Prediction of Academic Success Based on Teaching Quality Using Fuzzy Logic

This research aims to examine the impact of the quality of teaching on the success of vocational students, focusing on professional-applicative subjects in the field of information technology, as well as on general education subjects. The quality of teaching was analyzed through six factors: comprehensibility of the teaching content, applicability of the material, quality of the teaching material, teacher’s commitment, working atmosphere and objectivity of assessment. The academic success of students is represented by the course grade. An adaptive neuro-fuzzy system, ANFIS, was used to model the relationship between the aforementioned factors and academic achievement, which combines the advantages of fuzzy logic and neural networks. Data were collected through a structured questionnaire with a Likert scale, and the model was trained and evaluated using soft computing techniques. The results show that the quality of the teaching material, the dedication of teachers and the working atmosphere have the greatest influence on the success of students. These factors were singled out as the most significant predictors of success within the ANFIS model. The findings indicate the need for a systematic approach to improving the quality of teaching in professional studies and confirm the usefulness of artificial intelligence in the analysis of educational data.