Prediction of Student Academic Performance in Higher Education Institutions Using Data Mining

Kishori Kasat, Naim Shaikh, Meenakshi, Khaled A. Z. Alyamani

2023

Abstract

In recent years, the analysis of student performance has emerged as one of the most significant research foci in the domains of Educational Data Mining (EDM) and Learning Analytics (LA). Because of this, a number of schools have started utilising EDM and LA to make forecasts about their students’ potential for future academic success. This gives instructors and school administrators the ability to track the development of each student and take corrective action before it is too late. This article presents a model for Prediction of student academic performance in higher education institutions using data mining. This methodology section consists of data acquisition and classification phases. Data acquisition includes collecting student data on the basis of predefined attributes and classification is performed using Naïve bayes, linear regression and random forest methods. Random Forest algorithm is predicting student performance more accurately.

Paper Citation

in Harvard Style

Kasat K., Shaikh N., Meenakshi. and Alyamani K. (2023). Prediction of Student Academic Performance in Higher Education Institutions Using Data Mining. In Proceedings of the 1st International Conference on Artificial Intelligence for Internet of Things: Accelerating Innovation in Industry and Consumer Electronics - Volume 1: AI4IoT; ISBN 978-989-758-661-3, SciTePress, pages 651-655. DOI: 10.5220/0012615000003739

in Bibtex Style

@conference{ai4iot23,
author={Kishori Kasat and Naim Shaikh and Meenakshi and Khaled A. Z. Alyamani},
title={Prediction of Student Academic Performance in Higher Education Institutions Using Data Mining},
booktitle={Proceedings of the 1st International Conference on Artificial Intelligence for Internet of Things: Accelerating Innovation in Industry and Consumer Electronics - Volume 1: AI4IoT},
year={2023},
pages={651-655},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012615000003739},
isbn={978-989-758-661-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Artificial Intelligence for Internet of Things: Accelerating Innovation in Industry and Consumer Electronics - Volume 1: AI4IoT
TI - Prediction of Student Academic Performance in Higher Education Institutions Using Data Mining
SN - 978-989-758-661-3
AU - Kasat K.
AU - Shaikh N.
AU - Meenakshi.
AU - Alyamani K.
PY - 2023
SP - 651
EP - 655
DO - 10.5220/0012615000003739
PB - SciTePress