STEM考试成绩:在大型语言模型时代,开放式与封闭式的方法
Rasi Mizori1, Muhayman Sadiq1, Malik Takreem Ahmad1
1GKT School of Medicine, Faculty of Life Sciences & Medicine, King's College London, London, UK.
The clinical teacher
|November 4, 2024
概括
在STEM教育中,开放式考试 (OBEs) 通常优于封闭式考试 (CBEs),但像LLM这样的复杂AI工具挑战了它们的学术完整性. 需要进一步的研究,以确保在不断变化的技术环境中进行公平的评估.
科学领域:
- 在STEM教育方面.
- 教育技术的教育技术
- 学术诚信 学术诚信
背景情况:
- 由于COVID-19大流行加速了远程学习,在STEM中增加了对开放式考试 (OBEs) 和封闭式考试 (CBEs) 的审查.
- 复杂的大型语言模型 (LLM) 正在成为教育评估的重要因素.
研究的目的:
- 与CBEs相比,评估OBEs在STEM科目中的学生表现和感知方面的有效性.
- 考虑LLM对OBE有效性的影响.
主要方法:
- 从2013年开始发表的同行评审文章的系统审查,遵守PRISMA指南.
- 用随机效应模型评估标准化平均差异的八项研究的元分析.
- 使用I平方统计,科克兰的Q测试和Tau统计评估异质性.
主要成果:
- 一般来说,OBE比CBE更好的学生成绩,尽管观察到显著的异质性 (I2 = 97%).
- 观察性研究表明,OBE的效果更为明显,同时还存在对技术困难和作弊的担忧.
结论:
- 与CBEs相比,OBE评估能力与当前的教育范式更好地协调一致.
- 法律学士学位的出现对OBE的有效性和学术完整性提出了挑战,需要进一步调查.
- 机构必须谨慎考虑OBE能力,考虑到不断发展的技术,并确保公平的学生评估.
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