Improved fuzzy modelling to predict the academic performance of distance education students

It is essential to predict distance education students’ year-end academic performance early during the course of the semester and to take precautions using such prediction-based information. This will, in particular, help enhance their academic performance and, therefore, improve the overall educat...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Osman Yildiz, Abdullah Bal, Sevinc Gulsecen
Formato:	article
Lenguaje:	EN
Publicado:	Athabasca University Press 2013
Materias:	Distance Education academic performance fuzzy logic genetic algorithm online learning Special aspects of education LC8-6691
Acceso en línea:	https://doaj.org/article/70021fe3179f440f8d7ac37921db7f4f
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:70021fe3179f440f8d7ac37921db7f4f
record_format	dspace
spelling	oai:doaj.org-article:70021fe3179f440f8d7ac37921db7f4f2021-12-02T19:20:54ZImproved fuzzy modelling to predict the academic performance of distance education students10.19173/irrodl.v14i5.15951492-3831https://doaj.org/article/70021fe3179f440f8d7ac37921db7f4f2013-12-01T00:00:00Zhttp://www.irrodl.org/index.php/irrodl/article/view/1595https://doaj.org/toc/1492-3831 It is essential to predict distance education students’ year-end academic performance early during the course of the semester and to take precautions using such prediction-based information. This will, in particular, help enhance their academic performance and, therefore, improve the overall educational quality. The present study was on the development of a mathematical model intended to predict distance education students’ year-end academic performance using the first eight-week data on the learning management system. First, two fuzzy models were constructed, namely the classical fuzzy model and the expert fuzzy model, the latter being based on expert opinion. Afterwards, a gene-fuzzy model was developed optimizing membership functions through genetic algorithm. The data on distance education were collected through Moodle, an open source learning management system. The data were on a total of 218 students who enrolled in Basic Computer Sciences in 2012. The input data consisted of the following variables: When a student logged on to the system for the last time after the content of a lesson was uploaded, how often he/she logged on to the system, how long he/she stayed online in the last login, what score he/she got in the quiz taken in Week 4, and what score he/she got in the midterm exam taken in Week 8. A comparison was made among the predictions of the three models concerning the students’ year-end academic performance. Osman YildizAbdullah BalSevinc GulsecenAthabasca University PressarticleDistance Educationacademic performancefuzzy logicgenetic algorithmonline learningSpecial aspects of educationLC8-6691ENInternational Review of Research in Open and Distributed Learning, Vol 14, Iss 5 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Distance Education academic performance fuzzy logic genetic algorithm online learning Special aspects of education LC8-6691
spellingShingle	Distance Education academic performance fuzzy logic genetic algorithm online learning Special aspects of education LC8-6691 Osman Yildiz Abdullah Bal Sevinc Gulsecen Improved fuzzy modelling to predict the academic performance of distance education students
description	It is essential to predict distance education students’ year-end academic performance early during the course of the semester and to take precautions using such prediction-based information. This will, in particular, help enhance their academic performance and, therefore, improve the overall educational quality. The present study was on the development of a mathematical model intended to predict distance education students’ year-end academic performance using the first eight-week data on the learning management system. First, two fuzzy models were constructed, namely the classical fuzzy model and the expert fuzzy model, the latter being based on expert opinion. Afterwards, a gene-fuzzy model was developed optimizing membership functions through genetic algorithm. The data on distance education were collected through Moodle, an open source learning management system. The data were on a total of 218 students who enrolled in Basic Computer Sciences in 2012. The input data consisted of the following variables: When a student logged on to the system for the last time after the content of a lesson was uploaded, how often he/she logged on to the system, how long he/she stayed online in the last login, what score he/she got in the quiz taken in Week 4, and what score he/she got in the midterm exam taken in Week 8. A comparison was made among the predictions of the three models concerning the students’ year-end academic performance.
format	article
author	Osman Yildiz Abdullah Bal Sevinc Gulsecen
author_facet	Osman Yildiz Abdullah Bal Sevinc Gulsecen
author_sort	Osman Yildiz
title	Improved fuzzy modelling to predict the academic performance of distance education students
title_short	Improved fuzzy modelling to predict the academic performance of distance education students
title_full	Improved fuzzy modelling to predict the academic performance of distance education students
title_fullStr	Improved fuzzy modelling to predict the academic performance of distance education students
title_full_unstemmed	Improved fuzzy modelling to predict the academic performance of distance education students
title_sort	improved fuzzy modelling to predict the academic performance of distance education students
publisher	Athabasca University Press
publishDate	2013
url	https://doaj.org/article/70021fe3179f440f8d7ac37921db7f4f
work_keys_str_mv	AT osmanyildiz improvedfuzzymodellingtopredicttheacademicperformanceofdistanceeducationstudents AT abdullahbal improvedfuzzymodellingtopredicttheacademicperformanceofdistanceeducationstudents AT sevincgulsecen improvedfuzzymodellingtopredicttheacademicperformanceofdistanceeducationstudents
_version_	1718376743087112192

Improved fuzzy modelling to predict the academic performance of distance education students

Ejemplares similares