Machine learning investigation of optimal psychoemotional well-being factors for students’ reading literacy
Author:
Funder
Beijing Education Science Planning
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s10639-024-12580-8.pdf
Reference113 articles.
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2. Alivernini, F., & Manganelli, S. (2015). Country, school and students factors associated with extreme levels of science literacy across 25 countries. International Journal of Science Education, 37(12), 1992–2012. https://doi.org/10.1080/09500693.2015.1060648
3. Alivernini, F., Cavicchiolo, E., Manganelli, S., Chirico, A., & Lucidi, F. (2020). Students’ psychological well-being and its multilevel relationship with immigrant background, gender, socioeconomic status, achievement, and class size. School Effectiveness and School Improvement, 31(2), 172–191. https://doi.org/10.1080/09243453.2019.1642214
4. Antaramian, S. (2017). The importance of very high life satisfaction for students’ academic success. Cogent Education, 4(1), 1307622. https://doi.org/10.1080/2331186X.2017.1307622
5. Apley, D. W., & Zhu, J. (2020). Visualizing the effects of predictor variables in black box supervised learning models. Journal of the Royal Statistical Society Series B: Statistical Methodology, 82(4), 1059–1086. https://doi.org/10.1111/rssb.12377
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