Analysis of Students’ Performance in an Online Discussion Forum: A Social Network Approach
In the new era of digitalization, the education sector is experiencing changes in terms of the learning design, teaching methods, engagement of the learners and integration of technology. Flexibility of online courses is attracting more and more students to the learning platforms every day. The learning activity of students on the online platforms generates enormous amount of data. Practically every click can be traced and described. Learners are viewing the video lectures and digital lessons as a passive form of learning and most of the active learning is taking place in the form of online discussions. Therefore, in order to measure the students’ active engagement in an online course, it is essential to evaluate the communication channels. However, the assessment methods for online discussion remain limited. The objective of the work is to provide a ranking of students, based on their participation in the online discussion forum. It provides an opportunity for the teachers to automatically assess students’ performance quantitatively based on systematic approach. In this paper, network centrality measures are utilized to rank the students based on their interactivity. Text analytics is applied in association with sentiment analysis to assess meaningfulness of each student’s communication. The method was tested on the online course data of “Systematic Creativity and TRIZ basics” at LUT University, Finland. This work was partially supported by CEPHEI project of the ERASMUS + EU framework.
KeywordsSocial-network analysis Online-discussion forums Online learning Sentiment analysis Neuro-linguistic programming NLP
This work was partially supported by CEPHEI project of ERASMUS+ EU framework which is an ongoing project focusing on digitalization of industrial innovation-related contents. The authors hope to conduct further research and extend this work in future with possible support from this project.
- 4.Bergmann, J., Sams, A.: Remixing chemistry class: two Colorado teachers make vodcasts of their lectures to free up class time for hands-on activities. Learn. Lead. Technol. 36, 22–27 (2009)Google Scholar
- 8.Dekker, A.: Applying social network analysis concepts to military C4ISR architectures. Connections 24, 93–103 (2002)Google Scholar
- 9.Early, S.L.: Book review student engagement techniques: a handbook for college faculty. J. Sch. Teach. Learn. 11(1), 155–157 (2011)Google Scholar
- 12.Ferrara, E.: Measurement and analysis of online social networks systems. In: Encyclopedia of Social Network Analysis and Mining (2018). https://doi.org/10.1007/978-1-4939-7131-2_242
- 13.Hanneman, R.A., Riddle, M.: Introduction to Social Network Methods. Network (1998). https://doi.org/10.1109/78.700969
- 15.Rabbany, R., et al.: Collaborative learning of students in online discussion forums: a social network analysis perspective. In: Studies in Computational Intelligence (2014). https://doi.org/10.1007/978-3-319-02738-8_16
- 16.Rajaraman, A., Ullman, J.D.: Mining of Massive Datasets (2011). https://doi.org/10.1017/cbo9781139058452
- 17.Rosvall, M., Bergstrom, C.T.: Maps of information flow reveal community structure in complex networks. Proc. Natl. Acad. Sci. U.S.A. (2008). https://doi.org/10.1073/pnas.0706851105
- 18.Scott, J., et al.: Social network analysis: an introduction. In: The SAGE Handbook of Social Network Analysis (2015). https://doi.org/10.4135/9781446294413.n2
- 20.Suraj, P., Roshni, V.S.K.: Social network analysis in student online discussion forums. In: 2015 IEEE Recent Advances in Intelligent Computational Systems, RAICS 2015, pp. 134–138, December 2016. https://doi.org/10.1109/raics.2015.7488402
- 22.Widmer, E.D., Lafarg, L.-A.: Boundedness and connectivity of contemporary families: a case study. Connections 22, 30–36 (1999)Google Scholar
- 23.Wise, A., Zhao, Y., Hausknecht, S.: W11-Learning analytics for online discussions: a pedagogical model for intervention with embedded and extracted analytics. In: Proceedings of the Conference on Learning Analytics (2013). https://doi.org/10.1145/2460296.2460308