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