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

dc.authorscopusid 57194776706
dc.authorscopusid 35614751000
dc.authorscopusid 16237826800
dc.contributor.author Gurcan,F.
dc.contributor.author Ozyurt,O.
dc.contributor.author Cagiltay,N.E.
dc.date.accessioned 2024-09-10T21:35:47Z
dc.date.available 2024-09-10T21:35:47Z
dc.date.issued 2021
dc.department Atılım University en_US
dc.department-temp Gurcan F., Distance Education Application and Research Centre, Karadeniz Technical University, Trabzon, Turkey; Ozyurt O., Karadeniz Technical University, OF Technology Faculty, Software Engineering Department, Trabzon, Turkey; Cagiltay N.E., Software Engineering Department, Atilim University, Ankara, Turkey en_US
dc.description.abstract 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 “elearning 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. © 2021. International Review of Research in Open and Distance Learning. All Rights Reserved. en_US
dc.identifier.citationcount 40
dc.identifier.doi 10.19173/irrodl.v22i2.5358
dc.identifier.endpage 18 en_US
dc.identifier.issn 1492-3831
dc.identifier.issue 2 en_US
dc.identifier.scopus 2-s2.0-85107655935
dc.identifier.scopusquality Q1
dc.identifier.startpage 1 en_US
dc.identifier.uri https://doi.org/10.19173/irrodl.v22i2.5358
dc.identifier.volume 22 en_US
dc.language.iso en en_US
dc.relation.ispartof International Review of Research in Open and Distributed Learning en_US
dc.relation.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/openAccess en_US
dc.scopus.citedbyCount 52
dc.subject developmental stages en_US
dc.subject e-learning en_US
dc.subject text mining en_US
dc.subject topic modeling en_US
dc.subject trends en_US
dc.title Investigation of Emerging Trends in the E-Learning Field Using Latent Dirichlet Allocation en_US
dc.type Article en_US
dspace.entity.type Publication

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