Investigation of Emerging Trends in the E-Learning Field Using Latent Dirichlet Allocation
DOI:
https://doi.org/10.19173/irrodl.v22i2.5358Keywords:
e-learning, text-mining, topic modeling, trends, developmental stagesAbstract
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.
References
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
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