Investigation of Emerging Trends in the E-Learning Field Using Latent Dirichlet Allocation

E-learning studies are becoming very important today as they provide alternatives and support to all types of teaching and learning programs. The effect of the COVID-19 pandemic on educational systems has further increased the significance of e-learning. Accordingly, gaining a full understanding of...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Fatih Gurcan, Ozcan Ozyurt, Nergiz Ercil Cagitay
Formato:	article
Lenguaje:	EN
Publicado:	Athabasca University Press 2021
Materias:	e-learning text-mining topic modeling trends developmental stages Special aspects of education LC8-6691
Acceso en línea:	https://doaj.org/article/3188ed3c2e2840e9a0dd9bd9afcdbfc6
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:3188ed3c2e2840e9a0dd9bd9afcdbfc6
record_format	dspace
spelling	oai:doaj.org-article:3188ed3c2e2840e9a0dd9bd9afcdbfc62021-12-02T18:02:58ZInvestigation of Emerging Trends in the E-Learning Field Using Latent Dirichlet Allocation10.19173/irrodl.v22i2.53581492-3831https://doaj.org/article/3188ed3c2e2840e9a0dd9bd9afcdbfc62021-01-01T00:00:00Zhttp://www.irrodl.org/index.php/irrodl/article/view/5358https://doaj.org/toc/1492-3831E-learning studies are becoming very important today as they provide alternatives and support to all types of teaching and learning programs. The effect of the COVID-19 pandemic on educational systems has further increased the significance of e-learning. Accordingly, gaining a full understanding of the general topics and trends in e-learning studies is critical for a deeper comprehension of the field. There are many studies that provide such a picture of the e-learning field, but the limitation is that they do not examine the field as a whole. This study aimed to investigate the emerging trends in the e-learning field by implementing a topic modeling analysis based on latent Dirichlet allocation (LDA) on 41,925 peer-reviewed journal articles published between 2000 and 2019. The analysis revealed 16 topics reflecting emerging trends and developments in the e-learning field. Among these, the topics “MOOC,” “learning assessment,” and “e-learning systems” were found to be key topics in the field, with a consistently high volume. In addition, the topics of “learning algorithms,” “learning factors,” and “adaptive learning” were observed to have the highest overall acceleration, with the first two identified as having a higher acceleration in recent years. Going by these results, it is concluded that the next decade of e-learning studies will focus on learning factors and algorithms, which will possibly create a baseline for more individualized and adaptive mobile platforms. In other words, after a certain maturity level is reached by better understanding the learning process through these identified learning factors and algorithms, the next generation of e-learning systems will be built on individualized and adaptive learning environments. These insights could be useful for e-learning communities to improve their research efforts and their applications in the field accordingly. Fatih GurcanOzcan OzyurtNergiz Ercil CagitayAthabasca University Pressarticlee-learningtext-miningtopic modelingtrendsdevelopmental stagesSpecial aspects of educationLC8-6691ENInternational Review of Research in Open and Distributed Learning, Vol 22, Iss 2 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	e-learning text-mining topic modeling trends developmental stages Special aspects of education LC8-6691
spellingShingle	e-learning text-mining topic modeling trends developmental stages Special aspects of education LC8-6691 Fatih Gurcan Ozcan Ozyurt Nergiz Ercil Cagitay Investigation of Emerging Trends in the E-Learning Field Using Latent Dirichlet Allocation
description	E-learning studies are becoming very important today as they provide alternatives and support to all types of teaching and learning programs. The effect of the COVID-19 pandemic on educational systems has further increased the significance of e-learning. Accordingly, gaining a full understanding of the general topics and trends in e-learning studies is critical for a deeper comprehension of the field. There are many studies that provide such a picture of the e-learning field, but the limitation is that they do not examine the field as a whole. This study aimed to investigate the emerging trends in the e-learning field by implementing a topic modeling analysis based on latent Dirichlet allocation (LDA) on 41,925 peer-reviewed journal articles published between 2000 and 2019. The analysis revealed 16 topics reflecting emerging trends and developments in the e-learning field. Among these, the topics “MOOC,” “learning assessment,” and “e-learning systems” were found to be key topics in the field, with a consistently high volume. In addition, the topics of “learning algorithms,” “learning factors,” and “adaptive learning” were observed to have the highest overall acceleration, with the first two identified as having a higher acceleration in recent years. Going by these results, it is concluded that the next decade of e-learning studies will focus on learning factors and algorithms, which will possibly create a baseline for more individualized and adaptive mobile platforms. In other words, after a certain maturity level is reached by better understanding the learning process through these identified learning factors and algorithms, the next generation of e-learning systems will be built on individualized and adaptive learning environments. These insights could be useful for e-learning communities to improve their research efforts and their applications in the field accordingly.
format	article
author	Fatih Gurcan Ozcan Ozyurt Nergiz Ercil Cagitay
author_facet	Fatih Gurcan Ozcan Ozyurt Nergiz Ercil Cagitay
author_sort	Fatih Gurcan
title	Investigation of Emerging Trends in the E-Learning Field Using Latent Dirichlet Allocation
title_short	Investigation of Emerging Trends in the E-Learning Field Using Latent Dirichlet Allocation
title_full	Investigation of Emerging Trends in the E-Learning Field Using Latent Dirichlet Allocation
title_fullStr	Investigation of Emerging Trends in the E-Learning Field Using Latent Dirichlet Allocation
title_full_unstemmed	Investigation of Emerging Trends in the E-Learning Field Using Latent Dirichlet Allocation
title_sort	investigation of emerging trends in the e-learning field using latent dirichlet allocation
publisher	Athabasca University Press
publishDate	2021
url	https://doaj.org/article/3188ed3c2e2840e9a0dd9bd9afcdbfc6
work_keys_str_mv	AT fatihgurcan investigationofemergingtrendsintheelearningfieldusinglatentdirichletallocation AT ozcanozyurt investigationofemergingtrendsintheelearningfieldusinglatentdirichletallocation AT nergizercilcagitay investigationofemergingtrendsintheelearningfieldusinglatentdirichletallocation
_version_	1718378799061532672

Investigation of Emerging Trends in the E-Learning Field Using Latent Dirichlet Allocation

Ejemplares similares