一个通用的动态学习模型框架用于认知诊断
Zichu Liu1, Shiyu Wang2, Houping Xiao3
1University of Georgia, Athens, Georgia, USA.
The British journal of mathematical and statistical psychology
|February 22, 2025
概括
本研究提出了一个动态的学习模型,以使用准确性和响应时间来跟踪学生的进步. 它有助于教育工作者了解学习轨迹和考试行为,以获得更好的教育见解.
科学领域:
- 教育测量教育的测量
- 心理测量 心理测量 心理测量
- 学习分析学习分析
背景情况:
- 有效监测学生的学习轨迹对于提高教育水平至关重要.
- 基于计算机的测试产生了丰富的数据集来分析学生的表现.
- 现有的模型可能无法完全捕捉复杂的学习动态和行为.
研究的目的:
- 引入一个通用的动态学习模型框架.
- 整合响应准确度和响应时间,以全面了解学习.
- 估计随着时间的推移与多种属性相关的学习轨迹.
主要方法:
- 开发一个通用的动态学习模型.
- 整合响应准确度和响应时间.
- 用马尔科夫链蒙特卡洛 (MCMC) 算法应用贝叶斯估计方法.
主要成果:
- 模拟研究验证了MCMC算法用于参数恢复.
- 该模型有效地捕捉了学习轨迹和各种测试行为.
- 该模型在应用于真实教育数据时显示出实际实用性.
结论:
- 经过验证的动态学习模型为学生的学习进度提供了细微的见解.
- 它有助于教育工作者和研究人员了解评估期间的行为动态.
- 这一框架加强了在教育环境中对学生学习的监测和增强.
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