Academic Performance Prediction Using Machine Learning Algorithms

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

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

Academic Performance Prediction Using Machine Learning Algorithms

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

Qiannan Normal College for Nationalities
Nanchang Institute of Science and Technology
University of Bolton
University of Hassan II Casablanca

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

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, Random Forest with 94.9% accuracy was the best prediction algorithm.

Original language	English
Title of host publication	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
Editors	Celestine Iwendi, Zakaria Boulouard, Natalia Kryvinska
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	361-372
Number of pages	12
ISBN (Print)	9783031371639
DOIs	https://doi.org/10.1007/978-3-031-37164-6_27
State	Published - 2023
Externally published	Yes
Event	International Conference on Advances in Communication Technology and Computer Engineering, ICACTCE 2023 - Bolton, United Kingdom Duration: 24 Feb 2023 → 25 Feb 2023

Publication series

Name	Lecture Notes in Networks and Systems
Volume	735 LNNS
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	International Conference on Advances in Communication Technology and Computer Engineering, ICACTCE 2023
Country/Territory	United Kingdom
City	Bolton
Period	24/02/23 → 25/02/23

Keywords

Academic performance
Decision tree
K-NN
Machine learning
Multilayer perceptron
Naïve bayes
Prediction
Random forest
SVM

Access to Document

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

Cite this

Hai, T., Zhou, J., Abolfath Zadeh, S., Adedayo, A. O., Gan, S. F., Iwendi, C., & Boulouard, Z. (2023). Academic Performance Prediction Using Machine Learning Algorithms. In C. Iwendi, Z. Boulouard, & N. Kryvinska (Eds.), 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; Vol. 735 LNNS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-37164-6_27

Hai, Tao ; Zhou, Jincheng ; Abolfath Zadeh, Shirin et al. / Academic Performance Prediction Using Machine Learning Algorithms. 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. editor / Celestine Iwendi ; Zakaria Boulouard ; Natalia Kryvinska. Springer Science and Business Media Deutschland GmbH, 2023. pp. 361-372 (Lecture Notes in Networks and Systems).

@inproceedings{120a11d0a60a4320b08da8b954d54f77,

title = "Academic Performance Prediction Using Machine Learning Algorithms",

abstract = "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, Na{\"i}ve 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, Random Forest with 94.9\% accuracy was the best prediction algorithm.",

keywords = "Academic performance, Decision tree, K-NN, Machine learning, Multilayer perceptron, Na{\"i}ve bayes, Prediction, Random forest, SVM",

author = "Tao Hai and Jincheng Zhou and \{Abolfath Zadeh\}, Shirin and Adedayo, \{Afolake O.\} and Gan, \{S. F.\} and Celestine Iwendi and Zakaria Boulouard",

note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; International Conference on Advances in Communication Technology and Computer Engineering, ICACTCE 2023 ; Conference date: 24-02-2023 Through 25-02-2023",

year = "2023",

doi = "10.1007/978-3-031-37164-6\_27",

language = "English",

isbn = "9783031371639",

series = "Lecture Notes in Networks and Systems",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "361--372",

editor = "Celestine Iwendi and Zakaria Boulouard and Natalia Kryvinska",

booktitle = "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",

address = "Germany",

}

Hai, T, Zhou, J, Abolfath Zadeh, S, Adedayo, AO, Gan, SF, Iwendi, C & Boulouard, Z 2023, Academic Performance Prediction Using Machine Learning Algorithms. in C Iwendi, Z Boulouard & N Kryvinska (eds), 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. Lecture Notes in Networks and Systems, vol. 735 LNNS, Springer Science and Business Media Deutschland GmbH, pp. 361-372, International Conference on Advances in Communication Technology and Computer Engineering, ICACTCE 2023, Bolton, United Kingdom, 24/02/23. https://doi.org/10.1007/978-3-031-37164-6_27

Academic Performance Prediction Using Machine Learning Algorithms. / Hai, Tao; Zhou, Jincheng; Abolfath Zadeh, Shirin et al.
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. ed. / Celestine Iwendi; Zakaria Boulouard; Natalia Kryvinska. Springer Science and Business Media Deutschland GmbH, 2023. p. 361-372 (Lecture Notes in Networks and Systems; Vol. 735 LNNS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Academic Performance Prediction Using Machine Learning Algorithms

AU - Hai, Tao

AU - Zhou, Jincheng

AU - Abolfath Zadeh, Shirin

AU - Adedayo, Afolake O.

AU - Gan, S. F.

AU - Iwendi, Celestine

AU - Boulouard, Zakaria

PY - 2023

Y1 - 2023

N2 - 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, Random Forest with 94.9% accuracy was the best prediction algorithm.

AB - 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, Random Forest with 94.9% accuracy was the best prediction algorithm.

KW - Academic performance

KW - Decision tree

KW - K-NN

KW - Machine learning

KW - Multilayer perceptron

KW - Naïve bayes

KW - Prediction

KW - Random forest

KW - SVM

UR - https://www.scopus.com/pages/publications/85174502803

U2 - 10.1007/978-3-031-37164-6_27

DO - 10.1007/978-3-031-37164-6_27

M3 - Conference contribution

AN - SCOPUS:85174502803

SN - 9783031371639

T3 - Lecture Notes in Networks and Systems

SP - 361

EP - 372

BT - 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

A2 - Iwendi, Celestine

A2 - Boulouard, Zakaria

A2 - Kryvinska, Natalia

PB - Springer Science and Business Media Deutschland GmbH

T2 - International Conference on Advances in Communication Technology and Computer Engineering, ICACTCE 2023

Y2 - 24 February 2023 through 25 February 2023

ER -

Hai T, Zhou J, Abolfath Zadeh S, Adedayo AO, Gan SF, Iwendi C et al. Academic Performance Prediction Using Machine Learning Algorithms. In Iwendi C, Boulouard Z, Kryvinska N, editors, 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. Springer Science and Business Media Deutschland GmbH. 2023. p. 361-372. (Lecture Notes in Networks and Systems). doi: 10.1007/978-3-031-37164-6_27

Academic Performance Prediction Using Machine Learning Algorithms

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this