Abstract
Psychoemotional well-being factors have been recognized to have a significant impact on students’ reading literacy. However, identifying which key psychoemotional well-being factors most significantly influence students’ reading performance is still not fully explored. This research examines the psychoemotional well-being factors that distinguish the reading literacy of high-level students from low-level ones using machine learning methods in four regions of China, including Bei**g, Shanghai, Jiangsu, and Zhejiang. In total, 3497 samples were drawn from the public database of the PISA 2018, including 2935 high-level students (with proficiency level at or above Level 5) and 562 low-achieving students (at Level 2 or below). By applying Recursive Feature Elimination with Cross-Validation feature selection and Support Vector Machine classifiers approach, this study successfully identifies 15 key factors (e.g., students’ socioeconomic status and learning goals) from the total 25 psychoemotional well-being factors that synergistically distinguish high-level students from low-level students with a high accuracy score (0.905). Further, using the Shapley Additive exPlanations method, the feature importance of the features set is shown, and 10 factors relevant to the psychoemotional well-being show the feature importance of reading literacy of high-level students. This study provides important insights into the factors of psychoemotional well-being that influence students’ reading literacy development.
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Data availability
Publicly available datasets were analyzed in this study. The data that support the findings of this study are openly available at https://webfs.oecd.org/pisa2018/SPSS_STU_QQQ.zip.
Materials and/or code availability
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
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Acknowledgements
This study was supported by the 2023 priority research topic for Bei**g Education Science Planning, focused on the “Current Status and Comparative Study of Science Education in Bei**g’s Primary and Secondary Schools” (Grant number: 3059-0012). I would like to declare on behalf of my co-authors that the work described was original research that has not been published previously, and not under consideration for publication elsewhere, in whole or in part. We are grateful to the editors and reviewers for their contribution to the peer review of this work.
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Zhai, X., Yuan, W., Liu, T. et al. Machine learning investigation of optimal psychoemotional well-being factors for students’ reading literacy. Educ Inf Technol (2024). https://doi.org/10.1007/s10639-024-12580-8
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DOI: https://doi.org/10.1007/s10639-024-12580-8