Using Machine Learning Approaches to Explore Non-Cognitive Variables Influencing Reading Proficiency in English Among Filipino Learners FINAL REPORT

Filipino students ranked last in reading proficiency among all countries/territories in the PISA 2018, with only 19% meeting the minimum (Level 2) standard. It is imperative to understand the range of factors contributing to low reading proficiency, specifically variables that can be the target of i...

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Main Authors: Lucas, Rochelle I., Cordell II, Macario O., Teves, Jude Michael M., Yap, Sashmir A., Chua, Unisse C., Bernardo, Allan I.
Format: text
Published: Animo Repository 2021
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Online Access:https://animorepository.dlsu.edu.ph/res_aki/14
https://animorepository.dlsu.edu.ph/cgi/viewcontent.cgi?article=1011&context=res_aki
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Institution: De La Salle University
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spelling oai:animorepository.dlsu.edu.ph:res_aki-10112023-04-04T03:02:44Z Using Machine Learning Approaches to Explore Non-Cognitive Variables Influencing Reading Proficiency in English Among Filipino Learners FINAL REPORT Lucas, Rochelle I. Cordell II, Macario O. Teves, Jude Michael M. Yap, Sashmir A. Chua, Unisse C. Bernardo, Allan I. Filipino students ranked last in reading proficiency among all countries/territories in the PISA 2018, with only 19% meeting the minimum (Level 2) standard. It is imperative to understand the range of factors contributing to low reading proficiency, specifically variables that can be the target of interventions to help the students with poor reading proficiency. We used machine learning approaches, specifically binary classification methods, to identify the variables that best predict low (Level 1b and lower) vs. higher (Level 1a or better) reading proficiency using the Philippine PISA data from a nationally representative sample of 15-year-old students. Several binary classification methods were applied, and the best classification model was derived using support vector machines (SVM), with 81.2% average test accuracy. The 20 variables with the highest impact in the model were identified and interpreted using the socioecological perspective of development and learning. These variables included students’ home-related resources and socioeconomic constraints, learning motivation and mindsets, reading classroom experiences with teachers, reading self-beliefs, attitudes and experiences, and social experiences in the school environment. The results were discussed with reference to the need for a system perspective to address poor proficiency that requires interconnected interventions that go beyond the students’ reading classroom. 2021-11-01T07:00:00Z text application/pdf https://animorepository.dlsu.edu.ph/res_aki/14 https://animorepository.dlsu.edu.ph/cgi/viewcontent.cgi?article=1011&context=res_aki Angelo King Institute for Economic and Business Studies Animo Repository reading proficiency non-cognitive variables machine learning support vector machines motivation growth mindset reading self-concept bullying school connectedness PISA Educational Methods Elementary Education Language and Literacy Education
institution De La Salle University
building De La Salle University Library
continent Asia
country Philippines
Philippines
content_provider De La Salle University Library
collection DLSU Institutional Repository
topic reading proficiency
non-cognitive variables
machine learning
support vector machines
motivation
growth mindset
reading self-concept
bullying
school connectedness
PISA
Educational Methods
Elementary Education
Language and Literacy Education
spellingShingle reading proficiency
non-cognitive variables
machine learning
support vector machines
motivation
growth mindset
reading self-concept
bullying
school connectedness
PISA
Educational Methods
Elementary Education
Language and Literacy Education
Lucas, Rochelle I.
Cordell II, Macario O.
Teves, Jude Michael M.
Yap, Sashmir A.
Chua, Unisse C.
Bernardo, Allan I.
Using Machine Learning Approaches to Explore Non-Cognitive Variables Influencing Reading Proficiency in English Among Filipino Learners FINAL REPORT
description Filipino students ranked last in reading proficiency among all countries/territories in the PISA 2018, with only 19% meeting the minimum (Level 2) standard. It is imperative to understand the range of factors contributing to low reading proficiency, specifically variables that can be the target of interventions to help the students with poor reading proficiency. We used machine learning approaches, specifically binary classification methods, to identify the variables that best predict low (Level 1b and lower) vs. higher (Level 1a or better) reading proficiency using the Philippine PISA data from a nationally representative sample of 15-year-old students. Several binary classification methods were applied, and the best classification model was derived using support vector machines (SVM), with 81.2% average test accuracy. The 20 variables with the highest impact in the model were identified and interpreted using the socioecological perspective of development and learning. These variables included students’ home-related resources and socioeconomic constraints, learning motivation and mindsets, reading classroom experiences with teachers, reading self-beliefs, attitudes and experiences, and social experiences in the school environment. The results were discussed with reference to the need for a system perspective to address poor proficiency that requires interconnected interventions that go beyond the students’ reading classroom.
format text
author Lucas, Rochelle I.
Cordell II, Macario O.
Teves, Jude Michael M.
Yap, Sashmir A.
Chua, Unisse C.
Bernardo, Allan I.
author_facet Lucas, Rochelle I.
Cordell II, Macario O.
Teves, Jude Michael M.
Yap, Sashmir A.
Chua, Unisse C.
Bernardo, Allan I.
author_sort Lucas, Rochelle I.
title Using Machine Learning Approaches to Explore Non-Cognitive Variables Influencing Reading Proficiency in English Among Filipino Learners FINAL REPORT
title_short Using Machine Learning Approaches to Explore Non-Cognitive Variables Influencing Reading Proficiency in English Among Filipino Learners FINAL REPORT
title_full Using Machine Learning Approaches to Explore Non-Cognitive Variables Influencing Reading Proficiency in English Among Filipino Learners FINAL REPORT
title_fullStr Using Machine Learning Approaches to Explore Non-Cognitive Variables Influencing Reading Proficiency in English Among Filipino Learners FINAL REPORT
title_full_unstemmed Using Machine Learning Approaches to Explore Non-Cognitive Variables Influencing Reading Proficiency in English Among Filipino Learners FINAL REPORT
title_sort using machine learning approaches to explore non-cognitive variables influencing reading proficiency in english among filipino learners final report
publisher Animo Repository
publishDate 2021
url https://animorepository.dlsu.edu.ph/res_aki/14
https://animorepository.dlsu.edu.ph/cgi/viewcontent.cgi?article=1011&context=res_aki
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