Investigation of Emerging Trends in the E-Learning Field Using Latent Dirichlet Allocation
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.
Abramo, G., D’Angelo, C. A., & Caprasecca, A. (2009). Allocative efficiency in public research funding: Can bibliometrics help? Research Policy, 38(1), 206–215. https://doi.org/10.1016/j.respol.2008.11.001
Asadzandi, S., Rakhshani, T., & Mohammadi, A. (2017). Content analysis study of e-learning literature based on scopus record through 2013: With a focus on the place of Iran’s productions. International Journal on E-Learning: Corporate, Government, Healthcare, and Higher Education, 16(3), 213–229. https://eric.ed.gov/?id=EJ1140955
Barteit, S., Guzek, D., Jahn, A., Bärnighausen, T., Jorge, M. M., & Neuhann, F. (2020). Evaluation of e-learning for medical education in low- and middle-income countries: A systematic review. Computers and Education, 145. https://doi.org/10.1016/j.compedu.2019.103726
Bashir, F., & Warraich, N. F. (2020). Systematic literature review of Semantic Web for distance learning. Interactive Learning Environments. https://doi.org/10.1080/10494820.2020.1799023
Blei, D. M. (2012). Probabilistic topic models. Communications of the ACM, 55(4), 77–84. https://doi.org/10.1145/2133806.2133826
Blei, D. M., & Lafferty, J. D. (2007). Correction: A correlated topic model of Science. The Annals of Applied Statistics, 1(2), 634. https://doi.org/10.1214/07-aoas136
Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3(4/5), 993–1022. https://dl.acm.org/doi/10.5555/944919.944937
Çakiroğlu, Ü., Kokoç, M., Gökoğlu, S., Öztürk, M., & Erdoğdu, F. (2019). An analysis of the journey of open and distance education: Major concepts and cutoff points in research trends. International Review of Research in Open and Distance Learning, 20(1), 2–20. https://doi.org/10.19173/irrodl.v20i1.3743
Chavarría-Bolaños, D., Gómez-Fernández, A., Dittel-Jiménez, C., & Montero-Aguilar, M. (2020). E-learning in dental schools in the times of COVID-19: A review and analysis of an educational resource in times of the COVID-19 pandemic. Odovtos – International Journal of Dental Sciences, 22(3), 69–86. https://doi.org/10.15517/ijds.2020.41813
Chiappe, A., & Lee, L. L. (2017). Open teaching: A new way on e-learning? Electronic Journal of E-Learning, 15(5), 369–383. https://academic-publishing.org/index.php/ejel/article/view/1845
Fermín-González, M. (2019). Research on virtual education, inclusion, and diversity: A systematic review of scientific publications (2007–2017). International Review of Research in Open and Distance Learning, 20(5), 146–167. https://doi.org/10.19173/irrodl.v20i5.4349
González, C. (2010). What do university teachers think eLearning is good for in their teaching? Studies in Higher Education, 35(1), 61–78. https://doi.org/10.1080/03075070902874632
Graf, S., Liu, T.-C., & Kinshuk. (2010). Analysis of learners’ navigational behaviour and their learning styles in an online course. Journal of Computer Assisted Learning, 26(2), 116–131. https://doi.org/10.1111/j.1365-2729.2009.00336.x
Gürcan, F. (2009). Web içerik madenciliği ve konu sınıflandırılması. Karadeniz Teknik Üniversitesi. http://acikerisim.ktu.edu.tr/jspui/handle/123456789/437
Gurcan, F. (2018). Multi-class classification of Turkish texts with machine learning algorithms. ISMSIT 2018—2nd International Symposium on Multidisciplinary Studies and Innovative Technologies. https://doi.org/10.1109/ISMSIT.2018.8567307
Gurcan, F. (2019). Extraction of core competencies for big data: Implications for competency-based engineering education. International Journal of Engineering Education, 35(4), 1110–1115. https://www.ijee.ie/contents/c350419.html
Gurcan, F., & Cagiltay, N. E. (2020). Research trends on distance learning: A text mining-based literature review from 2008 to 2018. Interactive Learning Environments. https://doi.org/10.1080/10494820.2020.1815795
Gurcan, F., Cagiltay, N. E., & Cagiltay, K. (2021). Mapping human–computer interaction research themes and trends from its existence to today: A topic modeling-based review of past 60 years. International Journal of Human–Computer Interaction, 37(3), 267–280. https://doi.org/10.1080/10447318.2020.1819668
Hung, J. L. (2012). Trends of e-learning research from 2000 to 2008: Use of text mining and bibliometrics. British Journal of Educational Technology, 43(1), 5–16. https://doi.org/10.1111/j.1467-8535.2010.01144.x
