<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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 requirements 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.</p>
</abstract>

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 requirements 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.

Early Student-at-Risk Detection by Current Learning Performance and Learning Behavior Indicators

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

The article is focused on the problem of early prediction of students’ learning failures with the purpose of their possible prevention by timely introducing...

Early Student-at-Risk Detection by Current Learning Performance and Learning Behavior Indicators

Online veröffentlicht: 10. Apr. 2022

Seitenbereich: 117 - 133

Eingereicht: 02. Dez. 2021

Akzeptiert: 21. Feb. 2022

DOI: https://doi.org/10.2478/cait-2022-0008

Schlüsselwörter
Learning analytics, Learning success, Learning failure, Student-at-risk, Early warning system, Bayesian network, k-Nearest Neighbors, Linear discriminant analysis

© 2022 Tatiana A. Kustitskaya et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Early Student-at-Risk Detection by Current Learning Performance and Learning Behavior Indicators

Online veröffentlicht: 10. Apr. 2022

Seitenbereich: 117 - 133

Eingereicht: 02. Dez. 2021

Akzeptiert: 21. Feb. 2022

DOI: https://doi.org/10.2478/cait-2022-0008

SchlüsselwörterLearning analytics, Learning success, Learning failure, Student-at-risk, Early warning system, Bayesian network, k-Nearest Neighbors, Linear discriminant analysis

© 2022 Tatiana A. Kustitskaya et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
Learning analytics, Learning success, Learning failure, Student-at-risk, Early warning system, Bayesian network, k-Nearest Neighbors, Linear discriminant analysis