DIGITAL EDUCATION AND ACADEMIC EXCELLENCE OF STUDENTS: DEVELOPMENT OF EDUCATION BETWEEN LEVELS

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.

1. Dent A.L. The Relation Between Self-Regulated Learning and Academic Achievement Across Childhood and Adolescence: A Meta-Analysis / A.L. Dent, A.C. Koenka // Educational Psychology Review. – 2016. – № 28(3). – Р. 425-474. https://orcid.org/10.1007/s10648-015-9320-8.

2. Mammadov S. Big Five Personality Traits and Academic Performance: A Meta-Analysis / S. Mammadov // Journal of Personality. – 2022. – № 90(2). – Р. 222-255. https://orcid.org/10.1111/jopy.12663.

3. Berlanga V. Impact of Scholarships on University Academic Performance: A Comparative Analysis of Students With and Without Scholarships / V. Berlanga, F. Corti // Frontiers in Education. – 2025. – № 10. – Р. 1554073. https://orcid.org/10.3389/feduc.2025.1554073.

4. Targeted Scholarship for Higher Education and Academic Performance: Evidence from Indonesia / T. Mulyaningsih et al // International Journal of Educational Development. – 2022. – № 88. – Р. 102510. https://orcid.org/10.1016/j.ijedudev.2021.102510.

5. Impact of Online Learning on Student’s Performance and Engagement: A Systematic Review / C.N. Akpen et al // Discover Education. – 2024. – № 3. – Р. 205. https://orcid.org/10.1007/s44217-024-00253-0.

6. Emergency Remote Teaching and Students’ Academic Performance in Higher Education during the COVID-19 Pandemic: A Case Study / S. Iglesias-Pradas et al // Computers in Human Behavior. – 2021. – № 119. – Р. 106713. https://orcid.org/10.1016/j.chb.2021.106713.

7. Witteveen D.P. The STEM Grading Penalty: An Alternative to the «Leaky Pipeline» Hypothesis / D. Witteveen P. Attewell // Science Education. – 2020. – № 104(4). – Р. 714-735. https://orcid.org/10.1002/sce.21580.

8. A Meta-Analysis of the Efficacy of Self-Regulated Learning Interventions on Academic Achievement in Online and Blended Environments in K-12 and Higher Education / Z. Xu et al // Behaviour & Information Technology. – 2023. – № 42(16). – Р. 2911-2931. https://orcid.org/10.1080/0144929X.2022.2151935.

9. Application of Student’s t-Test, Analysis of Variance, and Covariance / Р. Mishra et al // Annals of Cardiac Anaesthesia. – 2019. – № 22(4). – Р. 407-411. https://orcid.org/10.4103/aca.ACA_94_19.

10. Post-Hoc Tests in One-Way ANOVA: The Case for Normal Distribution / J. Juarros-Basterretxea et al // Methodology. – 2024. – № 20(2). – Р. 84-99. https://orcid.org/10.5964/meth.11721.