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Machine learning methods in predicting the student academic motivation (CROSBI ID 248433)

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Đurđević Babić, Ivana Machine learning methods in predicting the student academic motivation // Croatian operational research review, 8 (2017), 2; 443-461. doi: 10.17535/crorr.2017.0028

Podaci o odgovornosti

Đurđević Babić, Ivana

engleski

Machine learning methods in predicting the student academic motivation

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.

academic motivation, machine learning, neural networks, decision tree, support vector machine

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Podaci o izdanju

8 (2)

2017.

443-461

objavljeno

1848-0225

1848-9931

10.17535/crorr.2017.0028

Povezanost rada

Informacijske i komunikacijske znanosti

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