Predicting academic performance with fuzzy logic in prospective physical education and sports teachers.

Numerous factors contribute to student success in educational settings, with academic support and learning strategies identified as key influences. Existing research highlights that various academic assistance and individual learning approaches shape student success. Although different methods are available for predicting academic achievement, the application of fuzzy logic in this context remains relatively underexplored. This study seeks to address this gap by employing a fuzzy logic model to forecast the exam performance of prospective physical education and sports teachers. Specifically, the study examines how the learning approaches and perceived levels of academic support among these candidates influence their exam outcomes. The results indicate that fuzzy logic provides a viable tool for predicting student success, demonstrating its potential as an alternative to traditional predictive methods. Furthermore, the findings suggest that students' learning approaches and perceptions of academic support play a significant role in shaping their academic achievements. By integrating fuzzy logic into educational research, this study contributes to a broader understanding of how non-linear and complex interactions between academic support and learning strategies impact student performance. These results highlight the practical potential of fuzzy logic models in identifying at-risk students and designing targeted interventions to improve academic outcomes. Educators and institutions can create more effective and personalised learning environments by fostering deep learning approaches and enhancing academic support systems.