使用一个天真的贝叶斯方法来识别基于多个来源的学术风险: 一个概念性的复制.
Carly Oddleifson1, Stephen Kilgus1, David A Klingbeil1
1Department of Educational Psychology, University of Wisconsin-Madison, United States.
Journal of school psychology
|December 22, 2024
概括
这项研究试图复制一个天真的贝叶斯式方法来预测学生的成绩,但发现它与个人选器的表现类似,未能复制之前的发现. 大多数学生仍然没有区分,需要进一步评估.
科学领域:
- 教育心理学教育心理学
- 教育中的数据科学教育中的数据科学
- 学术选学术选
背景情况:
- 准确预测学生的学业成绩对于及时干预至关重要.
- 之前的研究探讨了将学术和社会情绪行为 (SEB) 数据结合在一起的天真贝叶斯方法.
- 复制研究对于在教育环境中验证预测模型至关重要.
研究的目的:
- 为了在概念上复制Pendergast等人. " (2018) 关于天真贝叶斯方法的诊断准确性的研究.
- 评估学术和SEB选数据与国家成绩测试的联合预测能力.
- 评估一个天真贝叶斯模型在区分学生干预风险水平的有用性.
主要方法:
- 一种天真的贝叶斯式方法整合了学术 (aimswebPlus) 和SEB (SEB风险选器) 数据.
- 分析了19所小学3至5年级的5753名学生的数据.
- 预测性表现与州成就测试成绩 (密苏里州评估计划) 进行了比较.
主要成果:
- 幼稚的贝叶斯方法显示了与个人目标webPlus措施相似的诊断准确性.
- 高比例的学生 (65%-87%) 仍然没有区分,表明不清楚的风险状况.
- 这项研究未能复制原始Pendergast等人所发现的结果. (2018年) 的研究.
结论:
- 幼稚的贝叶斯式方法,虽然整合了多个数据源,但与个人选器相比,并没有显著改善学生风险的差异化.
- 高比例的无差异化学生表明模型在干预决策的实际应用中的局限性.
- 需要进一步的研究来完善预测模型,并了解导致学生评估结果不分化的因素.
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