• Register
  • Login

European Journal of Molecular & Clinical Medicine

  • Home
  • Browse
    • Current Issue
    • By Issue
    • By Subject
    • Keyword Index
    • Author Index
    • Indexing Databases XML
  • Journal Info
    • About Journal
    • Aims and Scope
    • Editorial Board
    • Publication Ethics
    • Indexing and Abstracting
    • Peer Review Process
    • News
  • Guide for Authors
  • Submit Manuscript
  • Contact Us
Advanced Search

Notice

As part of Open Journals’ initiatives, we create website for scholarly open access journals. If you are responsible for this journal and would like to know more about how to use the editorial system, please visit our website at https://ejournalplus.com or
send us an email to info@ejournalplus.com

We will contact you soon

  1. Home
  2. Volume 7, Issue 2
  3. Authors

Online ISSN: 2515-8260

Volume7, Issue2

COMPARATIVE STUDY OF MACHINE LEARNING ALGORITHMS FOR EVALUATING STUDENT ACADEMIC PERFORMANCE

    Kanchan Sanyal, Dulal Kumbhakar, Sunil Karforma Mudassar Abdullah Nik Adzrieman B. Abd Rahman

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 2, Pages 6806-6814

  • Show Article
  • Download
  • Cite
  • Statistics
  • Share

Abstract

Machine learning (ML) is transforming education and fundamentally changing teaching, learning and research. The ML technique helps the institution to utilize the resources in better ways and produces results in the best possible effective manner. The learning combines various processes like data preparation, classification, association, building models, training, clustering, prediction etc. to improve performance of students. It helps to the students to select any particular course based on their choices and previous performances. The main focus of this study is to analyze the various classification techniques over the educational data. The comparative study was conducted to predict the student performances based on some social variables (extracurricular activities, family education support, and desire for the higher education), previous exam grades and along with other attributes. In this paper, the Naive Bayes(NB) , Bayes Network(BN), Radial Bias Function (RBF), Multi-Layer Perceptron (MLP), Back Propagation Network(BPN), Random Forest(RF), J48, Radial Basis Function Network (RBFN)  classification techniques were chosen for the experiment. After testing all the data over the mentioned classification we found that correctly classified instances percentage is 100% for Random Forest and it is highest compared to other classification algorithms.
Keywords:
  • PDF (715 K)
  • XML
(2020). COMPARATIVE STUDY OF MACHINE LEARNING ALGORITHMS FOR EVALUATING STUDENT ACADEMIC PERFORMANCE. European Journal of Molecular & Clinical Medicine, 7(2), 6806-6814.
Kanchan Sanyal, Dulal Kumbhakar, Sunil Karforma; Mudassar Abdullah Nik Adzrieman B. Abd Rahman. "COMPARATIVE STUDY OF MACHINE LEARNING ALGORITHMS FOR EVALUATING STUDENT ACADEMIC PERFORMANCE". European Journal of Molecular & Clinical Medicine, 7, 2, 2020, 6806-6814.
(2020). 'COMPARATIVE STUDY OF MACHINE LEARNING ALGORITHMS FOR EVALUATING STUDENT ACADEMIC PERFORMANCE', European Journal of Molecular & Clinical Medicine, 7(2), pp. 6806-6814.
COMPARATIVE STUDY OF MACHINE LEARNING ALGORITHMS FOR EVALUATING STUDENT ACADEMIC PERFORMANCE. European Journal of Molecular & Clinical Medicine, 2020; 7(2): 6806-6814.
  • RIS
  • EndNote
  • BibTeX
  • APA
  • MLA
  • Harvard
  • Vancouver
  • Article View: 58
  • PDF Download: 106
  • LinkedIn
  • Twitter
  • Facebook
  • Google
  • Telegram
Journal Information

Publisher:

Email:  info@ejmcm.com

  • Home
  • Glossary
  • News
  • Aims and Scope
  • Privacy Policy
  • Sitemap

Editorial Team:  editor@ejmcm.com

For Special Issue Proposal : chiefeditor.ejmcm@gmail.com / info@ejmcm.com

This journal is licensed under a Creative Commons Attribution 4.0 International (CC-BY 4.0)

Powered by eJournalPlus