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

Bibliographic Data

ID21729341
AuthorsFatih Gurcan (0000-0001-9915-6686, Karadeniz Technical University), Ozcan Ozyurt (0000-0002-0047-6813, Karadeniz Technical University), Nergiz Ercil Cagitay (Atilim University)
Year2021
Volume22
Issue2
Pages1-18
Publication date2021-01-14
Open AccessYes
TypeARTICLE
VenueThe International Review of Research in Open and Distributed Learning (JOURNAL)
Journal identifiersISSN: 1492-3831 • E-ISSN: 1492-3831
PublisherAthabasca University Press (PUBLISHER • CA)
DOI10.19173/irrodl.v22i2.5358
OpenAlexW3164796821
LanguageEN
Citations received8
References cited6

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

Adaptive Learning · Data science · Educational technology · Latent Dirichlet allocation · Machine learning · Mathematics education · Topic model · Advanced Text Analysis Techniques · Computer Science · Mathematics · Online Learning and Analytics · Psychology · Recommender Systems and Techniques · Artificial Intelligence

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