SLIM: a process for analyzing learners' behavior and discourse within large online communities




Social learning, Informal learning environments, Online communities, Assessment, Process


Educational researchers have an increasing interest in systematically assessing social learning that takes place in large online communities, nowadays one of the most important producers of Big Data in education. However, there is no agreement on how to measure the performance of such communities in informal learning settings. Assessing online Social Learning (SL) is a complex process that calls for an analytical approach in order to understand the various dimensions of learner discourse and the structure of the social interactions. This paper presents SLIM (Process for assessing online Social Learning within online communities in Informal environments): a process that combines structure and discourse analyses to assess SL indicators within large Online Learning Communities (OLC). Initially, we have used data provided by informal environments to perform Social Network Analysis (SNA) in order to identify conditions and behavioral patterns associated to learning. Next, we have incorporated these data into an unsupervised machine learning method to identify a discourse style related to learning. SLIM has been initially applied to two large online communities from the news sharing site Reddit. We are interested in characterizing and assessing the massively distributed learning, and just-in-time learning associated with the development of sustained online communities in informal environments. The results point out a set of quantitative measures and machine learning models that can be used to outline the evolution of SL indicators over time. They suggest that participation, ongoing collaboration and positive emotion have a fundamental role for knowledge creation and sharing. These findings can be used to take actions in order to regulate social interaction within large OLC.


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Azevedo, R., Taub, M., Mudrick, N. V., Millar, G. C., Bradbury, A. E., & Price, M. J. (2017). Using data visualizations to foster emotion regulation during self-regulated learning with advanced learning technologies. In Informational environments (pp. 225–247). Springer. doi: 10.1007/978-3-319-64274-1_10 [GS Search]

Becheru, A., Calota, A., & Popescu, E. (2018). Analyzing students’ collaboration patterns in a social learning environment using studentviz platform. Smart Learning Environments, 5(1), 1–18. doi: 10.1186/s40561-018-0063-0 [GS Search]

Chatti, M. A., Muslim, A., & Schroeder, U. (2017). Toward an open learning analytics ecosystem. In B. K. Daniel (Ed.), Big data and learning analytics in higher education (pp. 195–219). Springer International Publishing. doi: 10.1007/978-3-319-06520-5_12 [GS Search]

Chen, B., Chang, Y.-h., Ouyang, F., & Zhou, W. (2018). Fostering student engagement in online discussion through social learning analytics. The Internet and Higher Education, 37(January), 21–30. doi: 10.1016/j.iheduc.2017.12.002 [GS Search]

Chen, X., Vorvoreanu, M., & Madhavan, K. (2014). Mining social media data for understanding students’ learning experiences. IEEE Transactions on learning technologies, 7(3), 246–259. doi: 10.1109/TLT.2013.2296520 [GS Search]

Corbi, A., & Burgos, D. (2020). How to integrate formal and informal settings in massive open online courses through a transgenic learning approach. In Radical solutions and learning analytics (pp. 173–191). Springer. doi: 10.1007/978-981-15-4526-9_11 [GS Search]

Czerkawski, B. C. (2016). Blending formal and informal learning networks for online learning. Int. Review of Research in Open and Distributed Learning, 17(3), 138–156. doi: 10.19173/irrodl.v17i3.2344 [GS Search]

Dascalu, M., McNamara, D. S., Trausan-Matu, S., & Allen, L. K. (2018). Cohesion network analysis of cscl participation. Behavior Research Methods, 50(2), 604–619. doi: 10.3758/s13428-017-0888-4 [GS Search]

De Wever, B., Schellens, T., Valcke, M., & Van Keer, H. (2006). Content analysis schemes to analyze transcripts of online asynchronous discussion groups: A review. Computers and Education, 46(1), 6–28. doi: 10.1016/j.compedu.2005.04.005 [GS Search]

De Laat, M., & Prinsen, F. (2014). Social Learning Analytics: Navigating the Changing Settings of Higher Education. Research & Practice in Assessment, 9(1), 51–60. [GS Search]

