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2023 | OriginalPaper | Chapter

Learning Factors for TIMSS Math Performance Evidenced Through Machine Learning in the UAE

Authors : Ali Nadaf, Samantha Monroe, Sarath Chandran, Xin Miao

Published in: Artificial Intelligence in Education Technologies: New Development and Innovative Practices

Publisher: Springer Nature Singapore

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Abstract

Understanding how the UAE K12 education system performs with data-driven evidence is key to inform better policy making to support UAE vision to upskill human capital growth for its economic transformation. In this study, we investigate the potential of using machine learning techniques to understand key learning factors contributing to UAE student math performance on the TIMSS 2019 assessment. Due to the fact that learning factors co-exist and interact with one another, we explore the SHapley Additive exPlanations (SHAP) approach to explain the complexity of the model. The results highlight the importance and contributions of each learning factor and uncover the relationships between the learning factors. Understanding key learning factors and identifying evidence-based intervention opportunities will help policymakers with informed education intervention designs to improve student mathematics learning, in order to improve UAE student TIMSS math performance over the long run.

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Retrieved from the TIMSS 2019 International Database Downloads, https://timss2019.org/international-database/.

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Title: Learning Factors for TIMSS Math Performance Evidenced Through Machine Learning in the UAE
Authors: Ali Nadaf
Samantha Monroe
Sarath Chandran
Xin Miao
Publisher: Springer Nature Singapore
Book: Artificial Intelligence in Education Technologies: New Development and Innovative Practices
Print ISBN: 978-981-19-8039-8

Electronic ISBN: 978-981-19-8040-4

Copyright Year: 2023
DOI: https://doi.org/10.1007/978-981-19-8040-4_4

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