Kaizer, B. M., Sanches da Silva, C. E., Zerbini, T., & Paiva, A. P. (2020). E-learning training in work corporations: A review on instructional planning. European Journal of Training and Development, 44(6/7), 615-636. https://doi.org/10.1108/EJTD-08-2019-0149
Khanal, S. S., Prasad, P. W. C., Alsadoon, A., & Maag, A. (2020). A systematic review: Machine learning based recommendation systems for e-learning. Education and Information Technologies, 25, 2635–2664. https://doi.org/10.1007/s10639-019-10063-9
Kibuku, R. N., Ochieng, D. O., & Wausi, A. N. (2020). E-learning challenges faced by universities in Kenya: A literature review. Electronic Journal of e-Learning, 18(2), 150–161. https://doi.org/10.34190/EJEL.20.18.2.004
Klingenberg, O. G., Holkesvik, A. H., & Augestad, L. B. (2020). Digital learning in mathematics for students with severe visual impairment: A systematic review. British Journal of Visual Impairment, 38(1), 38–57. https://doi.org/10.1177/0264619619876975
Krull, G., & Duart, J. M. (2017). Research trends in mobile learning in higher education: A systematic review of articles (2011–2015). International Review of Research in Open and Distance Learning, 18(7). https://doi.org/10.19173/irrodl.v18i7.2893
Mongeon, P., & Paul-Hus, A. (2016). The journal coverage of Web of Science and Scopus: A comparative analysis. Scientometrics, 106, 213–228. https://doi.org/10.1007/s11192-015-1765-5
Patra, S. K., Bhattacharya, P., & Verma, N. (2006). Bibliometric study of literature on bibliometrics. DESIDOC Journal of Library & Information Technology, 26(1). https://doi.org/10.14429/djlit.26.1.3672
Rodrigues, H., Almeida, F., Figueiredo, V., & Lopes, S. L. (2019). Tracking e-learning through published papers: A systematic review. Computers and Education, 136, 87–98. https://doi.org/10.1016/j.compedu.2019.03.007
Rodrigues, M. W., Isotani, S., & Zárate, L. E. (2018). Educational data mining: A review of evaluation process in the e-learning. Telematics and Informatics, 35(6), 1701–1717. https://doi.org/10.1016/j.tele.2018.04.015
Rowley, J., & Slack, F. (2004). Conducting a literature review. Management Research News, 27(6), 31–39. https://doi.org/10.1108/01409170410784185
Tibaná-Herrera, G., Fernández-Bajón, M. T., & De Moya-Anegón, F. (2018a). Categorization of e-learning as an emerging discipline in the world publication system: A bibliometric study in SCOPUS. International Journal of Educational Technology in Higher Education, 15(1), 21. https://doi.org/10.1186/s41239-018-0103-4
Tibaná-Herrera, G., Fernández-Bajón, M. T., & De Moya-Anegón, F. (2018b). Output, collaboration and impact of e-learning research: Bibliometric analysis and visualizations at the country and institutional level (Scopus 2003–2016). Profesional de La Informacion, 27(5), 1082–1096. https://doi.org/10.3145/epi.2018.sep.12
Valverde-Berrocoso, J., del Carmen Garrido-Arroyo, M., Burgos-Videla, C., & Morales-Cevallos, M. B. (2020). Trends in educational research about e-learning: A systematic literature review (2009–2018). Sustainability (Switzerland), 12(12), 5153. https://doi.org/10.3390/su12125153
Yang, X. L., Lo, D., Xia, X., Wan, Z. Y., & Sun, J. L. (2016). What security questions do developers ask? A large-scale study of stack overflow posts. Journal of Computer Science and Technology, 31, 910–924. https://doi.org/10.1007/s11390-016-1672-0
Zitzmann, N. U., Matthisson, L., Ohla, H., & Joda, T. (2020). Digital undergraduate education in dentistry: A systematic review. International Journal of Environmental Research and Public Health, 17(9), 3269. https://doi.org/10.3390/ijerph17093269
This work is licensed under a Creative Commons Attribution 4.0 International License.
This work is licensed under a Creative Commons Attribution 4.0 International Licence. The copyright of all content published in IRRODL is retained by the authors.
This copyright agreement and use license ensures, among other things, that an article will be as widely distributed as possible and that the article can be included in any scientific and/or scholarly archive.
You are free to
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
The licensor cannot revoke these freedoms as long as you follow the license terms below:
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.