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https://doi.org/10.17535/crorr.2017.0028

Machine learning methods in predicting the student academic motivation

Ivana Đurđević Babić ; Faculty of Education, University of Osijek, Cara Hadrijana 10, 31 000 Osijek, Croatia

Puni tekst: engleski, pdf (1 MB) str. 443-461 preuzimanja: 975* citiraj
APA 6th Edition
Đurđević Babić, I. (2017). Machine learning methods in predicting the student academic motivation. Croatian Operational Research Review, 8 (2), 443-461. https://doi.org/10.17535/crorr.2017.0028
MLA 8th Edition
Đurđević Babić, Ivana. "Machine learning methods in predicting the student academic motivation." Croatian Operational Research Review, vol. 8, br. 2, 2017, str. 443-461. https://doi.org/10.17535/crorr.2017.0028. Citirano 18.09.2020.
Chicago 17th Edition
Đurđević Babić, Ivana. "Machine learning methods in predicting the student academic motivation." Croatian Operational Research Review 8, br. 2 (2017): 443-461. https://doi.org/10.17535/crorr.2017.0028
Harvard
Đurđević Babić, I. (2017). 'Machine learning methods in predicting the student academic motivation', Croatian Operational Research Review, 8(2), str. 443-461. https://doi.org/10.17535/crorr.2017.0028
Vancouver
Đurđević Babić I. Machine learning methods in predicting the student academic motivation. Croatian Operational Research Review [Internet]. 2017 [pristupljeno 18.09.2020.];8(2):443-461. https://doi.org/10.17535/crorr.2017.0028
IEEE
I. Đurđević Babić, "Machine learning methods in predicting the student academic motivation", Croatian Operational Research Review, vol.8, br. 2, str. 443-461, 2017. [Online]. https://doi.org/10.17535/crorr.2017.0028

Sažetak
Academic motivation is closely related to academic performance. For educators, it is equally important to detect early students with a lack of academic motivation as it is to detect those with a high level of academic motivation. In endeavouring to develop a classification model for predicting student academic motivation based on their behaviour in learning management system (LMS) courses, this paper intends to establish links between the predicted student academic motivation and their behaviour in the LMS course. Students from all years at the Faculty of Education in Osijek participated in this research. Three machine learning classifiers (neural networks, decision trees, and support vector machines) were used. To establish whether a significant difference in the performance of models exists, a t-test of the difference in proportions was used. Although, all classifiers were successful, the neural network model was shown to be the most successful in detecting the student academic motivation based on their behaviour in LMS course.

Ključne riječi
academic motivation; machine learning; neural networks; decision tree; support; vector machine

Hrčak ID: 193542

URI
https://hrcak.srce.hr/193542

Posjeta: 1.217 *