IDEAS home Printed from https://ideas.repec.org/a/pal/palcom/v10y2023i1d10.1057_s41599-023-01705-y.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1057/s41599-023-01705-y
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1057/s41599-023-01705-y?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Luis Bryant C. Canino & Sotero O. Malayao Jr & Noel Lito B. Sayson, 2024. "Implementation of Inquiry – Based Lesson on Lights, Mirrors and Lenses in A Gamified Classroom," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 8(5), pages 2614-2624, May.
    2. 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.
    3. Cesario M. Labajo, Jr, 2024. "Modified Frayer Model and Semantic Map Its Effectiveness in Enhancing the Performance in Science of Grade 7 Science Students," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 8(3s), pages 1310-1324, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Silva, Ana Daniela & Vautero, Jaisso & Usssene, Camilo, 2021. "The influence of family on academic performance of Mozambican university students," International Journal of Educational Development, Elsevier, vol. 87(C).
    2. John Jerrim & Luis Alejandro Lopez‐Agudo & Oscar David Marcenaro‐Gutierrez, 2021. "Posh but Poor: The Association Between Relative Socio‐Economic Status and Children’s Academic Performance," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 67(2), pages 334-362, June.
    3. Muhammad Qahraman Kakar, 2021. "Ethnic Disparities, Women Education and Empowerment in South Asia," Erudite Ph.D Dissertations, Erudite, number ph21-01 edited by Manon Domingues Dos Santos.
    4. Dominic Weinberg & Gonneke W J M Stevens & Catrin Finkenauer & Bert Brunekreef & Henriëtte A Smit & Alet H Wijga, 2019. "The pathways from parental and neighbourhood socioeconomic status to adolescent educational attainment: An examination of the role of cognitive ability, teacher assessment, and educational expectation," PLOS ONE, Public Library of Science, vol. 14(5), pages 1-20, May.
    5. Grätz, Michael, 2019. "When Less Conditioning Provides Better Estimates: Overcontrol and Collider Bias in Research on Intergenerational Mobility," Working Paper Series 2/2019, Stockholm University, Swedish Institute for Social Research.
    6. Lisa Meehan & Gail Pacheco & Zoe Pushon, 2017. "Explaining ethnic disparities in bachelor's qualifications: Participation, retention and completion in NZ," Working Papers 2017/01, New Zealand Productivity Commission.
    7. 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.
    8. Sikora, Joanna & Biddle, Nicholas, 2015. "How gendered is ambition? Educational and occupational plans of Indigenous youth in Australia," International Journal of Educational Development, Elsevier, vol. 42(C), pages 1-13.
    9. Selma Paco & Joji Linaugo, 2023. "Concept Retention among Senior High School Science, Technology, Engineering, and Mathematics (STEM) Students Exposed to a Strategic Intervention Material (SIM)," Technium Social Sciences Journal, Technium Science, vol. 41(1), pages 72-81, March.
    10. Suzette H. Lazanas & Marilou C. Urbina, 2023. "Academic Marketing Climate, Marketing Strategies And Student Enrollment Turnout Of Council Admission And Marketing In Selected Schools In District 1 Province Of Laguna," Technium Social Sciences Journal, Technium Science, vol. 45(1), pages 196-209, July.
    11. José Luis Gálvez-Nieto & Karina Polanco-Levicán & Ítalo Trizano-Hermosilla & Juan Carlos Beltrán-Véliz, 2022. "Relationships between School Climate and Values: The Mediating Role of Attitudes towards Authority in Adolescents," IJERPH, MDPI, vol. 19(5), pages 1-13, February.
    12. Michael Grätz, 2022. "When less conditioning provides better estimates: overcontrol and endogenous selection biases in research on intergenerational mobility," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(5), pages 3769-3793, October.
    13. Lisa Meehan & Gail Pacheco & Zoe Pushon, 2017. "Explaining ethnic disparities in bachelor’s degree participation: Evidence from NZ," Working Papers 2017-03, Auckland University of Technology, Department of Economics.
    14. Di Tommaso, Maria Laura & Contini, Dalit & De Rosa, Dalila & Piazzalunga, Daniela, 2020. "Tackling the Gender Gap in Math with Active Learning Teaching Practices," Department of Economics and Statistics Cognetti de Martiis. Working Papers 202016, University of Turin.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pal:palcom:v:10:y:2023:i:1:d:10.1057_s41599-023-01705-y. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: https://www.nature.com/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.