Analyzing Enrolment Patterns: Modified Stacked Ensemble Statistical Learning-Based Approach to Educational Decision-Making

Authors

  • Chuan Zun Liang Centre for Mathematical Sciences Universiti Malaysia Pahang Al-Sultan Abdullah Lebuh Persiaran Tun Khalil Yaakob 26300 Kuantan, Pahang Malaysia.
  • Nursultan Japashov Educational Theory and Practice Department University at Albany, New York State University 1400 Washington Ave Albany, NY 12222 United States of America
  • Soon Kien Yuan Centre for Mathematical Sciences Universiti Malaysia Pahang Al-Sultan Abdullah Lebuh Persiaran Tun Khalil Yaakob 26300 Kuantan, Pahang Malaysia.
  • Tan Wei Qing Centre for Mathematical Sciences Universiti Malaysia Pahang Al-Sultan Abdullah Lebuh Persiaran Tun Khalil Yaakob 26300 Kuantan, Pahang Malaysia.
  • Noriszura Ismail Department of Mathematical Sciences Faculty of Science and Technology Universiti Kebangsaan Malaysia 43600 UKM Bangi Selangor Darul Ehsan Malaysia.

Abstract

In the realm of global Science, Technology, Engineering, and Mathematics (STEM) education, the declining enrollment 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 these determinants, following the 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.

Author Biographies

Chuan Zun Liang, Centre for Mathematical Sciences Universiti Malaysia Pahang Al-Sultan Abdullah Lebuh Persiaran Tun Khalil Yaakob 26300 Kuantan, Pahang Malaysia.

Dr. Chuan Zun Liang, a statistics senior lecturer at Universiti Malaysia Pahang Al-Sultan Abdullah, excels in image processing, pattern recognition, statistical modeling, Bayesian and Applied Statistics, and Social Sciences. His impactful contributions and passion for teaching make him a distinguished figure in the field.

Nursultan Japashov, Educational Theory and Practice Department University at Albany, New York State University 1400 Washington Ave Albany, NY 12222 United States of America

Dr. Nursultan Japshov has expertise in education research and currently pursuing the Doctor of Philosophy at the University of Albany, United States of America (USA). His expertise has significantly contributed to this submitted manuscript. 

Soon Kien Yuan, Centre for Mathematical Sciences Universiti Malaysia Pahang Al-Sultan Abdullah Lebuh Persiaran Tun Khalil Yaakob 26300 Kuantan, Pahang Malaysia.

Mr. Soon Kien Yuan is a fresh graduate pursuing his Bachelor of Applied Science in Data Analytics from Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA). He is one of the excellent students of the corresponding author in the data science programming course.   

Tan Wei Qing, Centre for Mathematical Sciences Universiti Malaysia Pahang Al-Sultan Abdullah Lebuh Persiaran Tun Khalil Yaakob 26300 Kuantan, Pahang Malaysia.

Mr. Tan Wei Qing is a fresh graduate pursuing his Bachelor of Applied Science in Data Analytics from Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA). He is one of the excellent students of the corresponding author in the data science programming course.   

Noriszura Ismail, Department of Mathematical Sciences Faculty of Science and Technology Universiti Kebangsaan Malaysia 43600 UKM Bangi Selangor Darul Ehsan Malaysia.

Prof. Dr. Noriszura Ismail, an actuarial science Professor at Universiti Kebangsaan Malaysia (UKM), excels in statistics and actuarial science. Collaborating for years, she actively contributes to research grants and publications. Her leadership and expertise significantly shape the program's academic excellence.

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Published

2024-07-31

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