Machine Learning-Based Teacher Education Student Placement Model Via Interest Profile and Diagnostic Test
DOI:
https://doi.org/10.37985/jer.v6i1.2331Keywords:
Student placement, teacher education, machine learningAbstract
Traditional student placement in college programs based on academic performance, interviews, and student choice may not always yield optimal results. This study proposes a machine learning-based model for teacher education program placement, integrating student interests and diagnostic test results across various specializations. Data from 208 freshmen in a teacher education institution (AY 2024-2025) were collected using a validated interest profile questionnaire and diagnostic test. Various machine learning methods were evaluated for classification performance. Results showed that most students exhibited strong interest in their chosen specialization, highlighting interest as a key placement factor. Diagnostic test performance trends further indicated that students tend to excel in their respective fields. The final placement model employed artificial neural networks, support vector machines, gradient boosting, and adaptive boosting, each achieving at least 80% classification accuracy and F1 score. This model offers a systematic and data-driven approach to optimizing teacher education student placement.
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Assiri, B., Bashraheel, M., & Alsuri, A. (2024). Enhanced student admission procedures at universities using data mining and machine learning techniques. Applied Sciences, 14(3), 1109. https://doi.org/10.3390/app14031109
Borsuk, M. E. (2008). Statistical prediction (artificial neural network). Dalam Encyclopedia of Ecology (pp. 3416–3422). Elsevier. https://www.sciencedirect.com/topics/veterinary-science-and-veterinary-medicine/artificial-neural-network
Colton, S., & Muggleton, S. (2006). Mathematical applications of inductive logic programming. Annals of Mathematics and Artificial Intelligence, 47(3-4), 227–256.
Cuy, R. C., & Salinas, J. L. (2019). Aspiration and readiness of Filipino senior high school students in pursuing college degree. ResearchGate. https://www.researchgate.net/publication/333166558_Aspiration_and_Readiness_of_Filipino_Senior_High_School_Students_in_Pursuing_College_Degree
De la Fuente, J. K. (n.d.). Overview of student learning outcome assessments in the Philippines. TeacherPH. https://www.teacherph.com/student-learning-outcome-assessments-philippines/
Del Rosario, C. M., Cruz, J. P., & Ramos, M. C. (2024). Understanding how senior high school students choose a college degree program: A phenomenological study. ResearchGate. https://www.researchgate.net/publication/382826258_Understanding_How_Senior_High_School_Students_Choose_a_College_Degree_Program_A_Phenomenological_Study
Gamal, B. (2020, November 11). Exploring Naive Bayes: Mathematics, how it works, pros & cons, and applications. Analytics Vidhya. https://medium.com/analytics-vidhya/na%C3%AFve-bayes-algorithm-5bf31e9032a2
Hackett, G., & Betz, N. E. (1981). A self-efficacy approach to the career development of women. Journal of Vocational Behavior, 18(3), 326–339.
Hoss, B., & Haghigat, A. (2021). Machine learning guide for oil and gas using Python. Elsevier. https://www.sciencedirect.com/science/article/abs/pii/B9780128219294000044
IBM. (n.d.). Machine learning. IBM Think. https://www.ibm.com/think/topics/machine-learning
Jacobs, J. (2024, April 16). Rostow's 5 stages of economic growth and development. ThoughtCo. https://www.thoughtco.com/rostows-stages-of-growth-development-model-1434564
Jordan, K. (2019, June 26). Spotlight on Parson's trait and factor theory. Careers New Zealand. https://www.careers.govt.nz/articles/spotlight-on-parsons-trait-and-factor-theory/
K to 12 Senior High School Core Curriculum – 21st Century Literature from the Philippines and the World CG. (n.d.). Department of Education, Philippines. https://www.deped.gov.ph/wp-content/uploads/2019/01/SHS-Core_21st-Century-Literature-from-the-Philippines-and-the-World-CG.pdf
Kozon, T. (2023, November 29). The impact of machine learning on society. Boringowl. https://boringowl.io/en/blog/the-impact-of-machine-learning-on-the-modern-society
Law Insider. (n.d.). Student placement. Law Insider. https://www.lawinsider.com/dictionary/student-placement
Lent, R. W., & Brown, S. D. (2019). Social cognitive career theory at 25: Empirical status of the interest, choice, and performance models. Journal of Vocational Behavior, 115, 103316.
Lent, R. W., Brown, S. D., & Hackett, G. (1994). Toward a unifying social cognitive theory of career and academic interest, choice, and performance [Monograph]. Journal of Vocational Behavior, 45(1), 79–122.
Microsoft. (n.d.). Machine learning algorithms. Azure. https://azure.microsoft.com/en-us/resources/cloud-computing-dictionary/what-are-machine-learning-algorithms/
Mishra, M. (2023, April 20). Stochastic gradient descent: A basic explanation. Medium. https://mohitmishra786687.medium.com/stochastic-gradient-descent-a-basic-explanation-cbddc63f08e0
Ogaga, D., & Zhao, H. (2023). The rise of artificial intelligence and machine learning in healthcare industry. International Journal of Research and Innovation in Applied Science, 8(4), 429–435. https://doi.org/10.51584/IJRIAS.2023.8429
Orwell, G. (2020). 1984. Project Gutenberg. (Original work published 1949)
Owen, D. (2015). Blueprints: A comprehensive guide to building and designing your project. CreateSpace Independent Publishing Platform.
Pham, M., Lam, B. Q., & Bui, A. T. N. (2024). Career exploration and its influence on the relationship between self-efficacy and career choice: The moderating role of social support. Heliyon, 10(12), e31808. https://doi.org/10.1016/j.heliyon.2024.e31808
Philippine struggle to make the grade in STEM education. (2022, November 11). SciDev.Net. https://www.scidev.net/asia-pacific/scidev-net-investigates/philippine-struggle-to-make-the-grade-in-stem-education/
Siregar, T. (2024). Analysis of interest and ability factors influencing students' choice of mathematics education major. ResearchGate. https://www.researchgate.net/publication/383156792_Analysis_of_Interest_and_Ability_Factors_Influencing_Students'_Choice_of_Mathematics_Education_Major
Subasi, A. (2020). Practical machine learning for data analysis using Python. Academic Press. https://www.sciencedirect.com/science/article/abs/pii/B9780128213797000035
Top Hat. (n.d.). Diagnostic assessment. Top Hat Glossary. https://tophat.com/glossary/d/diagnostic-assessment/
Turney, S. (2022, September 15). Frequency distribution | Tables, types & examples. Scribbr. https://www.scribbr.com/statistics/frequency-distributions/
University of Poland. (n.d.). ONet interest profiler*. University of Poland Career Services. https://www.up.edu/career/explore-majors-and-careers/onet-interest-profiler.html
Vocabulary.com. (n.d.). Blueprint. In Vocabulary.com dictionary. https://www.vocabulary.com/dictionary/blueprint
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Copyright (c) 2025 John Ben Temones, Lalaine Domanais, Jay Christian De La Cruz, Anthony Jay Timado, Edwin Codecio, Michael Gerald Llonado
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