LGS-KT:整合逻辑和语法技能,以实现有效的编程知识跟踪
Xinjie Sun1, Qi Liu2, Kai Zhang3
1School of Computer Science, Liupanshui Normal University, Liupanshui, China; School of Computer Science and Technology, University of Science and Technology of China, Hefei, China; State Key Laboratory of Cognitive Intelligence, Hefei, China.
概括
本研究引入了一种通过分析代码质量和学生互动来评估编程技能的新模型. 逻辑和语法技能知识跟踪 (LGS-KT) 模型显著提高了预测学生在编程练习中的表现的准确性.
科学领域:
- 计算机科学 计算机科学
- 教育技术的教育技术
- 人工智能的人工智能
背景情况:
- 传统的知识跟踪 (KT) 方法不充分评估编程技能,因为在编码过程中经常忽视关键的行为数据.
- 现有的方法主要依赖于练习结果,未能捕捉学生编程发展的细微差别.
- 需要更全面的模型,整合多种数据源,以准确测量编程能力.
研究的目的:
- 开发和评估一个新的知识追踪模型,逻辑和语法技能知识追踪 (LGS-KT),专门为编程教育设计.
- 通过结合逻辑和语法方面来提高学生编程技能的评估.
- 提高在编程练习中预测学生结果的准确性.
主要方法:
- 集成静态分析和动态监控 (CPU/内存使用) 以评估代码质量和学生编程行为.
- 通过分析多个学生对编程问题的代,开发了一个重量化的逻辑技能演变图.
- 建立了一个基于知识概念相似性的语法技能交互图,以模拟语法技能交互.
主要成果:
- 与现有方法相比,LGS-KT模型在预测学生成绩方面表现优越.
- 显著提高了推断学生编程语法技能状态的准确性.
- 综合方法提供了更彻底的代码质量和学生发展的评估.
结论:
- 通过整合逻辑和语法维度,LGS-KT模型提供了一种更有效的方法来评估编程技能.
- 这项研究突出了高级KT模型在个性化编程教育中的潜力.
- 该研究提供了开源数据和代码,以促进教育技术的进一步研究和创新.
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