS model are implemented to optimize the classroom teaching process. Meanwhile, project-based teaching activities are organized around such tasks as network topology design, protocol analysis, routing configuration, and network troubleshooting, facilitating students’ transformation from passive recipients of knowledge into active problem solvers and practitioners. The teaching practice indicates that the proposed model contributes to enhancing students’ classroom engagement, conceptual understanding, and engineering practice competence, and provides a useful reference for the instructional reform of computer-related courses in application-oriented undergraduate education.
文章引用:廖婧, 舒玉红, 肖其宇, 王中银. AIGC赋能BOPPPS教学模型的应用型本科《计算机网络》课堂教学模式与实践[J]. 教育进展, 2026, 16(8): 602-611. https://doi.org/10.12677/ae.2026.1681670

参考文献

[1] 杨宗凯, 王俊, 吴砥, 等. ChatGPT/生成式人工智能对教育的影响探析及应对策略[J]. 华东师范大学学报(教育科学版), 2023, 41(7): 26-35.
[2] UNESCO (2023) Guidance for Generative AI in Education and Research.
https://www.unesco.org/en/articles/guidance-generative-ai-education-and-research
[3] 祝智庭, 戴岭. 设计智慧驱动下教育数字化转型的目标向度、指导原则和实践路径[J]. 华东师范大学学报(教育科学版), 2023, 41(3): 12-24.
[4] 吴砥, 李环, 陈旭. 人工智能通用大模型教育应用影响探析[J]. 开放教育研究, 2023, 29(2): 19-25+45.
[5] Denny, P., Prather, J., Becker, B.A., Finnie-Ansley, J., Hellas, A., Leinonen, J., et al. (2024) Computing Education in the Era of Generative AI. Communications of the ACM, 67, 56-67.
https://doi.org/10.1145/3624720
[6] Liu, J., Omar, S.Z., Wang, Y. and Xiang, Y. (2025) BOPPPS Model Implementation and Students’ Performance: A Systematic Literature Review. Frontiers in Education, 9, Article ID: 1467225.
https://doi.org/10.3389/feduc.2024.1467225
[7] 于承敏, 娄东, 于承菊, 等. 基于BOPPPS教学模型的计算机网络课程线上线下混合式教学模式探索[J]. 计算机教育, 2025(1): 193-197.
[8] 黄荣怀, 刘梦彧, 刘嘉豪, 等. 智慧教育之“为何”与“何为”——关于智能时代教育的表现性与建构性特征分析[J]. 电化教育研究, 2023, 44(1): 5-12+35.
[9] Sain, Z.H., Thelma, C.C., Baharun, H., et al. (2024) Chatgpt for Positive Impact? Examining the Opportunities and Challenges of Large Language Models in Education. International Journal of Educational Development, 1, 87-100.
https://doi.org/10.61132/ijed.v1i3.75
[10] 高德毅, 宗爱东. 从思政课程到课程思政: 从战略高度构建高校思想政治教育课程体系[J]. 中国高等教育, 2017(1): 43-46.