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


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GÜRCAN F. , ÖZYURT Ö. , Cagiltay N. E.

INTERNATIONAL REVIEW OF RESEARCH IN OPEN AND DISTRIBUTED LEARNING, vol.22, no.2, pp.1-17, 2021 (Journal Indexed in SSCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 22 Issue: 2
  • Publication Date: 2021
  • Doi Number: 10.19173/irrodl.v22i2.5358
  • Title of Journal : INTERNATIONAL REVIEW OF RESEARCH IN OPEN AND DISTRIBUTED LEARNING
  • Page Numbers: pp.1-17

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.