识别阅读成就的关键因素:一种机器学习方法
Hao Liu1, Dongxia Yang1, Shangran Nie2
1Collaborative Innovation Centre for Assessment of Basic Education Quality, Beijing Normal University, Beijing, China.
iScience
|October 11, 2024
概括
数字阅读成绩受到元认知和阅读的乐趣的影响. 超认知:评估可信度显示了一个天花板效应,而超认知:总结和快乐/喜欢阅读有S形的影响.
科学领域:
- 教育心理学教育心理学
- 学习科学 学习科学
- 数字识字数字识字
背景情况:
- 数字阅读是PISA 2018评估中的一个关键技能.
- 传统的统计模型在教育研究中面临着嵌套数据和复杂关系的局限性.
研究的目的:
- 确定影响学生数字阅读成绩的关键因素.
- 应用先进的统计模型来分析复杂的教育数据.
主要方法:
- 利用了PISA 2018学生阅读成绩数据.
- 开发并应用了一个集成的随机效应预期最大化 (RE-EM) 回归树模型.
- 执行特征选择以确定重要的影响变量.
主要成果:
- 超认知 (特别是"评估可信度") 证明了对阅读成绩的上限效应.
- 超认知 ("总结") 和"快乐/喜欢阅读"对成绩表现出S形的影响.
- 确定了关键变量,包括元认知和阅读乐趣的各个方面.
结论:
- 对阅读的认知技能和积极态度对于数字阅读的成功至关重要.
- 在教育评估中,RE-EM回归树模型有效处理复杂的嵌套数据.
- 调查结果为教育政策和实践提供了洞察力,以提高数字阅读技能.
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