Assessing Academic Achievement in Adaptive 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
Skanda Moda Gururajarao, Valentina Everstova, Elena Potekhina, Elmira Moydinova, Alexander Nikulushkin, Nataļja Muračova

Adaptive learning has the potential to provide effective and efficient teaching of university students, preparing them to become highly qualified professionals. By considering current achievements along with the individual characteristics of students, it allows for the organization of the learning process in an optimal way. As a result of adaptive learning, students acquire all the knowledge and skills outlined in the study program. This paper proposes a multiplicative model that takes into account the maximum, minimum and optimal levels of student’s learning proficiency in the implementation of adaptive learning. The proposed model enables prediction the level of learning proficiency and adapts the learning process based on the individual characteristics of each student. The research results have significant theoretical and practical importance for the implementation of adaptive learning in universities and improving the efficiency and objectivity of the educational process.

Keywords
Adaptive Learning; Assessment; Identification; Modeling; Testing
DOI
10.1007/978-3-032-00239-6_31
Hyperlink
https://link.springer.com/chapter/10.1007/978-3-032-00239-6_31

Gururajarao, S., Everstova, V., Potekhina, E., Moydinova, E., Nikulushkin, A., Muračova, N. Assessing Academic Achievement in Adaptive 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 Science and Business Media Deutschland GmbH, 2025, pp.445-453. ISBN 978-3-032-00238-9. e-ISBN 978-3-032-00239-6. ISSN 2367-3370. e-ISSN 2367-3389. Available from: doi:10.1007/978-3-032-00239-6_31

Publication language
English (en)