比较聊天GPT反和同行反在塑造学生的评估判断统计分析:一个案例研究
Xiao Xie1, Lawrence Jun Zhang1, Aaron J Wilson1
1Faculty of Arts and Education, University of Auckland, Auckland 1010, New Zealand.
Behavioral sciences (Basel, Switzerland)
|July 29, 2025
概括
聊天GPT和同行反为学习统计分析的博士生提供了明显的好处. 将两者结合起来,可以在混合学习环境中提高研究能力.
科学领域:
- 教育教育教育教育教育教育.
- 人工智能的人工智能
- 统计 统计 统计 统计
背景情况:
- 在语言和教育的博士生往往需要统计分析技能,但可能缺乏信心.
- 只有论文课程在发展研究能力方面存在独特的挑战.
研究的目的:
- 调查聊天GPT-4o和同行反的教学潜力.
- 为了支持博士生在统计分析中的评价判断.
- 为了比较人工智能对学习的影响与同行反.
主要方法:
- 一个为期14周的博士级统计分析课程.
- 32名博士生在作业上收到了ChatGPT-4o或同行反.
- 进行了跟进采访,对6名参与者进行了采访.
主要成果:
- 聊天GPT-4o提供了及时,详细的反,但对准确性验证的信心有限.
- 同行反促进了批判性反思和协作,但质量不同.
- 这两种反类型在准确性,价值和流程方面不同地影响了评估判断.
结论:
- 无论是ChatGPT-4o还是同行反,都不能完全支持学生的统计能力.
- 战略性地结合人工智能和同行反,可以优化混合环境中的学习.
- 这种方法可以更好地为新手研究人员提供必要的分析技能.
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