保持Elo活着:基于Elo评级的学习系统的评估和改进测量特性
Maria Bolsinova1, Bence Gergely1,2, Matthieu J S Brinkhuis3
1Department of Methodology and Statistics, Tilburg University, Tilburg, The Netherlands.
在教育中用于能力估计的Elo评分系统显示了偏见和上下文依赖的差异. 适应性项目选择可以膨胀评级差异,防止趋同. 建议采用双级分级链解决方案.
科学领域:
- 教育技术的教育技术
- 心理测量 心理测量 心理测量
- 计算机科学 计算机科学
背景情况:
- 埃洛评分系统在在线教育中广泛用于能力估计,项目校准和适应性.
- 它在教育环境中的应用需要对其测量特性进行批判性分析.
- 了解Elo的局限性对于准确的学生能力和项目难度评估至关重要.
研究的目的:
- 在教育应用中批判性地分析Elo评分系统的缺陷.
- 调查有关学生能力和项目难度的Elo评级的测量特性.
- 在适应性教育环境中识别Elo表现的问题.
主要方法:
- 进行了一项模拟研究,以检查Elo评分系统的非对称属性.
- 该研究分析了各种教育场景下的评级偏差和差异.
- 场景包括适应性项目选择和同时更新学生的能力和项目难度.
主要成果:
- 发现Elo评级通常具有偏见,并表现出取决于上下文的差异.
- 在适应性情景中,评级差异随着时间的推移的人工增加,阻碍了趋同.
- 取决于当前评级错误的项目选择加剧了趋同问题.
结论:
- 埃洛评分系统固有的偏见和差异问题限制了其在教育环境中的准确性.
- 适应性项目选择策略可能会对评级可靠性和收产生负面影响.
- 一个建议的解决方案涉及使用两个并行评级链来减轻适应性选择依赖性,并改善趋同.
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