Development and evaluation of an IoT-based data analytics system for optimizing football learning in higher education
DOI:
https://doi.org/10.17977/um086v4i12026p11-22Keywords:
Data analytics, football learning, Internet of Things, sports education, universityAbstract
Background: While smart technology and big data have advanced, football instruction at Indonesian universities lags. Most local classrooms lack high-tech tools, presenting an opportunity to modernize sports science. Objective: This research aimed to develop an IoT-based system and analytics platform for higher education, equipping students and coaches with data-driven insights for modern football training. Methods: Conducted at Universitas Negeri Makassar over five months (August–December), the study followed a Research and Development approach with six phases: needs analysis, design, development, expert validation, field trials, and effectiveness evaluation. Participants included Physical Education students divided into experimental (IoT system) and control (traditional methods) groups. Instruments concisely combined wearable sensors (accelerometers, gyroscopes, GPS) for movement tracking with skill assessments, expert questionnaires, and interviews. Quantitative data were analyzed via paired and independent-samples t-tests; effect sizes via partial eta-squared (η²p); reliability via intraclass correlation coefficients (ICC). Qualitative data were reduced, displayed, and summarized. Result: Key outcomes included a validated four-layer IoT system (expert-approved in field, technology, and pedagogy), a data analytics model spanning planning, realization, control, and evaluation, and significant skill improvements (passing accuracy, sprint speed, agility, kicking power) in the experimental group with large practical effects. Conclusion: The IoT system proved feasible and effective for enhancing football skills. Future research could scale it to broader athletic training contexts or integrate AI for real-time feedback.
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