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Contrasting Profiles of Low-Performing Mathematics Students in Public and Private Schools in the Philippines: Insights from Machine Learning

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Title: Contrasting Profiles of Low-Performing Mathematics Students in Public and Private Schools in the Philippines: Insights from Machine Learning
Authors: Bernardo, Allan B. I. (ORCID 0000-0003-3938-266X), Cordel, Macario O., II (ORCID 0000-0001-7270-9236), Lapinid, Minie Rose C. (ORCID 0000-0002-1436-496X), Teves, Jude Michael M. (ORCID 0000-0002-7173-5341), Yap, Sashmir A., Chua, Unisse C. (ORCID 0000-0001-7467-214X)
Source: Journal of Intelligence. 2022 10.
Availability: MDPI AG. Klybeckstrasse 64, 4057 Basel, Switzerland. e-mail: indexing@mdpi.com; e-mail: jintelligence@mdpi.com; Web site: https://www.mdpi.com/journal/jintelligence
Peer Reviewed: Y
Page Count: 16
Publication Date: 2022
Document Type: Journal Articles
Reports - Research
Education Level: Secondary Education
Descriptors: Public Schools, Private Schools, Low Achievement, Mathematics Achievement, Artificial Intelligence, Foreign Countries, Achievement Tests, International Assessment, Secondary School Students, Socioeconomic Status, Prediction
Geographic Terms: Philippines
Assessment and Survey Identifiers: Program for International Student Assessment
ISSN: 2079-3200
Abstract: Filipino students performed poorly in the 2018 Programme for International Student Assessment (PISA) mathematics assessment, with more than 50% obtaining scores below the lowest proficiency level. Students from public schools also performed worse compared to their private school counterparts. We used machine learning approaches, specifically binary classification methods, to model the variables that best identified the poor performing students (below Level 1) vs. better performing students (Levels 1 to 6) using the PISA data from a nationally representative sample of 15-year-old Filipino students. We analyzed data from students in private and public schools separately. Several binary classification methods were applied, and the best classification model for both private and public school groups was the Random Forest classifier. The ten variables with the highest impact on the model were identified for the private and public school groups. Five variables were similarly important in the private and public school models. However, there were other distinct variables that relate to students' motivations, family and school experiences that were important in identifying the poor performing students in each school type. The results are discussed in relation to the social and social cognitive experiences of students that relate to socioeconomic contexts that differ between public and private schools.
Abstractor: As Provided
Entry Date: 2022
Accession Number: EJ1353615
Database: ERIC
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  Data: Contrasting Profiles of Low-Performing Mathematics Students in Public and Private Schools in the Philippines: Insights from Machine Learning
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  Data: <searchLink fieldCode="AR" term="%22Bernardo%2C+Allan+B%2E+I%2E%22">Bernardo, Allan B. I.</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-3938-266X">0000-0003-3938-266X</externalLink>)<br /><searchLink fieldCode="AR" term="%22Cordel%2C+Macario+O%2E%2C+II%22">Cordel, Macario O., II</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-7270-9236">0000-0001-7270-9236</externalLink>)<br /><searchLink fieldCode="AR" term="%22Lapinid%2C+Minie+Rose+C%2E%22">Lapinid, Minie Rose C.</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-1436-496X">0000-0002-1436-496X</externalLink>)<br /><searchLink fieldCode="AR" term="%22Teves%2C+Jude+Michael+M%2E%22">Teves, Jude Michael M.</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-7173-5341">0000-0002-7173-5341</externalLink>)<br /><searchLink fieldCode="AR" term="%22Yap%2C+Sashmir+A%2E%22">Yap, Sashmir A.</searchLink><br /><searchLink fieldCode="AR" term="%22Chua%2C+Unisse+C%2E%22">Chua, Unisse C.</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-7467-214X">0000-0001-7467-214X</externalLink>)
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  Data: <searchLink fieldCode="SO" term="%22Journal+of+Intelligence%22"><i>Journal of Intelligence</i></searchLink>. 2022 10.
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  Data: MDPI AG. Klybeckstrasse 64, 4057 Basel, Switzerland. e-mail: indexing@mdpi.com; e-mail: jintelligence@mdpi.com; Web site: https://www.mdpi.com/journal/jintelligence
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  Data: 16
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  Data: <searchLink fieldCode="DE" term="%22Public+Schools%22">Public Schools</searchLink><br /><searchLink fieldCode="DE" term="%22Private+Schools%22">Private Schools</searchLink><br /><searchLink fieldCode="DE" term="%22Low+Achievement%22">Low Achievement</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematics+Achievement%22">Mathematics Achievement</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+Intelligence%22">Artificial Intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Foreign+Countries%22">Foreign Countries</searchLink><br /><searchLink fieldCode="DE" term="%22Achievement+Tests%22">Achievement Tests</searchLink><br /><searchLink fieldCode="DE" term="%22International+Assessment%22">International Assessment</searchLink><br /><searchLink fieldCode="DE" term="%22Secondary+School+Students%22">Secondary School Students</searchLink><br /><searchLink fieldCode="DE" term="%22Socioeconomic+Status%22">Socioeconomic Status</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction%22">Prediction</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22Philippines%22">Philippines</searchLink>
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  Data: Filipino students performed poorly in the 2018 Programme for International Student Assessment (PISA) mathematics assessment, with more than 50% obtaining scores below the lowest proficiency level. Students from public schools also performed worse compared to their private school counterparts. We used machine learning approaches, specifically binary classification methods, to model the variables that best identified the poor performing students (below Level 1) vs. better performing students (Levels 1 to 6) using the PISA data from a nationally representative sample of 15-year-old Filipino students. We analyzed data from students in private and public schools separately. Several binary classification methods were applied, and the best classification model for both private and public school groups was the Random Forest classifier. The ten variables with the highest impact on the model were identified for the private and public school groups. Five variables were similarly important in the private and public school models. However, there were other distinct variables that relate to students' motivations, family and school experiences that were important in identifying the poor performing students in each school type. The results are discussed in relation to the social and social cognitive experiences of students that relate to socioeconomic contexts that differ between public and private schools.
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  Data: EJ1353615
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      Pagination:
        PageCount: 16
    Subjects:
      – SubjectFull: Public Schools
        Type: general
      – SubjectFull: Private Schools
        Type: general
      – SubjectFull: Low Achievement
        Type: general
      – SubjectFull: Mathematics Achievement
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      – SubjectFull: Artificial Intelligence
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      – SubjectFull: Foreign Countries
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      – SubjectFull: International Assessment
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      – SubjectFull: Socioeconomic Status
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      – SubjectFull: Prediction
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      – SubjectFull: Philippines
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      – SubjectFull: Program for International Student Assessment
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      – TitleFull: Contrasting Profiles of Low-Performing Mathematics Students in Public and Private Schools in the Philippines: Insights from Machine Learning
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