TY - JOUR AU - Gurcan, Fatih AU - Ozyurt, Ozcan AU - Cagitay, Nergiz Ercil PY - 2021/01/14 Y2 - 2024/03/29 TI - Investigation of Emerging Trends in the E-Learning Field Using Latent Dirichlet Allocation JF - The International Review of Research in Open and Distributed Learning JA - IRRODL VL - 22 IS - 2 SE - Research Articles DO - 10.19173/irrodl.v22i2.5358 UR - https://www.irrodl.org/index.php/irrodl/article/view/5358 SP - 1-18 AB - <p class="3">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-<span style="color: #010000;">19</span> 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 <span style="color: #010000;">41</span>,<span style="color: #010000;">925</span> peer-reviewed journal articles published between <span style="color: #010000;">2000</span> and <span style="color: #010000;">2019</span>. The analysis revealed <span style="color: #010000;">16</span> 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.</p> ER -