VOlUME 05 ISSUE 04 April 2026
Mariano Dos Santos
Instituto Católico para Formação de Professores (ICFP) Baucau, Timor-Leste
DOI : https://doi.org/10.58806/ijsshmr.2026.v5i4n12Google Scholar Download Pdf
ABSTRACT
This study explores the classification of teacher attitudes and motivations toward implementing a Student-Centered Learning (SCL)-based curriculum in Timor-Leste using a neural network approach. Teachers were categorized into three groups: Proactive, Moderate, and Resistant. Proactive teachers exhibit a high level of motivation and positive attitudes, actively overcoming obstacles to implement SCL. Moderate teachers require additional support, such as advanced training or incentives, to enhance their engagement and adapt to the curriculum. Resistant teachers show scepticism and prefer traditional teaching methods, often due to a lack of confidence and resources. The neural network model demonstrated high accuracy in identifying these patterns, with intrinsic motivation emerging as the dominant factor influencing teacher readiness. The findings highlight the necessity of targeted interventions to address challenges faced by resistant and moderate groups, including comprehensive training programs, administrative support, and innovative teaching aids. Additionally, this research underscores the importance of fostering intrinsic motivation and positive attitudes to enhance professional capacity and adaptability. These insights serve as a valuable resource for policymakers and educators aiming to optimize the implementation of SCL curricula, thereby improving educational outcomes in resource-constrained environments.
KEYWORDS:Student-Centered Learning (SCL), Neural Networks, Teachers’ Attitude and Motivation, Curriculum Implementation
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Cite this Article:Santos, M. D. (2026). Using Neural Networks to Analyze Teachers' Attitudes and Motivations Toward Student-Centered Learning-Based Curriculum in Timor-Leste. INTERNATIONAL JOURNAL OF SOCIAL SCIENCE HUMANITY & MANAGEMENT RESEARCHS, 5(4), 676-685. https://doi.org/10.58806/ijsshmr.2026.v5i4n12