Ferguson, R., & Shum, S. B. (2012). Social learning analytics: five approaches. In Proc. of 2nd int. conf. on learning analytics & knowledge (pp. 23–33). doi: 10.1145/2330601.2330616 [GS Search]

Ferreira, M., Rolim, V., Mello, R. F., Lins, R. D., Chen, G., & Gasevic, D. (2020). Towards automatic content analysis of social presence in transcripts of online discussions. In Proc. of the 10th int. conf. on learning analytics and knowledge. doi: 10.1145/3375462.3375495 [GS Search]

Fincham, E., Gasevic, D., & Pardo, A. (2018). From social ties to network processes: Do tie definitions matter? Journal of Learning Analytics, 5(2), 9–28. doi: 10.18608/jla.2018.52.2 [GS Search]

Fraga, B. S., da Silva, A. P. C., & Murai, F. (2018). Online social networks in health care: a study of mental disorders on reddit. In 2018 ieee/wic/acm int. conf. on web intelligence (wi) (pp. 568–573). doi: 10.1109/WI.2018.00-36 [GS Search]

Galanis, N., Mayol, E., Alier, M., & García-Peñalvo, F. J. (2016). Supporting, evaluating and validating informal learning. a social approach. Computers in Human Behavior, 55, 596–603. doi: 10.1016/j.chb.2015.08.005 [GS Search]

Galvin, S., & Greenhow, C. (2020). Educational networking: A novel discipline for improved k-12 learning based on social networks. In Educational networking (pp. 3–41). Springer. doi: 10.1007/978-3-030-29973-6_1 [GS Search]

Garrison, D. R., Anderson, T., & Archer, W. (2010). The first decade of the community of inquiry framework: A retrospective. Internet and Higher Education, 13(1-2), 5–9. doi: 10.1016/j.iheduc.2009.10.003 [GS Search]

Gasevic, D., Joksimovic, S., Eagan, B. R., & Shaffer, D. W. (2019). Sens: Network analytics to combine social and cognitive perspectives of collaborative learning. Computers in Human Behavior, 92(July 2018), 562–577. doi: 10.1016/j.chb.2018.07.003 [GS Search]

Greenhow, C., Gibbins, T., & Menzer, M. M. (2015). Re-thinking scientific literacy out-of-school: Arguing science issues in a niche Facebook application. Computers in Human Behavior, 53, 593–604. doi: 10.1016/j.chb.2015.06.031 [GS Search]

Gruzd, A., Paulin, D., & Haythornthwaite, C. (2016). Analyzing social media and learning through content and social network analysis: A faceted methodological approach. Journal of Learning Analytics, 3(3), 46–71. doi: 10.18608/jla.2016.33.4 [GS Search]

Haythornthwaite, C. (2018). Learning , connectivity and networks. Information and Learning Science. doi: 10.1108/ILS-06-2018-0052 [GS Search]

Haythornthwaite, C., de Laat, M., & Schreurs, B. (2016). A social network analytic perspective on e-learning. In The sage handbook of e-learning research (pp. 251–269). London: SAGE Publications. [GS Search]

Haythornthwaite, C., Kumar, P., Gruzd, A., Gilbert, S., Esteve del Valle, M., & Paulin, D. (2018). Learning in the wild: coding for learning and practice on reddit. Learning, media and technology, 43(3), 219–235. doi: 10.1080/17439884.2018.1498356 [GS Search]

Hudgins, W., Lynch, M., Schmal, A., Sikka, H., Swenson, M., & Joyner, D. A. (2020). Informal Learning Communities: The Other Massive Open Online ’C’. L@S 2020 - Proceedings of the 7th ACM Conference on Learning @ Scale, 91–101. doi: 10.1145/3386527.3405926 [GS Search]

Jan, S. K. (2019). Investigating virtual communities of practice with social network analysis: guidelines from a systematic review of research. Int. Journal of Web Based Communities, 15(1), 25. doi: 10.1504/ijwbc.2019.098697 [GS Search]

