Santoso, Leo Willyanto and Yulia, (2020) Predicting student performance in higher education using multi-regression models. [UNSPECIFIED]
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Abstract
Supporting the goal of higher education to produce graduation who will be a professional leader is a crucial. Most of universities implement intelligent information system (IIS) to support in achieving their vision and mission. One of the features of IIS is student performance prediction. By implementing data mining model in IIS, this feature could precisely predict the student� grade for their enrolled subjects. Moreover, it can recognize at-risk students and allow top educational management to take educative interventions in order to succeed academically. In this research, multi-regression model was proposed to build model for every student. In our model, Learning Management System (LMS) activity logs were computed. Based on the testing result on big students datasets, courses, and activities indicates that these models could improve the accuracy of prediction model by over 15%.
| Item Type: | UNSPECIFIED |
|---|---|
| Uncontrolled Keywords: | Data mining, Education, Multi-regression, Prediction, Student |
| Subjects: | Q Science > QA Mathematics > QA76 Computer software |
| Divisions: | Faculty of Industrial Technology > Informatics Engineering Department |
| Depositing User: | Admin |
| Date Deposited: | 10 Mar 2020 19:03 |
| Last Modified: | 11 Jan 2023 16:15 |
| URI: | https://repository.petra.ac.id/id/eprint/18688 |
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