Predicting Student Academic Success |
Author(s): |
| Rohit Channappa Hutagonna , Sinhgad College Of Engineering; Manisha Kumari, Sinhgad College Of Engineering; Rakshak Bhat, Sinhgad College Of Engineering; Harshit Pande, Sinhgad College Of Engineering |
Keywords: |
| Machine Learning, SVM Classifier, Feature Extraction, Pre-Processing |
Abstract |
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Performance degradation assessment (PDA) is of great significance to ensure safety and availability of mechanical equipment. As an important issue of PDA, the robustness of the trained model directly affects the assessment efficiency and restricts its application in practice. This paper examines how features affect student persistence or dropout at South African higher education institutions, based on three previous studies. In the previous studies, high school grades were used as a valid predictor of student success. The quality of a high school’s learning environment has an effect on almost every aspect of higher education success. In this paper, we attempt to pro- vide a data-driven solution to the data-congested environment of at- tributes related to student success and contribute towards preventing the increased dropout rates at South African higher education institutions. |
Other Details |
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Paper ID: IJSRDV11I30029 Published in: Volume : 11, Issue : 3 Publication Date: 01/06/2023 Page(s): 29-31 |
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