The Science and Information (SAI) Organization
  • Home
  • About Us
  • Journals
  • Conferences
  • Contact Us

Publication Links

  • IJACSA
  • Author Guidelines
  • Publication Policies
  • Digital Archiving Policy
  • Promote your Publication
  • Metadata Harvesting (OAI2)

IJACSA

  • About the Journal
  • Call for Papers
  • Editorial Board
  • Author Guidelines
  • Submit your Paper
  • Current Issue
  • Archives
  • Indexing
  • Fees/ APC
  • Reviewers
  • Apply as a Reviewer

IJARAI

  • About the Journal
  • Archives
  • Indexing & Archiving

Special Issues

  • Home
  • Archives
  • Proposals
  • Guest Editors
  • SUSAI-EE 2025
  • ICONS-BA 2025
  • IoT-BLOCK 2025

Future of Information and Communication Conference (FICC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Computing Conference

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Intelligent Systems Conference (IntelliSys)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Future Technologies Conference (FTC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact
  • Home
  • Call for Papers
  • Editorial Board
  • Guidelines
  • Submit
  • Current Issue
  • Archives
  • Indexing
  • Fees
  • Reviewers
  • Subscribe

DOI: 10.14569/IJACSA.2022.0130558
PDF

Study on Feature Engineering and Ensemble Learning for Student Academic Performance Prediction

Author 1: Du Xiaoming
Author 2: Chen Ying
Author 3: Zhang Xiaofang
Author 4: Guo Yu

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 5, 2022.

  • Abstract and Keywords
  • How to Cite this Article
  • {} BibTeX Source

Abstract: Student academic performance prediction is one of the important works in the teaching management, which can realize accurate management, scientific teaching and personalized learning by mining important features affecting the academic performance and accurately predicting academic. Due to the subjectivity of feature extraction and the randomness of hyperparameters, the accuracy of academic performance prediction needs to be improved. Therefore, in order to improve the accuracy of prediction, an academic prediction method based on Feature Engineering and ensemble learning is proposed, which makes full use of the advantages of random forest in feature extraction and the ability of XGBoost in prediction. Firstly, the feature importance is calculated and ranked by using the random forest method, and the optimal feature subset combined with the forward search strategy. Secondly, the optimal feature subset is input into the XGBoost model for prediction. The sparrow search algorithm is used to optimize the XGBoost hyperparameters to further improve the accuracy of academic prediction. Finally, the performance of the proposed method is verified through the experiments of the public data set. The experimental results show that the academic prediction method designed is better than the single learner prediction method and other integrated learning prediction methods. The accuracy result jumps to 82.4%. It has good prediction performance and can provide support for teachers to teach according to students’ aptitude.

Keywords: Academic performance prediction; feature engineering; ensemble learning; random forest; XGBoost

Du Xiaoming, Chen Ying, Zhang Xiaofang and Guo Yu, “Study on Feature Engineering and Ensemble Learning for Student Academic Performance Prediction” International Journal of Advanced Computer Science and Applications(IJACSA), 13(5), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130558

@article{Xiaoming2022,
title = {Study on Feature Engineering and Ensemble Learning for Student Academic Performance Prediction},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130558},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130558},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {5},
author = {Du Xiaoming and Chen Ying and Zhang Xiaofang and Guo Yu}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

IJACSA

Upcoming Conferences

IntelliSys 2025

28-29 August 2025

  • Amsterdam, The Netherlands

Future Technologies Conference 2025

6-7 November 2025

  • Munich, Germany

Healthcare Conference 2026

21-22 May 2026

  • Amsterdam, The Netherlands

Computing Conference 2026

9-10 July 2026

  • London, United Kingdom

IntelliSys 2026

3-4 September 2026

  • Amsterdam, The Netherlands

Computer Vision Conference 2026

15-16 October 2026

  • Berlin, Germany
The Science and Information (SAI) Organization
BACK TO TOP

Computer Science Journal

  • About the Journal
  • Call for Papers
  • Submit Paper
  • Indexing

Our Conferences

  • Computing Conference
  • Intelligent Systems Conference
  • Future Technologies Conference
  • Communication Conference

Help & Support

  • Contact Us
  • About Us
  • Terms and Conditions
  • Privacy Policy

© The Science and Information (SAI) Organization Limited. All rights reserved. Registered in England and Wales. Company Number 8933205. thesai.org