Main Article Content
Staying at the front line in learning research is challenging because many fields are rapidly developing. One such field is research on the temporal aspects of computer-supported collaborative learning (CSCL). To obtain an overview of these fields, systematic literature reviews can capture patterns of existing research. However, conducting systematic literature reviews is time-consuming and do not reveal future developments in the field. This study proposes a machine learning method based on topic modelling that takes articles from a systematic literature review on the temporal aspects of CSCL (49 original articles published before 2019) as a starting point to describe the most recent development in this field (52 new articles published between 2019 and 2020). We aimed to explore how to identify new relevant articles in this field and relate the original articles to the new articles. First, we trained the topic model with the Results, Discussion, and Conclusion sections of the original articles, enabling us to correctly identify 74% (n = 17) of new and relevant articles. Second, clusterisation of the original and new articles indicated that the field has advanced in its new and relevant articles because the topics concerning the regulation of learning and collaborative knowledge construction related 26 original articles to 10 new articles. New irrelevant studies typically emerged in clusters that did not include any specific topic with a high topic occurrence. Our method may provide researchers with resources to follow the patterns in their fields instead of conducting repetitive systematic literature reviews.
FLR adopts the Attribution-NonCommercial-NoDerivs Creative Common License (BY-NC-ND). That is, Copyright for articles published in this journal is retained by the authors with, however, first publication rights granted to the journal. By virtue of their appearance in this open access journal, articles are free to use, with proper attribution, in educational and other non-commercial settings.
Alexander, P. A. (2020). Methodological guidance paper: The art and science of quality systematic reviews. Review of Educational Research, 90(1), 6–23. https://doi.org/10.3102/0034654319854352
Bird, S., Loper E., & Klein, E. (2009). Natural language processing with Python. O’Reilly Media Inc.
Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022.
Boyd-Graber, J. L., Hu, Y., & Mimno, D. (2017). Applications of topic models (Vol. 11). Now Publishers Incorporated.
Chen, X., Zou, D., & Xie, H. (2020). Fifty years of British Journal of Educational Technology: A topic modeling based bibliometric perspective. British Journal of Educational Technology, 51(3), 692–708. https://doi.org/10.1111/bjet.12907
Gruber, H., Hämäläinen, R. H., Hickey, D. T., Pang, M. F., & Pedaste, M. (2020). Mission and scope of the Journal Educational Research Review. Educational Research Review, 30, 100328. https://doi.org/10.1016/j.edurev.2020.100328
Hadwin, A. F. (2021). Commentary and future directions: What can multi-modal data reveal about temporal and adaptive processes in self-regulated learning? Learning and Instruction, 72, 101287. https://doi.org/10.1016/j.learninstruc.2019.101287
Hew, K. F., Lan, M., Tang, Y., Jia, C., & Lo, C. K. (2019), Where is the ‘theory’ within the field of educational technology research? British Journal of Educational Technology, 50(3), 956–971. https://doi.org/10.1111/bjet.12770
Järvelä, S., & Rosé, C. P. (2020). Advocating for group interaction in the age of COVID-19. International Journal of Computer-Supported Collaborative Learning, 15(2), 143–147. https://doi.org/10.1007/s11412-020-09324-4
Lämsä, J. (2020). Developing the temporal analysis for computer-supported collaborative learning in the context of scaffolded inquiry [Doctoral dissertation, University of Jyväskylä]. JYU dissertations, 245. http://urn.fi/URN:ISBN:978-951-39-8248-5
Lämsä, J., Hämäläinen, R., Koskinen, P., Viiri, J., & Lampi, E. (2021). What do we do when we analyse the temporal aspects of computer-supported collaborative learning? A systematic literature review. Educational Research Review, 33, 100387. https://doi.org/10.1016/j.edurev.2021.100387
Moher, D., Liberati, A., Tetzlaff, J., Altman, D.G., & The PRISMA Group. (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Med, 6(7), 1–6. https://doi.org/10.1136/bmj.b2535
Murphy, P. K., Knight, S. L., & Dowd, A. C. (2017). Familiar paths and new directions: Inaugural call for manuscripts. Review of Educational Research, 87(1), 3–6. https://doi.org/10.3102/0034654317691764
Rehurek, R., & Sojka, P. (2010). Software framework for topic modelling with large corpora. Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks (pp. 45–50). ELRA. https://doi.org/10.13140/2.1.2393.1847
Tuhkala, A., Kärkkäinen, T., & Nieminen, P. (2018). Semi-automatic literature mapping of participatory design studies 2006–2016. In Proceedings of the 15th Participatory Design Conference (pp. 1–5). Association for Computing Machinery. https://doi.org/10.1145/3210604.3210621