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Profiling low-proficiency science students in the Philippines using machine learning

Author

Listed:
  • Allan B. I. Bernardo

    (De La Salle University)

  • Macario O. Cordel

    (De La Salle University)

  • Marissa Ortiz Calleja

    (De La Salle University)

  • Jude Michael M. Teves

    (De La Salle University)

  • Sashmir A. Yap

    (De La Salle University)

  • Unisse C. Chua

    (De La Salle University)

Abstract

Filipino students’ performance in global assessments of science literacy has always been low, and this was confirmed again in the PISA 2018, where Filipino learners’ average science literacy scores ranked second to last among 78 countries. In this study, machine learning approaches were used to analyze PISA data from the student questionnaire to test models that best identify the poorest-performing Filipino students. The goal was to explore factors that could help identify the students who are vulnerable to very low achievement in science and that could indicate possible targets for reform in science education in the Philippines. The random forest classifier model was found to be the most accurate and more precise, and Shapley Additive Explanations indicated 15 variables that were most important in identifying the low-proficiency science students. The variables related to metacognitive awareness of reading strategies, social experiences in school, aspirations and pride about achievements, and family/home factors, include parents’ characteristics and access to ICT with internet connections. The results of the factors highlight the importance of considering personal and contextual factors beyond the typical instructional and curricular factors that are the foci of science education reform in the Philippines, and some implications for programs and policies for science education reform are suggested.

Suggested Citation

  • Allan B. I. Bernardo & Macario O. Cordel & Marissa Ortiz Calleja & Jude Michael M. Teves & Sashmir A. Yap & Unisse C. Chua, 2023. "Profiling low-proficiency science students in the Philippines using machine learning," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-12, December.
    • Handle: RePEc:pal:palcom:v:10:y:2023:i:1:d:10.1057_s41599-023-01705-y
    DOI: 10.1057/s41599-023-01705-y
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    References listed on IDEAS

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    1. Gary Marks, 2008. "Are Father’s or Mother’s Socioeconomic Characteristics More Important Influences on Student Performance? Recent International Evidence," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 85(2), pages 293-309, January.
    2. Elisa Caponera & Paolo Sestito & Paolo M. Russo, 2016. "The influence of reading literacy on mathematics and science achievement," The Journal of Educational Research, Taylor & Francis Journals, vol. 109(2), pages 197-204, March.
    3. Trinidad, Jose Eos, 2020. "Material resources, school climate, and achievement variations in the Philippines: Insights from PISA 2018," International Journal of Educational Development, Elsevier, vol. 75(C).
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    Cited by:

    1. Allan B. I. Bernardo & Ma. Joahna Mante-Estacio, 2023. "Metacognitive reading strategies and its relationship with Filipino high school students’ reading proficiency: insights from the PISA 2018 data," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-9, December.

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