Abstract
This study investigates the impact of educational level on students’ academic performance across bachelor’s, master’s, and doctoral programs. Analysis of Variance (ANOVA) and Tukey’s post hoc test were applied to identify statistically significant differences among the groups. The results demonstrate that master’s students, particularly those enrolled in specialized tracks, and doctoral students achieve higher average grades compared to undergraduates. Such differences can be explained by advanced research orientation, greater learning autonomy, and more developed self-regulation skills. At the same time, the complexity of academic disciplines was found to be an important determinant of performance outcomes. Technical courses such as Machine Learning and Microcontroller Programming showed lower average grades, while courses related to databases and Internet technologies were characterized by higher achievement levels. These findings provide valuable insights for universities to reconsider curriculum design, adapt teaching methods, and develop personalized learning strategies to enhance educational quality and competitiveness in higher education.