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

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.

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

“…One factor that may be increasingly important in identifying poor science achievers is access to ICT devices with internet access. Studies on Filipino students; PISA achievement in reading (Bernardo et al, 2021 ) and mathematics (Bernardo et al, 2022 ) also found the same factor as a predictor of achievement, consistent with much of the research in other countries (Hu et al, 2018 ; Petko et al, 2017 ; Yoon and Yun, 2023 ; but see Bulut and Cutumisu, 2018 ). Presumably, access to the internet outside the school environment has become an important resource for learning science; perhaps not just for accessing relevant scientific knowledge available online but also as a means of communicating with classmates for information sharing, collaboration in learning activities, and supporting each other’s motivations and engagement in science learning.…”

“…Such machine learning approaches have been used to study science achievement in PISA 2015 (Chen et al, 2021 ), but the study focused on identifying the top performers, not the poor performers. Such approaches have been used to study the PISA 2018 data in other countries like China (Lee, 2022 ), Singapore (Dong and Hu, 2019 ), and the Philippines (Bernardo et al, 2021 , 2022 ), but these studies focused on predicting either performance in reading, mathematics, or the average across domains, and none so far, have focused on the PISA 2018 science results. The analytic approaches are discussed in the methods section.…”

The Evolution of Intelligence: Analysis of the Journal of Intelligence and Intelligence

“…However, these clusters were only found in the basic quadrant, indicating high popularity but low level of development of topics using the keywords creativity, intelligence, personality, and wisdom (see Beghetto and Madison 2022 ; Childs et al 2022 ; Massie et al 2022 ; Suh and Ahn 2022 ) and education, metacognition, psychometrics-statistics, and emotional intelligence (see Forthmann et al 2022 ; Hofer et al 2022 ; Józsa et al 2022 ; Novikova et al 2022 ). An emerging topic was artificial intelligence blended with bias and assessment (see Andrews-Todd et al 2022 ; Bernardo et al 2022 ; Pásztor et al 2022 ). No articles were ranked in the core (motor) topic’s quadrant.…”

“…However, while JOI is open to novelty and popular themes, it lacks a set of well-developed topics that serve as a core for the journal. One area that favored JOI in recent publications was the inclusion of novel themes such as artificial intelligence and machine learning to address old problems in the field such as the need for psychometrically sound instruments and reduction of bias or by combining current topics such as creativity with novel tendencies ( Bernardo et al 2022 ; Marrone et al 2022 ). JOI might leverage its potential as an open-access journal to reach greater audiences and influence not only the field of intelligence, but also expand through other multidisciplinary avenues.…”

Bridging the gap of Jose Magsaysay elementary school learners in numeracy: Makati localized numeracy assessment tool for stage 1 (Grades 1-3)

“…In the local context, students from the Philippines had been reported to perform among the worst of all participating nations in the 2018 Programme for International Student Assessment (PISA). In the PISA assessment, less than 20% of students exhibited the minimum competency level (Level 2) in mathematics, while over 50% displayed extremely poor proficiency (below Level 1) (Bernardo et al, 2022). With scores below the lowest level of competence on the PISA, these Filipino learners have been far behind in mathematics education, indicating that more than half of this age group of Filipino students have poor mathematical skills in comparison to their counterparts from other countries.…”