A Mobile Application for Early Prediction of Student Performance Using Fuzzy Logic and Artificial Neural Networks
Dublin Core
Title
A Mobile Application for Early Prediction of Student Performance Using Fuzzy Logic and Artificial Neural Networks
Subject
A mobile App
Predictive systems
Fuzzy algorithm
Neural Network
Peda-gogy
Description
Identifying students at risk or potentials excellent students is increasingly important for higher education institutions to meet the needs of the students and develop efficient learning strategy. Early stage prediction can give an indication of the students’ performance during their study years. This helps tailoring an appropriate learning strategy for different groups.This work develops a novel framework for a mobile app to predict the students’ performance before starting the Universities’ education. The framework is built on a University’s students data from year 2009-2017. It has three main components, namely, a neural network model that predicts the GPA, a mobile App that tests basic knowledge in different domains, and a fuzzy model that estimates the future students’ performance.
Creator
Nosseir, Ann
Fathy, Yahia
Source
International Journal of Interactive Mobile Technologies (iJIM); Vol. 14 No. 02 (2020); pp. 4-18
1865-7923
Publisher
International Association of Online Engineering (IAOE), Vienna, Austria
Date
2020-02-10
Rights
Copyright (c) 2020 Ann Nosseir, Yahia Fathy
Relation
Format
application/pdf
Language
eng
Type
info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
Identifier
Citation
Ann Nosseir and Yahia Fathy, A Mobile Application for Early Prediction of Student Performance Using Fuzzy Logic and Artificial Neural Networks, International Association of Online Engineering (IAOE), Vienna, Austria, 2020, accessed November 6, 2024, https://igi.indrastra.com/items/show/1501