Academic Performance Modelling with Machine Learning Based on Cognitive and Non-Cognitive Features Cover

.blurhash-client-img { display: none !important; }

Academic Performance Modelling with Machine Learning Based on Cognitive and Non-Cognitive Features

Applied Computer Systems

Volume 26 (2021): Issue 2 (December 2021)

By: Bridgitte Owusu-Boadu, Isaac Kofi Nti , Owusu Nyarko-Boateng , Justice Aning and Victoria Boafo

Open Access

|Dec 2021

Authors

Bridgitte Owusu-Boadu

bink.hope@gmail.com

Brivink Consult & Technology, Sunyani, Ghana

Isaac Kofi Nti

Isaac.nti@uenr.edu.gh

Department of Computer Science and Informatics, University of Energy and Natural Resources, Sunyani, Ghana

Owusu Nyarko-Boateng

owusu.nyarko-boateng@uenr.edu.gh

Department of Computer Science and Informatics, University of Energy and Natural Resources, Sunyani, Ghana

Justice Aning

aning421@gmail.com

Department of Computer Science, Sunyani Technical University, Sunyani, Ghana

Victoria Boafo

Department of Computer Science & ICT, Mampong Technical College of Education, Mampong, Ghana

DOI: https://doi.org/10.2478/acss-2021-0015 | Journal eISSN: 2255-8691 | Journal ISSN: 2255-8683

Journal RSS Feed

Language: English

Page range: 122 - 131

Published on: Dec 30, 2021

Published by: Riga Technical University

In partnership with: Paradigm Publishing Services

Publication frequency: Volume open

Keywords:

Academic performance,

artificial neural network,

educational data mining,

k-nearest neighbour,

logistic regression,

machine learning,

support vector machine

Related subjects:

Computer sciences,

Artificial intelligence,

Information technology,

Project management,

Software development

© 2021 Bridgitte Owusu-Boadu, Isaac Kofi Nti, Owusu Nyarko-Boateng, Justice Aning, Victoria Boafo, published by Riga Technical University
This work is licensed under the Creative Commons Attribution 4.0 License.

Previous article Volume 26 (2021): Issue 2 (December 2021)Next article