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

dc.authorscopusid57194776706
dc.authorscopusid35614751000
dc.authorscopusid16237826800
dc.contributor.authorGurcan,F.
dc.contributor.authorOzyurt,O.
dc.contributor.authorCagiltay,N.E.
dc.date.accessioned2024-07-05T15:46:09Z
dc.date.available2024-07-05T15:46:09Z
dc.date.issued2021
dc.departmentAtılım Universityen_US
dc.department-tempGurcan 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, Turkeyen_US
dc.description.abstractE-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.citation38
dc.identifier.doi10.19173/irrodl.v22i2.5358
dc.identifier.endpage18en_US
dc.identifier.issn1492-3831
dc.identifier.issue2en_US
dc.identifier.scopusqualityQ1
dc.identifier.startpage1en_US
dc.identifier.urihttps://doi.org/10.19173/irrodl.v22i2.5358
dc.identifier.urihttps://hdl.handle.net/20.500.14411/4023
dc.identifier.volume22en_US
dc.language.isoenen_US
dc.relation.ispartofInternational Review of Research in Open and Distributed Learningen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectdevelopmental stagesen_US
dc.subjecte-learningen_US
dc.subjecttext miningen_US
dc.subjecttopic modelingen_US
dc.subjecttrendsen_US
dc.titleInvestigation of Emerging Trends in the E-Learning Field Using Latent Dirichlet Allocationen_US
dc.typeArticleen_US
dspace.entity.typePublication

Files

Collections