What Can Interaction Sequences Tell Us About Collaboration Quality in Small Learning Groups?
One advantage of small group collaboration in online courses is that it can enrich the students learning experience with regard to interactional and social dimensions. In this paper we apply a previously tested method of sequential analysis on group activity sequences. These activity sequences stem from an online course on computer mediated communication where the group tasks consisted of collaborative text production. Students activities in a group forum and a shared wiki were recorded and classified as coordination, monitoring, major/minor contribution. Analyses of clusters of similar sequences show, that there are characteristic patterns indicating productivity, fair work distribution, as well as satisfaction with the group work. Our findings are a step towards automatic diagnosis of collaboration problems in online group work to facilitate early interventions.
KeywordsLearning groups Online courses Sequence analysis Collaboration patterns
This study was conducted in the context of the IKARion Project and we want to thank the IKARion Project Team. The project was funded by the Federal Ministry of Education and Research (grant number: 16DHL1013).
- 3.Clow, D.: MOOCs and the funnel of participation. Proc. Third Int. Conf. Learn. Anal. Knowl. - LAK ’13. 185 (2013). https://doi.org/10.1145/2460296.2460332
- 4.Curtis, D.D., Lawson, M.J.: Exploring collaborative online learning. JALN - J. Asynchronous Learn. Netw. (presently OLC – Online Learn. Consortium) 5(1), 21–34 (2001). https://doi.org/10.24059/olj.v5i1.1885
- 6.Doberstein, D., et al.: Sequence patterns in small group work within a large online course. In: Gutwin, C., et al. (eds.) Collaboration and Technology, pp. 104–117 23rd International Conference on Collaboration and Technology (CRIWG), Saskatoon, SK, Canada (2017). https://doi.org/10.1007/978-3-319-63874-4Google Scholar
- 7.Doberstein, D., et al.: Using sequence analysis to characterize the efficiency of small groups in large online courses. In: Yang, J.C., et al. (eds.) Proceedings of the 26th International Conference on Computers in Education (ICCE 2018), pp. 247–256. Asia-Pacific Society for Computers in Education, Philippines (2018)Google Scholar
- 8.Ferschke, O., et al.: Fostering discussion across communication media in massive open online courses. In: Proceedings of the 11th International Conference on Computer-supported collaboration Learning (2015)Google Scholar
- 10.Kaufman, L., Rousseeuw, P.J.: Clustering by means of medoids. In: International Conference on Statistical Data Analysis Based on the L1-norm and Related Methods, March, pp. 405–416 (1987)Google Scholar
- 11.Staubitz, T., et al.: Collaborative learning in a MOOC environment. In: Proceedings of the 8th International Conference of Education, Research and Innovation, Seville, Spain, pp. 8237–8246 (2015)Google Scholar
- 12.Strauß, S., et al.: Developing a library of typical problems for collaborative learning in online courses. In: Proceedings of the 13th International Conference of the Learning Sciences (ICLS), London, UK, pp. 1045–1048 (2018)Google Scholar
- 13.Tomar, G.S., et al.: Intelligent conversational agents as facilitators and coordinators for group work in distributed learning environments (MOOCs). In: AAAI Spring Symposium - Technical Report SS-16-01, pp. 298–302 (2016)Google Scholar
- 14.Wichmann, A., et al.: Group formation for small-group learning: are heterogeneous groups more productive? In: Proceedings of the 12th International Symposium on Open Collaboration, pp. 14:1–14:4 (2016). https://doi.org/10.1145/2957792.2965662