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All Journal Journal of Education Research Educative: Jurnal Ilmiah Pendidikan
Temones, John Ben
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Humanities Education Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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Machine Learning-Based Teacher Education Student Placement Model Via Interest Profile and Diagnostic Test Temones, John Ben; Domanais, Lalaine; De La Cruz, Jay Christian; Timado, Anthony Jay; Codecio, Edwin; Llonado, Michael Gerald
Journal of Education Research Vol. 6 No. 1 (2025)
Publisher : Perkumpulan Pengelola Jurnal PAUD Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37985/jer.v6i1.2331

Abstract

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.
The Honor Paradox: Rethinking Academic Distinction and Mathematical Ability in Senior High School Graduates Temones, John Ben; Dacoro, Jaslyn; Gamelo, Haizel; Hallare, John Kevin; Villaralvo, John Paul; Violanda, Bea
Educative: Jurnal Ilmiah Pendidikan Vol. 3 No. 1 (2025): January-April 2025
Publisher : LPPI Yayasan Almahmudi bin Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70437/educative.v3i1.1040

Abstract

Despite being awarded academic honors, many Filipino students continue to underperform in mathematics—a disconnect highlighted by recent PISA results. This mixed-method case study interrogates the credibility of honor-based distinctions as indicators of actual mathematical competence. Focusing on 40 first-year mathematics education students at a state university, the study correlated senior high school honors with mathematics diagnostic test performance and investigated underlying factors through a validated survey. Of the 40 participants, 37 held academic honors, yet only 2 passed the test. Fisher’s Exact Test (α = 0.05) revealed no statistically significant relationship between honors and performance. Exploratory factor analysis uncovered three latent dimensions contributing to this discrepancy: (1) educational value and personal growth, (2) mathematical engagement and self-efficacy, and (3) resource availability and teacher support. These findings challenge the assumption that academic honors are reliable proxies for competence. They call into question the meritocratic logic underpinning institutional reward systems, urging a critical reassessment of how educational success is defined, measured, and recognized.
Machine Learning-Based Teacher Education Student Placement Model Via Interest Profile and Diagnostic Test Temones, John Ben; Domanais, Lalaine; De La Cruz, Jay Christian; Timado, Anthony Jay; Codecio, Edwin; Llonado, Michael Gerald
Journal of Education Research Vol. 6 No. 1 (2025)
Publisher : Perkumpulan Pengelola Jurnal PAUD Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37985/jer.v6i1.2331

Abstract

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.
Co-Authors Codecio, Edwin Dacoro, Jaslyn De La Cruz, Jay Christian Domanais, Lalaine Gamelo, Haizel Hallare, John Kevin Llonado, Michael Gerald Timado, Anthony Jay Villaralvo, John Paul Violanda, Bea