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

The International Review of Research in Open and Distributed Learning

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Field Value
 
Title Investigation of Emerging Trends in the E-Learning Field Using Latent Dirichlet Allocation
 
Creator Gurcan, Fatih
Ozyurt, Ozcan
Cagitay, Nergiz Ercil
 
Subject e-learning
text-mining
topic modeling
trends
developmental stages
 
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.
 
Publisher Athabasca University Press
 
Date 2021-01-14
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed, double-blind
 
Format text/html
application/epub+zip
application/pdf
 
Identifier http://www.irrodl.org/index.php/irrodl/article/view/5358
10.19173/irrodl.v22i2.5358
 
Source The International Review of Research in Open and Distributed Learning; Vol. 22 No. 2 (2021); 1-18
1492-3831
 
Language eng
 
Relation http://www.irrodl.org/index.php/irrodl/article/view/5358/5506
http://www.irrodl.org/index.php/irrodl/article/view/5358/5514
http://www.irrodl.org/index.php/irrodl/article/view/5358/5515
 
Rights http://creativecommons.org/licenses/by/4.0
 

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