Student’s Academic Performance Prediction Based on Machine Learning Regression Models

DOI

10.2991/978-94-6463-300-9_29How to use a DOI?

Keywords

Machine Learning; Student Performance Prediction; Random Forest Algorithm

Abstract

The utilization of machine learning methods for predicting student grades has emerged as a valuable approach to assessing the educational advancement of academic institutions, driven by the rapid evolution of these techniques. While prior studies have predominantly encompassed data from various facets, this research specifically focuses on forecasting students’ academic performance based solely on their past scores. The dataset employed in this study is obtained from Kaggle and comprises grades attained by college students majoring in computer science. Four distinct machine learning models, including linear regression, support vector regression, k-nearest neighbors, and random forest, are employed to predict the students’ scores using regression techniques. Notably, the paper streamlines the problem through data preprocessing, initially eliminating missing data, and subsequently applies the aforementioned models to predict student performance. Furthermore, the parameters are adjusted to get the best performance. From the outcome in this study, random forest is proved as the best model to predict the student’s grades in this dataset. This work finally shows predicting student’s future progress using the four models just by its past scores was acceptable and reasonable, also gives some possible reasons for the different outcomes from diverge algorithms.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - Sijian Lyu
PY  - 2023
DA  - 2023/11/27
TI  - Student’s Academic Performance Prediction Based on Machine Learning Regression Models
BT  - Proceedings of the 2023 International Conference on Image, Algorithms and Artificial Intelligence (ICIAAI 2023)
PB  - Atlantis Press
SP  - 293
EP  - 299
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-300-9_29
DO  - 10.2991/978-94-6463-300-9_29
ID  - Lyu2023
ER  -