一个贝叶斯一般模型来解释在测试中操作特定学习的个体差异
José H Lozano1, Javier Revuelta1
1Universidad Autónoma de Madrid, Spain.
Educational and psychological measurement
|July 3, 2023
概括
本研究提出了一种新的模型,用于在单个测试中测量个体学习差异. 贝叶斯模型准确地从实践中捕捉到学习效应,区分正确和不正确的答案如何影响学习.
科学领域:
- 心理测量 心理测量 心理测量
- 认知心理学 认知心理学
- 教育测量教育的测量
背景情况:
- 评估学习中的个体差异对于教育和心理学研究至关重要.
- 现有的模型可能无法完全捕捉在单个测试时段学习的细微差别.
- 了解实践如何对正确和不正确的反应产生不同的影响是关键.
研究的目的:
- 引入一个一般的多维模型来测量在单个测试管理中学习的个体差异.
- 根据答案正确性 (正确与不正确) 计算差异性学习效应.
- 为估计和评估学习参数提供一个强大的框架.
主要方法:
- 开发一个通用的多维学习模型.
- 应用贝叶斯框架用于模型估计和评估.
- 进行模拟研究以评估参数恢复和模型选择准确性.
- 使用逻辑能力测试数据进行经验验证.
主要成果:
- 模拟研究表明准确的参数恢复.
- 该模型在评估和选择任务中表现良好.
- 经验研究证实了该模型对现实世界的数据的适用性.
- 该模型成功地区分了不同类型的学习效应.
结论:
- 拟议的多维模型提供了一种灵活而准确的方法来衡量个体学习差异.
- 贝叶斯框架为估计和验证学习参数提供了一种可靠的方法.
- 这个模型对教育评估和认知研究具有实际意义.
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