Applying Statistical Methods to Analyze Students’ Academic Performance in E-learning
Software Engineering: Emerging Trends and Practices in System Development: Proceedings of 14th Computer Science On-line Conference 2025. Vol.1. Lecture Notes in Networks and Systems. Vol.1558 2025
Alexander Pupkov, Oleg Ikonnikov, Roman Kuzmich, Svetlana Kapustina, Alina Kataeva, Alexander Nikulushkin, Nataļja Muračova

E-learning provides unique opportunities for analyzing various aspects of the educational process. This paper investigates the impact of students’ time spent on an educational platform on their final course scores. It was hypothesized that imposing strict limits on students’ access time to educational material posted on the educational platform would enhance their discipline, motivation, and self-organization. Statistical methods, including Student's t-test, Shapiro-Wilk test, and Levene's test, were used to analyze the data. Furthermore, a correlation analysis was conducted, which revealed a significant relationship between the time spent on the educational platform and academic performance. Additionally, a linear regression was calculated to show the dependency between the time students spent on the educational platform and their final course scores. As a result of this research, the hypothesis of a positive effect of time constraints on student academic performance was rejected. The findings suggest that absence of strict limits enables students to allocate time more efficiently, resulting in higher scores. The study’s results and conclusions can assist university teachers and administrators in developing educational strategies that consider the individual needs of students and the specific characteristics of e-learning.


Keywords
Academic Performance; Correlation; E-learning; Linear Regression; Statistical Analysis; Student Motivation; T-Test; Time Management
DOI
10.1007/978-3-032-00712-4_28
Hyperlink
https://link.springer.com/chapter/10.1007/978-3-032-00712-4_28

Pupkov, A., Ikonnikov, O., Kuzmich, R., Kapustina, S., Kataeva, A., Nikulushkin, A., Muračova, N. Applying Statistical Methods to Analyze Students’ Academic Performance in E-learning. In: Software Engineering: Emerging Trends and Practices in System Development: Proceedings of 14th Computer Science On-line Conference 2025. Vol.1. Lecture Notes in Networks and Systems. Vol.1558, Russia, Moscow, 1-3 April, 2025. Cham: Springer, 2025, pp.413-423. ISBN 978-3-032-00711-7. e-ISBN 978-3-032-00712-4. ISSN 2367-3370. e-ISSN 2367-3389. Available from: doi:10.1007/978-3-032-00712-4_28

Publication language
English (en)
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