+91-9561854529 editorinchief.ijama@gmail.com submitpaper@ijama.in Contact Form

Empirical Study of Student Performance Prediction Using Machine Learning Models

Author(s):Dr. Rameshwar Prasad Tiwari

Affiliation: Department of Computer Science and Data Analytics, Madhav Institute of Technology and Science, Gwalior, Madhya Pradesh, India

Page No: 4-7

Volume issue & Publishing Year: Volume 3, Issue 2, Feb 2026

published on: 2026/02/14

Journal: International Journal of Advanced Multidisciplinary Application.(IJAMA)

ISSN NO: 3048-9350

DOI: https://doi.org/10.5281/zenodo.18649012

Download PDF

Article Indexing:

Abstract:
Predicting student academic performance has become an important research area due to increasing dropout rates, academic stress, and the need for personalized learning systems. Educational institutions generate large volumes of student data related to attendance, assessment scores, learning behavior, and demographic characteristics. Machine Learning (ML) techniques provide effective tools for analyzing such data and predicting student performance outcomes. This study presents a mathematical and empirical investigation of student performance prediction using supervised machine learning models. A comprehensive dataset containing academic, behavioral, and demographic attributes was analyzed using regression and classification techniques. Mathematical formulations of prediction models were developed, and performance was evaluated using statistical accuracy measures. The results indicate that machine learning models significantly improve prediction accuracy and can support early academic intervention and personalized education strategies

Keywords: Student Performance Prediction, Machine Learning, Educational Data Mining, Mathematical Modeling, Academic Analytics

Reference:

  • [1] C. Romero and S. Ventura, “Educational data mining,” IEEE Transactions on Systems, Man, and Cybernetics, 2010.
  • [2] R. Baker and K. Yacef, “The state of educational data mining,” Educational Data Mining, 2009.
  • [3] T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning, Springer, 2017.
  • [4] S. Alpaydin, Introduction to Machine Learning, MIT Press, 2020.
  • [5] UGC, Digital Learning Analytics in Higher Education, Government of India, 2022.