Jan, S. K., & Vlachopoulos, P. (2018). Influence of learning design of the formation of online communities of learning. International Review of Research in Open and Distributed Learning, 19(4). doi: 10.19173/irrodl.v19i4.3620 [GS Search]

Jan, S. K., & Vlachopoulos, P. (2019). Social network analysis: A framework for identifying communities in higher education online learning. Technology, Knowledge and Learning, 24(4), 621–639. doi: 10.1007/s10758-018-9375-y [GS Search]

Joksimovic, S., Gasevic, D., Kovanovic, V., Adesope, O., & Hatala, M. (2014). Psychological characteristics in cognitive presence of communities of inquiry: A linguistic analysis of online discussions. Internet and Higher Education, 22, 1–10. doi: 10.1016/j.iheduc.2014.03.001 [GS Search]

Joksimovic, S., Gasevic, D., Kovanovic, V., Riecke, B. E., & Hatala, M. (2015). Social presence in online discussions as a process predictor of academic performance. Journal of Computer Assisted Learning, 31(6), 638–654. doi: 10.1111/jcal.12107 [GS Search]

Joksimovic, S., Jovanovic, J., Kovanovic, V., Gasevic, D., Milikic, N., Zouaq, A., & van Staalduinen, J. P. (2019). Comprehensive analysis of discussion forum participation: From speech acts to discussion dynamics and course outcomes. IEEE Transactions on Learning Technologies, 13(1), 38–51. doi: 10.1109/TLT.2019.2916808 [GS Search]

Kent, C., Rechavi, A., & Rafaeli, S. (2019). Networked learning analytics: A theoretically informed methodology for analytics of collaborative learning. In Learning in a networked society (pp. 145–175). Springer. doi: 10.1007/978-3-030-14610-8_9 [GS Search]

Kim, D., Yoon, M., Jo, I.-H., & Branch, R. M. (2018). Learning analytics to support self-regulated learning in asynchronous online courses: A case study at a women’s university in south korea. Computers & Education, 127, 233–251. doi: 10.1016/j.compedu.2018.08.023 [GS Search]

Kovanović, V., Joksimović, S., Poquet, O., Hennis, T., Čukić, I., De Vries, P., . . . Gašević, D. (2018). Exploring communities of inquiry in massive open online courses. Computers & Education, 119, 44–58. doi: 10.1016/j.compedu.2017.11.010 [GS Search]

Lin, Y., Yu, R., & Dowell, N. (2020). Liwcs the same, not the same: Gendered linguistic signals of performance and experience in online stem courses. In Int. conf. on artificial intelligence in education (pp. 333–345). doi: 10.1007/978-3-030-52237-7_27 [GS Search]

Mamas, C., Bjorklund Jr, P., Daly, A. J., & Moukarzel, S. (2020). Friendship and support networks among students with disabilities in middle school. International Journal of Educational Research, 103, 101608. doi: 10.1016/j.ijer.2020.101608 [GS Search]

Nistor, N., Dascalu, M., Tarnai, C., & Trausan-Matu, S. (2020). Predicting newcomer integration in online learning communities: Automated dialog assessment in blogger communities. Computers in Human Behavior, 105(September 2019), 106202. doi: 10.1016/j.chb.2019.106202 [GS Search]

Nistor, N., Derntl, M., & Klamma, R. (2015). Learning Analytics : Trends and Issues of the Empirical Research of the Years 2011 – 2014. Design for Teaching and Learning in a Networked World, 4, 453–459. doi: 10.1007/978-3-319-24258-3 [GS Search]

Pekrun, R. (2006). The control-value theory of achievement emotions: Assumptions, corollaries, and implications for educational research and practice. Educational psychology review, 18(4), 315–341. doi: 10.1007/s10648-006-9029-9 [GS Search]

Pennebaker, J. W., Boyd, R. L., Jordan, K., & Blackburn, K. (2015). The development and psychometric properties of liwc2015. [GS Search]

Pesare, E., Roselli, T., & Rossano, V. (2016). Visualizing student engagement in e-learning environment. In Proc. 22th int. conference on distributed multimedia systems (pp. 26–33). doi: 10.18293/DMS2016-028 [GS Search]

