Тип публикации: статья из журнала
Год издания: 2022
Идентификатор DOI: 10.2478/cait-2022-0008
Ключевые слова: bayesian network, early warning system, k-nearest neighbors, learning analytics, learning failure, learning success, linear discriminant analysis, student-at-risk
Аннотация: The article is focused on the problem of early prediction of students' learning failures with the purpose of their possible prevention by timely introducing supportive measures. We propose an approach to designing a predictive model for an academic course or module taught in a blended learning format. We introduce certain requiremeПоказать полностьюnts to predictive models concerning their applicability to the educational process such as interpretability, actionability, and adaptability to a course design. We test three types of classifiers meeting these requirements and choose the one that provides best performance starting from the early stages of the semester, and therefore provides various opportunities to timely support at-risk students. Our empirical studies confirm that the proposed approach is promising for the development of an early warning system in a higher education institution. Such systems can positively influence student retention rates and enhance learning and teaching experience for a long term. © 2022 Tatiana A. Kustitskaya et al., published by Sciendo.
Журнал: Cybernetics and Information Technologies
Выпуск журнала: Vol. 22, Is. 1
Номера страниц: 117-133
ISSN журнала: 13119702
Издатель: Sciendo