Implementing Machine Learning Techniques for Predicting Student Performance in an E-Learning Environment

IJIIS : International Journal of Informatics and Information Systems

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Field Value
 
Title Implementing Machine Learning Techniques for Predicting Student Performance in an E-Learning Environment
 
Creator Paramita, Adi Suryaputra
Tjahjono, Laura Mahendratta
 
Subject E-Learning, Data Mining, Machine Learning, Student Performance
 
Description The pandemic of COVID-19 has altered the way people learn. Learning has moved from offline to online throughout this pandemic. Predicting student performance based on relevant data has opened up a new field for educational institutions to improve teaching and learning processes, as well as course curriculum adjustments. Machine learning technology can assist universities in forecasting student performance so that necessary changes in lecture delivery and curriculum can be made. The performance of the pupils was predicted using machine learning techniques in this research. Open University (OU) educational data is examined. Demographic, engagement, and performance metrics are used. The results of the experiment. The k-NN strategy outperformed all other algorithms on the OU dataset in some circumstances, but the ANN approach outperformed them all in others.
 
Publisher Bright Publisher
 
Contributor
 
Date 2021-09-20
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
 
Format application/pdf
 
Identifier http://ijiis.org/index.php/IJIIS/article/view/112
10.47738/ijiis.v4i2.112
 
Source International Journal of Informatics and Information Systems; Vol 4, No 2: September 2021; 149-156
2579-7069
10.47738/ijiis.v4i2
 
Language eng
 
Relation http://ijiis.org/index.php/IJIIS/article/view/112/69
 
Rights Copyright (c) 2021 International Journal of Informatics and Information Systems
https://creativecommons.org/licenses/by-sa/4.0
 

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