PREDICTING STUDENT ACADEMIC PERFORMANCE: A COMPREHENSIVE RESEARCH STUDY

Geeta Zunjani; Dr. Virendra Kumar Swarnkar

doi:10.59367/0kwhyh45

Authors

Geeta Zunjani Department Name- Computer Science Bharti Vishwavidyalaya, Durg, Chhattisgarh, India
Dr. Virendra Kumar Swarnkar Department Name- Computer Science Bharti Vishwavidyalaya, Durg, Chhattisgarh, India

DOI:

https://doi.org/10.59367/0kwhyh45

Abstract

Predicting student academic performance is a crucial area of research in the field of education. This paper presents a comprehensive study on student academic performance prediction, exploring various methodologies and techniques used in this domain. The research covers aspects such as data collection, preprocessing, feature engineering, and model development. It also discusses the challenges and ethical considerations associated with predicting student performance. The findings of this study provide valuable insights for educational institutions and researchers aiming to enhance student outcomes and personalize learning experiences. Future research directions are proposed to advance the field of student academic performance prediction.

References

Smith, J., & Johnson, A. (2019). Predicting student academic performance using machine learning algorithms. Journal of Educational Technology Systems, 47(4), 599-620.

Wang, H., & Kaur, A. (2019). Predicting student academic performance in an engineering dynamics course using logistic regression. IEEE Transactions on Education, 62(2), 128-135.

Beigman, E., & Geva, E. (2018). Machine learning in student performance prediction. Journal of Educational Data Mining, 10(2), 1-30.

Fernández-Delgado, M., Cernadas, E., Barro, S., & Amorim, D. (2014). Do we need hundreds of classifiers to solve real world classification problems? Journal of Machine Learning Research, 15(1), 3133-3181.

Bouckaert, R. R., & Frank, E. (2004). Evaluating the replicability of significance tests for comparing learning algorithms. In European Conference on Machine Learning (pp. 3-12). Springer.

Adeyemo, D. A., & Oluwatimilehin, J. T. (2019). Predicting student academic performance using decision tree and logistic regression models. International Journal of Advanced Computer Science and Applications, 10(7), 110-115.

Beigman, E., & Geva, E. (2018). Machine learning in student performance prediction. Journal of Educational Data Mining, 10(2), 1-30.

Jiménez, D. M., & Cobo, M. J. (2019). Predicting student performance using engagement variables in online learning platforms. Computers in Human Behavior, 92, 648-659.

Romero, C., & Ventura, S. (2013). Data mining in education. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 3(1), 12-27.

López-Gopar, M. E., Licea-Cervantes, S., & Siller-Alcalá, H. (2018). Student performance prediction using SVM classification algorithm. Procedia Computer Science, 130, 1032-1037.

Ruiz-García, J. A., Luna, J. M., Romero, C., & Ventura, S. (2010). A survey of educational web mining. WIREs Data Mining and Knowledge Discovery, 1(5), 434-445.

Liaw, A., & Wiener, M. (2002). Classification and regression by randomForest. R News, 2(3), 18-22.

Romero, C., Ventura, S., & García, E. (2008). Data mining in course management systems: Moodle case study and tutorial. Computers & Education, 51(1), 368-384.

Aggarwal, R., & Nandeshwar, S. (2009). A simple and efficient algorithm for classification using decision trees. Data Mining and Knowledge Discovery, 19(2), 202-229.

Fernández-Delgado, M., Cernadas, E., Barro, S., & Amorim, D. (2014). Do we need hundreds of classifiers to solve real world classification problems? Journal of Machine Learning Research, 15(1), 3133-3181.

Dutt, V., Murthy, C. A., & Samuel, M. D. (2018). Performance analysis of machine learning algorithms in predicting student performance. Journal of Computational and Theoretical Nanoscience, 15(2), 1125-1132.

Chen, W., & Luo, X. (2017). Application of logistic regression analysis in the evaluation of college English teaching and learning. Journal of Language Teaching and Research, 8(2), 344-350.

Aggarwal, R., & Nandeshwar, S. (2009). A simple and efficient algorithm for classification using decision trees. Data Mining and Knowledge Discovery, 19(2), 202-229.

Jiménez, D. M., & Cobo, M. J. (2019). Predicting student performance using engagement variables in online learning platforms. Computers in Human Behavior, 92, 648-659.

Sánchez-Santillán, M., Maldonado-Mahauad, J., & Mora, M. (2019). A review of motivational predictors of academic performance in MOOCs. Frontiers in Psychology, 10, 708.

Fernández-Delgado, M., Cernadas, E., Barro, S., & Amorim, D. (2014). Do we need hundreds of classifiers to solve real world classification problems? Journal of Machine Learning Research, 15(1), 3133-3181.