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.2020.0110532
PDF

A New Approach to Predicting Learner Performance with Reduced Forgetting

Author 1: Dagou Dangui Augustin Sylvain Legrand KOFFI
Author 2: Tchimou N’TAKPE
Author 3: Assohoun ADJE
Author 4: Souleymane OUMTANAGA

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 5, 2020.

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

Abstract: The work on predicting learner performance allows researchers through machine learning methods to participate in the improvement of e-learning. This improvement allows, little by little, e-learning to be promoted and adopted by several educational structures around the world. Neural networks, widely used in various performance prediction works, have made several exploits. However, factors that are highly influential in the field of learning have not been explored in machine learning models. For this reason, our study attempts to show the importance of the forgetting factor in the learning system. Thus, to contribute to the improvement of accuracy in performance predictions. The interest being to draw the attention of researchers in this field to very influential factors that are not exploited. Our model takes into account the study of the forgetting factor in neural networks. The objective is to show the importance of attenuation the forgetting, on the quality of performance predictions in e-learning. Our model is compared to those based on Random Forest and linear regression algorithms. The results of our study show first that neural networks (95.20%) are better than Random Forest (95.15%) and linear regression (93.80%). Then, with the attenuation of forgetting, these algorithms give 96.63%, 95.85% and 93.80% respectively. This work allowed us to show the great relevance of oblivion in neural networks. Thus, the exploration of other unexploited factors will make better performance prediction models.

Keywords: Performance prediction; e-learning; artificial neural networks; forgetting factor

Dagou Dangui Augustin Sylvain Legrand KOFFI, Tchimou N’TAKPE, Assohoun ADJE and Souleymane OUMTANAGA, “A New Approach to Predicting Learner Performance with Reduced Forgetting” International Journal of Advanced Computer Science and Applications(IJACSA), 11(5), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110532

@article{KOFFI2020,
title = {A New Approach to Predicting Learner Performance with Reduced Forgetting},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110532},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110532},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {5},
author = {Dagou Dangui Augustin Sylvain Legrand KOFFI and Tchimou N’TAKPE and Assohoun ADJE and Souleymane OUMTANAGA}
}



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