Estimating Student’s Motivation Level from Accumulated Data in The Context of Learning
| dc.contributor.author | Abdimaulenov Zh. | |
| dc.date.accessioned | 2024-12-20T04:58:43Z | |
| dc.date.available | 2024-12-20T04:58:43Z | |
| dc.date.issued | 2022 | |
| dc.description.abstract | The motivation of students to perform academically is closely linked to their academic performance. Teachers should identify students with low academic motivation as early as possible, just as they should identify those with an extremely high level of motivation. The purpose of this article is to identify the influence of academic motivation on behavior in learning management systems (LMS) courses as a means of developing a classification model to predict students’ academic motivation. The research was conducted by students studying on the online platform eduway.kz. The classifiers were created using machine learning methods (ANN, decision trees aud SVM). To determine if there was a significant difference between the models, a t-test was conducted. While all classifier models were successful in predicting motivation based on students’ behavior in a learning management system course, the neural network model had the highest success rate. | |
| dc.identifier.uri | https://repository.sdu.edu.kz/handle/123456789/1600 | |
| dc.language.iso | en | |
| dc.subject | motivation, management, neural network, success rate | |
| dc.title | Estimating Student’s Motivation Level from Accumulated Data in The Context of Learning | |
| dc.type | Other |