Academic performance prediction using machine learning algorithms

Tao Hai; Jincheng Zhou; Shirin Abolfath Zadeh; Afolake O. Adedayo; S.F. Gan; Celestine Iwendi; Z. Boulouard

doi:10.1007/978-3-031-37164-6_27

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Academic performance prediction using machine learning algorithms

Conference proceeding

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Academic performance prediction using machine learning algorithms

Tao Hai, Jincheng Zhou, Shirin Abolfath Zadeh, Afolake O. Adedayo, S.F. Gan, Celestine Iwendi and Z. Boulouard

Proceedings of ICACTCE'23 — The International Conference on Advances in Communication Technology and Computer Engineering New Artificial Intelligence and the Internet of Things Based Perspective and Solutions, pp.361-372

Lecture Notes in Networks and Systems, 735

ICACTCE23 - International Conference on Advances in Communication Technology and Computer Engineering (Bolton, United Kingdom, 24/02/2023–25/02/2023)

24/09/2023

DOI: https://doi.org/10.1007/978-3-031-37164-6_27

Abstract

academic performance

prediction

multilayer perceptron

Random Forest

SVM

Naïve Bayes

Decision Tree

K-NN

Machine Learning

The objective of the study is to use a method to predict student performance during the semesters and to compare accuracy perceptron for a dataset of student performance. In this regard, Machine Learning techniques were applied to the student performance dataset provided by the Kaggle.com website. Multilayer Perceptron, Random Forest, SVM, Naïve Bayes, Decision tree and K-NN algorithms were used to predict the Grade result of students as a factor of performance. The Student Performance dataset is used to forecast how well students will perform in their tests. As a result, with 94.9% accuracy, the results were predicted.

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Title: Academic performance prediction using machine learning algorithms
Creators - without role: Tao Hai
Jincheng Zhou - Qiannan Normal College For Nationalities
Shirin Abolfath Zadeh - University of Greater Manchester
Afolake O. Adedayo - University of Greater Manchester
S.F. Gan - Qiannan Normal College For Nationalities
Celestine Iwendi - University of Greater Manchester
Z. Boulouard - University of Hassan II Casablanca
Contributors: Celestine Iwendi (Editor) - University of Greater Manchester
Z. Boulouard (Editor) - University of Hassan II Casablanca
N. Kryvinska (Editor)
Publication Details: Proceedings of ICACTCE'23 — The International Conference on Advances in Communication Technology and Computer Engineering New Artificial Intelligence and the Internet of Things Based Perspective and Solutions, pp.361-372
Conference: ICACTCE23 - International Conference on Advances in Communication Technology and Computer Engineering (Bolton, United Kingdom, 24/02/2023–25/02/2023)
Series: Lecture Notes in Networks and Systems; 735
Publisher: Springer; Cham
Format: text
Identifiers: 999704808841; 2367-3370; 2367-3389; 9783031371639; 2367-3389
Copyright: This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/978-3-031-37164-6_27
Academic Unit: Computing; Games
Language: English
Resource Type: Conference proceeding
Comment: Paper given at and published in the Proceedings of ICACTCE23 24 - 25th February 2023, University of Bolton
Generative AI Statement: https://icactce-conf.com/

Academic performance prediction using machine learning algorithms

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