Contrasting Profiles of Low-Performing Mathematics Students in Public and Private Schools in the Philippines: Insights from Machine Learning
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 use...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Journal of Intelligence |
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| Online Access: | https://www.mdpi.com/2079-3200/10/3/61 |
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| author | Allan B. I. Bernardo Macario O. Cordel Minie Rose C. Lapinid Jude Michael M. Teves Sashmir A. Yap Unisse C. Chua |
| author_facet | Allan B. I. Bernardo Macario O. Cordel Minie Rose C. Lapinid Jude Michael M. Teves Sashmir A. Yap Unisse C. Chua |
| author_sort | Allan B. I. Bernardo |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T23:33:28Z |
| format | Article |
| id | doaj.art-bc129e97e8884a0e94c309b01f7718ea |
| institution | Directory Open Access Journal |
| issn | 2079-3200 |
| language | English |
| last_indexed | 2024-03-09T23:33:28Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Intelligence |
| spelling | doaj.art-bc129e97e8884a0e94c309b01f7718ea2023-11-23T17:05:17ZengMDPI AGJournal of Intelligence2079-32002022-08-011036110.3390/jintelligence10030061Contrasting Profiles of Low-Performing Mathematics Students in Public and Private Schools in the Philippines: Insights from Machine LearningAllan B. I. Bernardo0Macario O. Cordel1Minie Rose C. Lapinid2Jude Michael M. Teves3Sashmir A. Yap4Unisse C. Chua5Department of Psychology, De La Salle University, Manila 1004, PhilippinesDr. Andrew L. Tan Data Science Institute, De La Salle University, Manila 1004, PhilippinesDepartment of Science Education, De La Salle University, Manila 1004, PhilippinesDr. Andrew L. Tan Data Science Institute, De La Salle University, Manila 1004, PhilippinesDr. Andrew L. Tan Data Science Institute, De La Salle University, Manila 1004, PhilippinesDr. Andrew L. Tan Data Science Institute, De La Salle University, Manila 1004, PhilippinesFilipino 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.https://www.mdpi.com/2079-3200/10/3/61mathematics achievementmachine learningPhilippinespublic vs. private schoolsschool typesocioeconomic differences |
| spellingShingle | Allan B. I. Bernardo Macario O. Cordel Minie Rose C. Lapinid Jude Michael M. Teves Sashmir A. Yap Unisse C. Chua Contrasting Profiles of Low-Performing Mathematics Students in Public and Private Schools in the Philippines: Insights from Machine Learning Journal of Intelligence mathematics achievement machine learning Philippines public vs. private schools school type socioeconomic differences |
| title | Contrasting Profiles of Low-Performing Mathematics Students in Public and Private Schools in the Philippines: Insights from Machine Learning |
| title_full | Contrasting Profiles of Low-Performing Mathematics Students in Public and Private Schools in the Philippines: Insights from Machine Learning |
| title_fullStr | Contrasting Profiles of Low-Performing Mathematics Students in Public and Private Schools in the Philippines: Insights from Machine Learning |
| title_full_unstemmed | Contrasting Profiles of Low-Performing Mathematics Students in Public and Private Schools in the Philippines: Insights from Machine Learning |
| title_short | Contrasting Profiles of Low-Performing Mathematics Students in Public and Private Schools in the Philippines: Insights from Machine Learning |
| title_sort | contrasting profiles of low performing mathematics students in public and private schools in the philippines insights from machine learning |
| topic | mathematics achievement machine learning Philippines public vs. private schools school type socioeconomic differences |
| url | https://www.mdpi.com/2079-3200/10/3/61 |
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