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Analyzing enrolment patterns: modified stacked ensemble statistical learning based approach to educational decision-making

In the realm of global Science, Technology, Engineering, and Mathematics (STEM) education, the declining enrolment in advanced mathematics courses poses a substantial challenge to the development of a robust STEM workforce and its role in sustainable economic growth. The study’s primary objectiv...

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Main Authors: Zun, Liang Chuan, Nursultan Japashov,, Soon, Kien Yuan, Tan, Wei Qing, Noriszura Ismail,
Format: Article
Language:English
Published: Penerbit Universiti Kebangsaan Malaysia 2024
Online Access:http://journalarticle.ukm.my/24276/1/Akademika_94_2_13.pdf
http://journalarticle.ukm.my/24276/
https://ejournal.ukm.my/akademika/issue/view/1725
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spelling my-ukm.journal.242762024-10-14T08:23:00Z http://journalarticle.ukm.my/24276/ Analyzing enrolment patterns: modified stacked ensemble statistical learning based approach to educational decision-making Zun, Liang Chuan Nursultan Japashov, Soon, Kien Yuan Tan, Wei Qing Noriszura Ismail, In the realm of global Science, Technology, Engineering, and Mathematics (STEM) education, the declining enrolment in advanced mathematics courses poses a substantial challenge to the development of a robust STEM workforce and its role in sustainable economic growth. The study’s primary objectives were to identify the determinants that impacted urban upper-secondary students' enrolment in Additional Mathematics within the Kuantan District, Pahang, Malaysia, and to develop a novel modified stacked ensemble statistical learning-based algorithm based on potential determinants, following the Cross Industry Standard Process for Data Mining (CRISP-DM) data science methodology. To pursue these objectives, this study collected and analyzed 389 responses from the first-batch urban upper secondary students in the Kuantan District who had enrolled in the newly revised Standard Based Curriculum for Secondary Schools (KSSM’s) Additional Mathematics syllabus, utilizing a modified research questionnaire and a one stage cluster sampling technique. The findings revealed that determinants such as education disciplines, ethnicity, gender, mathematics self-efficacy, peer influence, and teacher influence had significantly impacted students' decisions to enroll in Additional Mathematics. Moreover, the introduction of the novel modified stacked ensemble statistical learning-based algorithm had improved predictive accuracy compared to traditional dichotomous logistic regression algorithms on average, particularly at optimal training-to-test ratios of 70:30, 80:20, and 90:10. These insights were valuable for shaping educational policy and practice, emphasizing the importance of promoting STEM education initiatives and encouraging educators and counselors to empower students to pursue STEM careers while actively promoting gender equality within STEM fields Penerbit Universiti Kebangsaan Malaysia 2024 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/24276/1/Akademika_94_2_13.pdf Zun, Liang Chuan and Nursultan Japashov, and Soon, Kien Yuan and Tan, Wei Qing and Noriszura Ismail, (2024) Analyzing enrolment patterns: modified stacked ensemble statistical learning based approach to educational decision-making. AKADEMIKA, 94 (2). pp. 232-251. ISSN 0126-5008 https://ejournal.ukm.my/akademika/issue/view/1725
institution Universiti Kebangsaan Malaysia
building Tun Sri Lanang Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Kebangsaan Malaysia
content_source UKM Journal Article Repository
url_provider http://journalarticle.ukm.my/
language English
description In the realm of global Science, Technology, Engineering, and Mathematics (STEM) education, the declining enrolment in advanced mathematics courses poses a substantial challenge to the development of a robust STEM workforce and its role in sustainable economic growth. The study’s primary objectives were to identify the determinants that impacted urban upper-secondary students' enrolment in Additional Mathematics within the Kuantan District, Pahang, Malaysia, and to develop a novel modified stacked ensemble statistical learning-based algorithm based on potential determinants, following the Cross Industry Standard Process for Data Mining (CRISP-DM) data science methodology. To pursue these objectives, this study collected and analyzed 389 responses from the first-batch urban upper secondary students in the Kuantan District who had enrolled in the newly revised Standard Based Curriculum for Secondary Schools (KSSM’s) Additional Mathematics syllabus, utilizing a modified research questionnaire and a one stage cluster sampling technique. The findings revealed that determinants such as education disciplines, ethnicity, gender, mathematics self-efficacy, peer influence, and teacher influence had significantly impacted students' decisions to enroll in Additional Mathematics. Moreover, the introduction of the novel modified stacked ensemble statistical learning-based algorithm had improved predictive accuracy compared to traditional dichotomous logistic regression algorithms on average, particularly at optimal training-to-test ratios of 70:30, 80:20, and 90:10. These insights were valuable for shaping educational policy and practice, emphasizing the importance of promoting STEM education initiatives and encouraging educators and counselors to empower students to pursue STEM careers while actively promoting gender equality within STEM fields
format Article
author Zun, Liang Chuan
Nursultan Japashov,
Soon, Kien Yuan
Tan, Wei Qing
Noriszura Ismail,
spellingShingle Zun, Liang Chuan
Nursultan Japashov,
Soon, Kien Yuan
Tan, Wei Qing
Noriszura Ismail,
Analyzing enrolment patterns: modified stacked ensemble statistical learning based approach to educational decision-making
author_facet Zun, Liang Chuan
Nursultan Japashov,
Soon, Kien Yuan
Tan, Wei Qing
Noriszura Ismail,
author_sort Zun, Liang Chuan
title Analyzing enrolment patterns: modified stacked ensemble statistical learning based approach to educational decision-making
title_short Analyzing enrolment patterns: modified stacked ensemble statistical learning based approach to educational decision-making
title_full Analyzing enrolment patterns: modified stacked ensemble statistical learning based approach to educational decision-making
title_fullStr Analyzing enrolment patterns: modified stacked ensemble statistical learning based approach to educational decision-making
title_full_unstemmed Analyzing enrolment patterns: modified stacked ensemble statistical learning based approach to educational decision-making
title_sort analyzing enrolment patterns: modified stacked ensemble statistical learning based approach to educational decision-making
publisher Penerbit Universiti Kebangsaan Malaysia
publishDate 2024
url http://journalarticle.ukm.my/24276/1/Akademika_94_2_13.pdf
http://journalarticle.ukm.my/24276/
https://ejournal.ukm.my/akademika/issue/view/1725
_version_ 1814049116511010816
score 13.211869

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