Assignments as Influential Factor to Improve the Prediction of Student Performance in Online Courses

Studies on the prediction of student success in distance learning have explored mainly demographics factors and student interactions with the virtual learning environments. However, it is remarkable that a very limited number of studies use information about the assignments submitted by students as...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Aurora Esteban, Cristóbal Romero, Amelia Zafra
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	Multiple Instance Learning educational data mining OULAD virtual learning system predicting performance Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/489849b8fbb1446c8d11a2663dea2cfb
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:489849b8fbb1446c8d11a2663dea2cfb
record_format	dspace
spelling	oai:doaj.org-article:489849b8fbb1446c8d11a2663dea2cfb2021-11-11T15:12:28ZAssignments as Influential Factor to Improve the Prediction of Student Performance in Online Courses10.3390/app1121101452076-3417https://doaj.org/article/489849b8fbb1446c8d11a2663dea2cfb2021-10-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/21/10145https://doaj.org/toc/2076-3417Studies on the prediction of student success in distance learning have explored mainly demographics factors and student interactions with the virtual learning environments. However, it is remarkable that a very limited number of studies use information about the assignments submitted by students as influential factor to predict their academic achievement. This paper aims to explore the real importance of assignment information for solving students’ performance prediction in distance learning and evaluate the beneficial effect of including this information. We investigate and compare this factor and its potential from two information representation approaches: the traditional representation based on single instances and a more flexible representation based on Multiple Instance Learning (MIL), focus on handle weakly labeled data. A comparative study is carried out using the Open University Learning Analytics dataset, one of the most important public datasets in education provided by one of the greatest online universities of United Kingdom. The study includes a wide set of different types of machine learning algorithms addressed from the two data representation commented, showing that algorithms using only information about assignments with a representation based on MIL can outperform more than 20% the accuracy with respect to a representation based on single instance learning. Thus, it is concluded that applying an appropriate representation that eliminates the sparseness of data allows to show the relevance of a factor, such as the assignments submitted, not widely used to date to predict students’ academic performance. Moreover, a comparison with previous works on the same dataset and problem shows that predictive models based on MIL using only assignments information obtain competitive results compared to previous studies that include other factors to predict students performance.Aurora EstebanCristóbal RomeroAmelia ZafraMDPI AGarticleMultiple Instance Learningeducational data miningOULADvirtual learning systempredicting performanceTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10145, p 10145 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Multiple Instance Learning educational data mining OULAD virtual learning system predicting performance Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	Multiple Instance Learning educational data mining OULAD virtual learning system predicting performance Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Aurora Esteban Cristóbal Romero Amelia Zafra Assignments as Influential Factor to Improve the Prediction of Student Performance in Online Courses
description	Studies on the prediction of student success in distance learning have explored mainly demographics factors and student interactions with the virtual learning environments. However, it is remarkable that a very limited number of studies use information about the assignments submitted by students as influential factor to predict their academic achievement. This paper aims to explore the real importance of assignment information for solving students’ performance prediction in distance learning and evaluate the beneficial effect of including this information. We investigate and compare this factor and its potential from two information representation approaches: the traditional representation based on single instances and a more flexible representation based on Multiple Instance Learning (MIL), focus on handle weakly labeled data. A comparative study is carried out using the Open University Learning Analytics dataset, one of the most important public datasets in education provided by one of the greatest online universities of United Kingdom. The study includes a wide set of different types of machine learning algorithms addressed from the two data representation commented, showing that algorithms using only information about assignments with a representation based on MIL can outperform more than 20% the accuracy with respect to a representation based on single instance learning. Thus, it is concluded that applying an appropriate representation that eliminates the sparseness of data allows to show the relevance of a factor, such as the assignments submitted, not widely used to date to predict students’ academic performance. Moreover, a comparison with previous works on the same dataset and problem shows that predictive models based on MIL using only assignments information obtain competitive results compared to previous studies that include other factors to predict students performance.
format	article
author	Aurora Esteban Cristóbal Romero Amelia Zafra
author_facet	Aurora Esteban Cristóbal Romero Amelia Zafra
author_sort	Aurora Esteban
title	Assignments as Influential Factor to Improve the Prediction of Student Performance in Online Courses
title_short	Assignments as Influential Factor to Improve the Prediction of Student Performance in Online Courses
title_full	Assignments as Influential Factor to Improve the Prediction of Student Performance in Online Courses
title_fullStr	Assignments as Influential Factor to Improve the Prediction of Student Performance in Online Courses
title_full_unstemmed	Assignments as Influential Factor to Improve the Prediction of Student Performance in Online Courses
title_sort	assignments as influential factor to improve the prediction of student performance in online courses
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/489849b8fbb1446c8d11a2663dea2cfb
work_keys_str_mv	AT auroraesteban assignmentsasinfluentialfactortoimprovethepredictionofstudentperformanceinonlinecourses AT cristobalromero assignmentsasinfluentialfactortoimprovethepredictionofstudentperformanceinonlinecourses AT ameliazafra assignmentsasinfluentialfactortoimprovethepredictionofstudentperformanceinonlinecourses
_version_	1718436647900545024

Assignments as Influential Factor to Improve the Prediction of Student Performance in Online Courses

Ejemplares similares