Comparative analysis of machine learning algorithms for predicting student success and enhancing their educational outcomes
- Title
- Comparative analysis of machine learning algorithms for predicting student success and enhancing their educational outcomes
- Creator
- Sourav, Mayur Kumar; Gupta, Varuna
- Description
- The primary objective of this study is to predict the performance of students and evaluate the efficacy of various machine learning algorithms in predicting student success based on their marks and grades (academic factors). Through a comprehensive review of literature and experimentation, this research compares the performance of different machine learning models, including but not limited to decision trees, random forests, support vector machines, logistic regression, and neural networks. The evaluation metrics considered in this comparative analysis include accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC). Fourteen experiments have been performed and preliminary results suggest that performances of students on the basis of academic factors might be predictable and by understanding the strengths and weaknesses of student's educational outcomes and foster student achievement can be improved. Through extensive experimentation and comparative analysis, XGBoost(ExtremeGradient Boosting) and AdaBoost demonstrated as the most effective predictive models to analyze the students' performance. 2025 Author(s).
- Source
- AIP Conference Proceedings;Volume;3297;Issue;1;Article No.;286502;
- Date
- 01-01-2025
- Publisher
- American Institute of Physics
- Coverage
- Sourav M.K., Christ University, Bangalore, India; Gupta V., Christ University, Bangalore, India
- Rights
- Restricted Access; Hardcopy may be available in the library
- Relation
- ISSN: 0094243X;
- Format
- online
- Language
- English
- Type
- Conference paper
Collection
Citation
Sourav, Mayur Kumar; Gupta, Varuna, “Comparative analysis of machine learning algorithms for predicting student success and enhancing their educational outcomes,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/25716.