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.2020.0110960
PDF

The Most Efficient Classifiers for the Students’ Academic Dataset

Author 1: Ebtehal Ibrahim Al-Fairouz
Author 2: Mohammed Abdullah Al-Hagery

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 9, 2020.

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

Abstract: Educational institutions contain a vast collection of data accumulated for years, so it is difficult to use this data to solve problems related to the progress of the educational process and also contribute to achieving quality. For this reason, the use of data mining techniques helps to extract hidden knowledge that helps in making the decisions necessary to develop education and achieve quality requirements. The data of this study obtained from the College of Business and Economics at Qassim University. Three of the classifiers were compared in this study Decision Tree, Random Forest and Naïve Bayes. The results showed that Random Forest outperforms other algorithms with 71.5% of Precision, 71.2% F1-score, and also it got 71.3% of Recall and Classification Accuracy (CA). This study helps reduce failure by providing an academic advisor to students who have weaknesses in achieving a high-Grade Point Average (GPA). It also helps in developing the educational process by discovering and overcoming weaknesses.

Keywords: Data mining; student performance; classification algorithms; evaluation

Ebtehal Ibrahim Al-Fairouz and Mohammed Abdullah Al-Hagery, “The Most Efficient Classifiers for the Students’ Academic Dataset” International Journal of Advanced Computer Science and Applications(IJACSA), 11(9), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110960

@article{Al-Fairouz2020,
title = {The Most Efficient Classifiers for the Students’ Academic Dataset},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110960},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110960},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {9},
author = {Ebtehal Ibrahim Al-Fairouz and Mohammed Abdullah Al-Hagery}
}



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