在生成人工智能时代评估计算机编程技能的学习
Sara Ellen Wilson1, Matthew Nishimoto2
1Department of Mechanical Engineering, University of Kansas, Lawrence, KS 66045.
Journal of biomechanical engineering
|December 23, 2023
概括
生成型人工智能 (AI) 在工程教育中提出了挑战. 将家庭作业评估转变为在面对面评分时关注学生的理解,改善了中学期考试的成绩,这表明了对计算学习的有效方法.
科学领域:
- 工程教育 工程教育教育
- 计算机科学教育计算机科学教育
背景情况:
- 像ChatGPT这样的生成人工智能 (AI) 工具可以产生功能代码,这引起了计算机编码教育的担忧.
- 传统的评估方法可能会被人工智能生成的代码规避,需要新的教学方法.
研究的目的:
- 开发和评估一个新的评估策略,用于编程工程教育的任务.
- 为了减轻生成AI对编码密集型课程中学生学习的影响.
主要方法:
- 在C++和Matlab编程任务中实施了面对面的评分方法.
- 评估重点从代码正确性转移到学生的努力和表现出的理解.
- 分析了学生在期中考试中的表现.
主要成果:
- 完全参与面对面评分过程的学生取得了明显更好的中学期考试成绩.
- 新的评估方法与平均中期考试成绩略有增加相关,而此前的评分集中在代码正确性上.
- 该方法在支持计算学习方面表现出有效性.
结论:
- 在面对面评分时,评估重点转向学生参与和理解,这可以有效地支持工程教育中的计算学习.
- 这种教学策略在面对先进的生成人工智能工具时看起来很强大.
- 需要进一步的研究来探索负责任的生成AI在计算学习环境中的整合.
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