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

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