Rezaei, M. S., Bobarshad, H., & Badie, K. (2019). Toward enhancing collaborative learning groups formation in q&a website using tag-based next questions predictor. Int. Journal of Technology Enhanced Learning, 11(4), 441–462. doi: 10.1504/IJTEL.2019.102570 [GS Search]

Rourke, L., Anderson, T., Garrison, R., & Archer, W. (2001). Assessing social presence in asynchronous text-based computer. Journal of Distance Education, 14, 1–18. [GS Search]

Schreurs, B., & De Laat, M. (2014). The Network Awareness Tool: A web 2.0 tool to visualize informal networked learning in organizations. Computers in Human Behavior, 37(1), 385–394. doi: 10.1016/j.chb.2014.04.034 [GS Search]

Scott, J., & Carrington, P. J. (2011). The sage handbook of social network analysis. SAGE publications. [GS Search]

Shum, S. B., & Ferguson, R. (2012). Social learning analytics. Journal of educational technology & society, 15(3), 3–26. [GS Search]

Silva, R. F., Gimenes, I. M. S., & Maldonado, J. C. (2020). An Approach for Assessing Large Online Communities in Informal Learning Environments. In Anais do xxxi simpósio brasileiro de informática na educação (pp. 642–651). doi: 10.5753/cbie.sbie.2020.642 [GS Search]

Speily, O. R. B., Rezvanian, A., Ghasemzadeh, A., Saghiri, A. M., & Vahidipour, S. M. (2020). Lurkers versus posters: Investigation of the participation behaviors in online learning communities. In Educational networking (pp. 269–298). Springer. doi: 10.1007/978-3-030-29973-6_8 [GS Search]

Swiecki, Z., & Shaffer, D. W. (2020). ISENS: An integrated approach to combining epistemic and social network analyses. In Proc. of the 10th int. conf. on learning analytics & knowledge (pp. 305–313). doi: 10.1145/3375462.3375505 [GS Search]

Toikkanen, T., & Lipponen, L. (2011). The applicability of social network analysis to the study of networked learning. Interactive Learning Environments, 19(4), 365–379. doi: 10.1080/10494820903281999 [GS Search]

Wenger, E., Trayner, B., & De Laat, M. (2011). Promoting and assessing value creation in communities and networks: a conceptual framework. , 18(August), 1–60. [GS Search]

Weninger, T. (2014). An exploration of submissions and discussions in social news: Mining collective intelligence of reddit. Social Network Analysis and Mining, 4(1), 173–192. doi: 10.1007/s13278-014-0173-9 [GS Search]

Xiong, J., Feng, X., & Tang, Z. (2020). Understanding user-to-user interaction on government microblogs: An exponential random graph model with the homophily and emotional effect. Information Processing & Management, 57(4), 102229. doi: 10.1016/j.ipm.2020.102229 [GS Search]

Zhang, J., Ackerman, M. S., & Adamic, L. (2007). Expertise Networks in Online Communities: Structure and Algorithms. In International world wide web conference (pp. 221–230). doi: 10.1145/1242572.1242603 [GS Search]

Zhu, M., Herring, S. C., & Bonk, C. J. (2019). Exploring presence in online learning through three forms of computer-mediated discourse analysis. Distance Education, 40(2), 205–225. doi: 10.1080/01587919.2019.1600365 [GS Search]

Zou, W., Hu, X., Pan, Z., Li, C., Cai, Y., & Liu, M. (2021). Exploring the relationship between social presence and learners’ prestige in mooc discussion forums using automated content analysis and social network analysis. Computers in Human Behavior, 115, 106582. doi: 10.1016/j.chb.2020.106582 [GS Search]

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DA SILVA, R. F. SLIM: a process for analyzing learners’ behavior and discourse within large online communities. Revista Brasileira de Informática na Educação, [S. l.], v. 30, p. 573–597, 2022. DOI: 10.5753/rbie.2022.2614. Disponível em: Acesso em: 19 jun. 2024.



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