Liu Hao, Yang Dongxia, Nie Shangran, Chen Xi
Collaborative Innovation Centre for Assessment of Basic Education Quality, Beijing Normal University, Beijing, China.
Business School, The University of Sydney, Camperdown, NSW, Australia.
iScience. 2024 Aug 31;27(10):110848. doi: 10.1016/j.isci.2024.110848. eCollection 2024 Oct 18.
This article explored the influencing factors of digital reading achievement based on the PISA 2018 assessment of students' reading achievement. An integrated Random Effect-Expectation Maximization (RE-EM) regression tree model was the first constructed to address the shortcomings of traditional machine learning methods for nested data estimation and the limitations of traditional linear models in handling complex data. Our study identified the key variables for the feature selection in the integrated RE-EM regression tree model include various aspects of Meta-cognition, as well as the affective element of Joy/Liking for Reading. Notably, this study found that Meta-cognition: Assess Credibility exhibits a ceiling effect on reading achievement, where the marginal effect on reading achievement significantly diminishes at the higher variable values. Additionally, Meta-cognition: Summarizing and Joy/Liking for Reading both demonstrate an approximately S-shaped curve influence on reading achievement. These findings were discussed in critical theoretical and policy implications.
本文基于2018年国际学生评估项目（PISA）对学生阅读成绩的评估，探讨了数字阅读成绩的影响因素。首先构建了一个集成随机效应期望最大化（RE-EM）回归树模型，以解决传统机器学习方法在嵌套数据估计方面的不足以及传统线性模型在处理复杂数据方面的局限性。我们的研究确定了集成RE-EM回归树模型中特征选择的关键变量，包括元认知的各个方面，以及阅读的愉悦/喜爱情感因素。值得注意的是，本研究发现元认知：评估可信度对阅读成绩呈现出天花板效应，即在较高变量值时对阅读成绩的边际效应显著减小。此外，元认知：总结和阅读的愉悦/喜爱对阅读成绩均呈现出近似S形曲线的影响。本文对这些发现的关键理论和政策意义进行了讨论。
