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All Journal International Journal of Multidisciplinary: Applied Business and Education Research Schrödinger: Journal of Physics Education
Torralba, Edwin M.
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Economics, Econometrics & Finance Environmental Science Medicine & Pharmacology Social Sciences Other

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Fibonacci Numbers as Hyperparameters for Image Dimension of a Convolu-tional Neural Network Image Prognosis Classification Model of COVID X-ray Images Torralba, Edwin M.
International Journal of Multidisciplinary: Applied Business and Education Research Vol. 3 No. 9 (2022): International Journal of Multidisciplinary: Applied Business and Education Rese
Publisher : Future Science / FSH-PH Publications

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/ijmaber.03.09.11

Abstract

In recent years, convolutional neural networks (CNNs) have achieved amazing success in a variety of image categorization tasks. However, the architecture of CNNs has a significant impact on their performance. The designs of the most cutting-edge CNNs are frequently hand-crafted by experts in both CNNs and the topics under investigation. As a result, it's tough for users who don't have a lot of experience with CNNs to come up with the best CNN architecture for their individual image categorization challenges. This work investigates the application of the Fibonacci numbers to efficiently solve picture classification challenges by utilizing the hyperparameter of image dimension of COVID and non-COVID x-ray images. The suggested algorithm's greatest strength is the development of a CNN model that can be utilized for COVID viral prognosis using x-ray images to supplement existing COVID pandemic testing techniques. The proposed approach is tested using the metrics of training time, accuracy, precision, recall, and F1-score on commonly used benchmark image classification datasets. According to the experimental data, the CNN model with an image dimension of 55 x 55 surpasses the other CNN models in terms of training time, accuracy, recall, and F1-score. Several issues were raised about how to choose the best CNN models for prognostic picture categorization.
Playing Games to Earn Money: The Conceptual Framework of Interaction between Gender, Learning Styles, Problematic Gaming Behavior and Success-Economic Gain Motivation of Playing Games Torralba, Edwin M.
International Journal of Multidisciplinary: Applied Business and Education Research Vol. 4 No. 3 (2023): International Journal of Multidisciplinary: Applied Business and Education Rese
Publisher : Future Science / FSH-PH Publications

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/ijmaber.04.03.22

Abstract

This study identified the constructs of problematic gaming behavior that significantly impact the success-economic gain motivation of gamers. Furthermore, this study explored the role of gender, number of games played, and learning styles in problematic gaming behavior and the success-economic gain motivation of gamers using an online survey (N = 136). This study identified escape from adverse moods and preoccupation as the significant determinants of success-economic gain motivation of gamers. Escape from bad moods and preoccupation mediates the relationship between gamers' gender, number of games played, and success-economic gain motivation. Regression analysis reveals that active-reflective and sequential-global learning styles moderate the relationship between escapism, preoccupation, and success-economic gain motivation of gamers. The results suggest that the combination of active-global learning style and reflective-sequential learning style has the highest impact on the success-economic gain motivation of gamers. The results led to two conceptual frameworks that show how gender, learning styles, problematic gaming behavior, and success-economic gain motivation all play a role in game play.
Generative AI Scaffolding in Physics Education: A Phenomenological Analysis of Its Role and Implications in STEM Learning Torralba, Edwin M.
Schrödinger: Journal of Physics Education Vol. 6 No. 3 (2025): September
Publisher : Cahaya Ilmu Cendekia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37251/sjpe.v6i3.2031

Abstract

Purpose of the study: This study investigates how generative AI tools especially video generation scaffold high school students’ understanding of Newtonian mechanics, focusing on female learners in a STEM Honors Physics class. It explores how these tools impact conceptual mastery, critical thinking, creativity, and students’ perceptions of AI use in education. Methodology: Using a phenomenological qualitative design, the study involved 17 female students. It followed a three-phase structure preparatory, scaffolding, and post-discourse with tools like AI-generated videos, simulations, TAM-based surveys, and reflective journals, grounded in Constructivist Learning Theory and the Technology Acceptance Model. Main Findings: AI-enhanced visualizations improved students’ conceptual understanding and learning efficiency. Students gained critical thinking through prompt refinement and creativity. Ethical concerns and AI accuracy issues were noted. Overall, students showed moderate satisfaction, ease of use, and usefulness perceptions, but cautious intentions toward future AI use. Novelty/Originality of this study: This is among the first studies to apply generative AI hypermedia in high school physics education through a structured, theory-driven framework. It uniquely highlights gender-specific engagement, ethical considerations, and practical integration of AI in fostering deeper conceptual and creative STEM learning.
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