PROFILING K-12 LEARNERS IN SMART E-LEARNING: A COMPARATIVE STUDY OF K-MEANS AND FUZZY C-MEANS USING THE TAM, TTF, AND SUS FRAMEWORKS
DOI:
10.54443/morfai.v6i1.4700Published:
2025-12-18Downloads
Abstract
The rapid rise of smart technologies in education has reshaped STEM and coding learning,promoting adaptive and data-driven learning for diverse K-12 students. This study integrates the Technology Acceptance Model (TAM), Task Technology Fit (TTF), and System Usability Scale (SUS) to analyze students’ perceptions of usability, motivation, satisfaction, and learning outcomes. Data were collected from 450 students across elementary, middle, and high school levels to explore variations in digital learning experiences. Clustering analysis using K-Means and Fuzzy C-Means was applied to identify learner profiles based on perception and interaction patterns. The results show that Fuzzy C-Means produced more interpretable and coherent clusters than K-Means, capturing smoother boundaries and overlapping learner characteristics. Three profiles emerged: Young Explorers, Motivated Builders, and Independent Coders, reflecting developmental transitions across educational levels. These findings highlight how clustering approaches can enhance understanding of learner diversity and inform adaptive design in e-learning. The study contributes to personalized learning analytics by linking behavioral perspectives with computational modeling, offering insights for developing inclusive and user-centered digital education systems.
Keywords:
Educational data mining, Fuzzy C-Means, K-12 learners, K-Means, Smart e-learningReferences
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Copyright (c) 2026 Trifina Sartamti, Gerry Firmansyah
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