International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 6 Issue 6 November-December 2024 Submit your research before last 3 days of December to publish your research paper in the issue of November-December.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 6 Issue 6
November-December 2024

Submit your research paper

Indexing Partners
Academia Advanced Sciences Index Bielefeld Academic Search Engine CiteSeer DRJI Google Scholar Independent Search Engine & Directory Network ISI (International Scientific Indexing) Issuu Mendeley Research Networks
RefSeek ResearcherId - Thomson Reuters ResearchGate Scirus Scribd Semantic Scholar UTeM - Universiti Teknikal Malaysia Melaka Wiki for Call for Papers WorldCat Zenodo

Enhancing Academic Success Prediction: An Ensemble Approach

Author(s) Ashok M V, Safira Begum
Country India
Abstract The process of extracting meaning and knowledge from large volumes of data is termed as Data mining. It also refers to the way of inferring information from databases, which can be used in diverse fields including educational domain. Educational data mining plays a key role in finding ways to discover knowledge from data in the education sector. Educational Data Mining has evolved as a significant element in prediction of students’ academic performance. The most important goal of the paper is to analyze and evaluate the engineering students’ performance by applying stacking, an ensemble method in orange tool. Ensemble Stacking combines several base classifier models in order to create one optimal prediction model. Engineering students’ dataset was used to build predictive model using traditional classifiers SVM, Logistic Regression, Naïve Bayes and then stacking technique was implemented. The results showed that the proposed stacking technique obtains a high performance, which has a superior result compared to the other base classifiers techniques. Therefore, conclusion could be reached that the stacking performance is better than that of different algorithms.
Keywords educational data mining, classification, ensemble method, stacking
Field Computer Applications
Published In Volume 6, Issue 2, March-April 2024
Published On 2024-04-25
Cite This Enhancing Academic Success Prediction: An Ensemble Approach - Ashok M V, Safira Begum - IJFMR Volume 6, Issue 2, March-April 2024. DOI 10.36948/ijfmr.2024.v06i02.18163
DOI https://doi.org/10.36948/ijfmr.2024.v06i02.18163
Short DOI https://doi.org/gtsg68
View / Download PDF File

Share this



E-ISSN 2582-2160
CrossRef

CrossRef DOI is assigned to each research paper published in our journal.

IJFMR DOI prefix is
10.36948/ijfmr

Downloads
Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 6 Isu 6Cover Page Vol 6 Isu 5Cover Page Vol 6 Isu 4

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA Open Access

About IJFMR
Fees & Payment
Current Issue
Publication Archive 		Submit Research Paper
Track Submission Status
Publication Guidelines
Publication Ethics
Peer Review & Plagiarism 		Join as a Reviewer
Editors & Reviewers
Reviewer Referral Program
Get Reviewer Membership Certi. 		Website/Journal Policies
Usage Policy
Content Policies
Privacy Policy
Contact Us +91-9898-5948-55 editor@ijfmr.com