数据结构与算法智能答疑及自适应练习的研究
Research on Intelligent Question Answering and Adaptive Exercise of Data Structure and Algorithm
DOI: 10.12677/sea.2025.144077, PDF,    科研立项经费支持
作者: 刘城霞:北京信息科技大学计算机学院,北京
关键词: 智能答疑自适应练习错题本智能学习系统Intelligent Question Answering Adaptive Exercise Wrong Questions Book Intelligent Learning System
摘要: 随着信息技术的不断进步,传统的人工答疑及练习的方式已经不能满足学生学习的需要。为此,本文针对数据结构与算法课程设计了包含个性化练习及智能答疑的学习系统,它能够24小时回答学生有关数据结构与算法课程中的各种问题,提升了学生的学习效率。通过引入大语言模型技术,能够实现问题的自然语言理解。针对学生的薄弱环节,动态调整强化练习的题目难度,自适应的出题以及对学生答案解析并形成错题本,有效辅助知识学习的巩固。该智能学习系统不仅能够满足用户对数据结构与算法相关知识的答疑需求,还能提供个性化的学习支持,最终为用户带来高效、便捷且安全的学习体验。
Abstract: With the continuous progress of information technology, the traditional manual question answering and practice methods can no longer meet the needs of students. For this reason, this paper designs a learning system for the data structure and algorithm course, which includes personalized exercises and intelligent question answering. It can answer students’ questions about the data structure and algorithm course 24 hours a day, improving students’ learning efficiency. By introducing the technology of large language model, the natural language understanding of problems can be realized. According to the weak points of students, dynamically adjust the difficulty of the questions in the intensive exercises, adaptively set the questions, analyze the students’ answers and form a wrong question book, which effectively assists the consolidation of knowledge learning. The intelligent learning system can not only meet the user’s demand for answering questions about data structure and algorithm related knowledge, but also provide personalized learning support, which ultimately brings users an efficient, convenient and safe learning experience.
文章引用:刘城霞. 数据结构与算法智能答疑及自适应练习的研究[J]. 软件工程与应用, 2025, 14(4): 875-885. https://doi.org/10.12677/sea.2025.144077

参考文献

[1] 陈亮, 杨玉辉, 陈默, 等. GenAI驱动在线学习: 智能助教的构建与教学实践探索[J]. 现代教育技术, 2025, 35(6): 86-96.
[2] 石凤贵. 基于Web的特定领域智能答疑系统的设计与实现[J]. 承德石油高等专科学校学报, 2021, 23(1): 44-49+68.
[3] 齐凤林, 高珺, 邵园园. 高校在线教学平台智能答疑的复旦实践与探索[J]. 教育传播与技术, 2023(6): 89-96.
[4] Elhalwany, I., Mohammed, A., Wassif, K., et al. (2015) Using Textual Case-Based Reasoning in Intelligent Fatawa QA System. The International Arab Journal of Information Technology, 12, 503-509.
[5] Shang, Q., Xu, M., Qin, B., Lei, P. and Huang, J. (2021) Intelligent Question Answering System Based on Machine Reading Comprehension. Journal of Physics: Conference Series, 2050, Article ID: 012002. [Google Scholar] [CrossRef
[6] 谢珺, 杨海洋, 梁凤梅, 等. 基于课程图谱的智能答疑系统设计与开发——以“信号与系统”为例[J/OL]. 系统科学学报, 2025(3): 161-167.
http://kns.cnki.net/kcms/detail/14.1333.N.20250106.0915.010.html, 2025-06-26.
[7] Qian, M., Chen, Q., Huang, R., Zheng, H. and Chen, C. (2024) Classroom AI Question-Answering System Based on Machine Reading Comprehension Model. International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2024), Yinchuan, 7-9 June 2024, Article ID: 1325914. [Google Scholar] [CrossRef
[8] 刘展. 基于学生认知的自适应习题推荐研究与系统实现[D]: [硕士学位论文]. 扬州: 扬州大学, 2023.
[9] 段夕瑜. 基于知识图谱和知识追踪的自适应学习系统构建研究——以医学习题推荐为例[D]: [博士学位论文]. 沈阳: 中国医科大学, 2024.
[10] 李建伟, 武佳惠, 姬艳丽. 面向自适应学习的个性化学习路径推荐[J]. 现代教育技术, 2023, 33(1): 108-117.
[11] 任依梦. 基于深度知识追踪的个性化习题推荐算法研究[D]: [硕士学位论文]. 天津: 天津科技大学, 2023.
[12] 简小珠, 张敏强. 基于IRT的计算机化适应性测验的概念、类型及特征[J]. 中国考试, 2024(9): 66-75.
[13] 王焱龙. 基于知识图谱的在线学习效果动态评价与实施方法研究[D]: [硕士学位论文]. 西安: 西安理工大学, 2022.
[14] 杨文霞, 王卫华, 何朗, 等. 知识图谱赋能智慧教育的研究与实践——以武汉理工大学"线性代数"课程为例[J]. 高等工程教育研究, 2023(6): 111-117.
[15] 黄文先. 基于知识图谱的土木工程材料混合式教学[J]. 中国冶金教育, 2025(3): 20-22.