Predicting Students' Final Performance Using Artificial Neural Networks

22Citations
Citations of this article
312Readers
Mendeley users who have this article in their library.

Abstract

Artificial Intelligence (AI) is based on algorithms that allow machines to make decisions for humans. This technology enhances the users' experience in various ways. Several studies have been conducted in the field of education to solve the problem of student orientation and performance using various Machine Learning (ML) algorithms. The main goal of this article is to predict Moroccan students' performance in the region of Guelmim Oued Noun using an intelligent system based on neural networks, one of the best data mining techniques that provided us with the best results.

References Powered by Scopus

An overview and comparison of supervised data mining techniques for student exam performance prediction

281Citations
N/AReaders
Get full text

Recommender system for predicting student performance

238Citations
N/AReaders
Get full text

Educational data mining: Predictive analysis of academic performance of public school students in the capital of Brazil

218Citations
N/AReaders
Get full text

Cited by Powered by Scopus

Techniques and applications of Machine Learning and Artificial Intelligence in education: a systematic review

28Citations
N/AReaders
Get full text

An Overview of the Security Challenges in IoT Environment

26Citations
N/AReaders
Get full text

The accuracy of machine learning models relies on hyperparameter tuning: student result classification using random forest, randomized search, grid search, bayesian, genetic, and optuna algorithms

24Citations
N/AReaders
Get full text

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Cite

CITATION STYLE

APA

Ahajjam, T., Moutaib, M., Aissa, H., Azrour, M., Farhaoui, Y., & Fattah, M. (2022). Predicting Students’ Final Performance Using Artificial Neural Networks. Big Data Mining and Analytics, 5(4), 294–301. https://doi.org/10.26599/BDMA.2021.9020030

Readers' Seniority

Tooltip

PhD / Post grad / Masters / Doc 36

46%

Lecturer / Post doc 20

25%

Professor / Associate Prof. 15

19%

Researcher 8

10%

Readers' Discipline

Tooltip

Computer Science 68

76%

Engineering 10

11%

Social Sciences 6

7%

Business, Management and Accounting 5

6%

Save time finding and organizing research with Mendeley

Sign up for free