Identification of Students at Risk of Not Completing the Course Using Machine Learning
| dc.contributor.author | Bairamova D. | |
| dc.date.accessioned | 2024-12-12T06:11:16Z | |
| dc.date.available | 2024-12-12T06:11:16Z | |
| dc.date.issued | 2023 | |
| dc.description.abstract | This dissertation is dedicated to the topic of identifying students at risk of not successfully completing a course at an early stage of their education using machine learning algorithms. In this study, the final exam score, academic performance category, and the risk group of students failing to complete the course are determined for accurate and detailed identification of students at risk. Research shows that machine learning algorithms such as LightGBM Regressor (for the final exam score prediction), Logistic Regression (for identifying two groups of students - those who will complete and those who will not complete the course), and K-Means (for identifying the academic category) can help identify students who need assistance from teachers with high accuracy of prediction. Detecting this group of students at an early stage of their education can enhance students’ motivation for further learning and assist teachers in individually and timely identifying which student requires help. | |
| dc.identifier.uri | https://repository.sdu.edu.kz/handle/123456789/1572 | |
| dc.language.iso | en | |
| dc.subject | students, education, machine learning algorithms, LightGBM Regressor | |
| dc.title | Identification of Students at Risk of Not Completing the Course Using Machine Learning | |
| dc.type | Other |