SLOAN: Social Learning Optimization Analysis of Networks

Authors

  • David J. Lemay Cerence Inc.
  • Tenzin Doleck Simon Fraser University
  • Christopher G. Brinton Purdue University

DOI:

https://doi.org/10.19173/irrodl.v23i4.6484

Keywords:

social learning optimization analysis of networks, SLOAN, social cognitive theory, social learning, information theory, network analysis

Abstract

Online discussion research has mainly been conducted using case methods. This article proposes a method for comparative analysis based on network metrics such as information entropy and global network efficiency as more holistic measures characterizing social learning group dynamics. We applied social learning optimization analysis of networks (SLOAN) to a data set consisting of Coursera courses from a range of disciplines. We examined the relationship of discussion forum uses and measures of network efficiency, characterized by the information flow through the network. Discussion forums vary greatly in size and in use. Courses with a greater prevalence of subject-related versus procedural talk differed significantly in seeking but not disseminating behaviors in massive open online course discussion forums. Subject-related talk was related to higher network efficiency and had higher seeking and disseminating scores overall. We discuss the value of SLOAN for social learning and argue for the experimental study of online discussion optimization using a discussion post recommendation system for maximizing social learning.

References

Almatrafi, O., & Johri, A. (2019). Systematic review of discussion forums in massive open online courses (MOOCs). IEEE Transactions on Learning Technologies, 12(3), 413–428. https://doi.org/10.1109/tlt.2018.2859304

Andresen, M. A. (2009). Asynchronous discussion forums: Success factors, outcomes, assessments, and limitations. Educational Technology & Society, 12(1), 249–257. https://www.researchgate.net/publication/220374740_Asynchronous_Discussion_Forums_Success_Factors_Outcomes_Assessments_and_Limitations

Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Prentice-Hall.

Bandura, A. (2001). Social cognitive theory: An agentic perspective. Annual Review of Psychology, 52(1), 1-26. https://doi.org/10.1146/annurev.psych.52.1.1

Bergner, Y., Kerr, D., & Pritchard, D. E. (2015, June 26–29). Methodological challenges in the analysis of MOOC data for exploring the relationship between discussion forum views and learning outcomes. In EDM ’15: Proceedings of the 8th International Conference on Educational Data Mining (pp. 234–241). International Educational Data Mining Society. https://files.eric.ed.gov/fulltext/ED560510.pdf

Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of Machine Learning Research, 3, 993-1022. https://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf

Boroujeni, M. S., Hecking, T., Hoppe, H. U., & Dillenbourg, P. (2017, March). Dynamics of MOOC discussion forums. In LAK ’17: Proceedings of the Seventh International Learning Analytics & Knowledge Conference (pp. 128–137). Association for Computing Machinery. https://doi.org/10.1145/3027385.3027391

Boyd, S., & Vandenberghe, L. (2004). Convex optimization. Cambridge University Press. https://doi.org/10.1017/CBO9780511804441

Brinton, C. G., Buccapatnam, S., Wong, F. M. F., Chiang, M., & Poor, H. V. (2016, April 10–14). Social learning networks: Efficiency optimization for MOOC forums. IEEE INFOCOM 2016—The 35th Annual IEEE International Conference on Computer Communications, San Francisco, CA. https://doi.org/10.1109/INFOCOM.2016.7524579

Brinton, C. G., Buccapatnam, S., Zheng, L., Cao, D., Lan, A. S., Wong, F. M. F., Ha, S., Chiang, M., & Poor, H. V. (2018). On the efficiency of online social learning networks. IEEE/ACM Transactions on Networking, 26(5), 2076–2089. https://doi.org/10.1109/TNET.2018.2859325

Brown, A. L. (1992). Design experiments: Theoretical and methodological challenges in creating complex interventions in classroom settings. Journal of the Learning Sciences, 2(2), 141–178. https://doi.org/10.1207/s15327809jls0202_2

Castañeda, L., & Williamson, B. (2021). Assembling new toolboxes of methods and theories for innovative critical research on educational technology. Journal of New Approaches in Educational Research, 9(2), 1–14. https://doi.org/10.7821/naer.2021.1.703

Castro, M., & Tumibay, G. (2019). A literature review: Efficacy of online learning courses for higher education institution using meta-analysis. Education and Information Technologies, 26, 1367–1385. https://doi.org/10.1007/s10639-019-10027-zhttps://doi.org/10.1007/s10639-019-10027-z

Chiu, T. K., & Hew, T. K. (2018). Factors influencing peer learning and performance in MOOC asynchronous online discussion forum. Australasian Journal of Educational Technology, 34(4), 16-28. https//doi.org/10.14742/ajet.3240

Cobb, P., Confrey, J., diSessa, A., Lehrer, R., & Schauble, L. (2003). Design experiments in educational research. Educational Researcher, 32(1), 9–13. https://doi.org/10.3102/0013189X032001009

Crittenden, W. (2005). A social learning theory of cross-functional case education. Journal of Business Research, 58(7), 960–966. https://doi.org/10.1016/j.jbusres.2003.12.005

Deaton, S. (2015). Social learning theory in the age of social media: Implications for educational practitioners. I-Manager’s Journal of Educational Technology, 12(1), 1–6. https://doi.org/10.26634/jet.12.1.3430

Dehmer, M. and Mowshowitz, A. (2011). Generalized graph entropies. Complexity, 17(2) 45-50.

Doleck, T., Lemay, D. J., & Brinton, C. G. (2021). Evaluating the efficiency of social learning networks: Perspectives for harnessing learning analytics to improve discussions. Computers & Education, 164, Article 104124. https://doi.org/10.1016/j.compedu.2021.104124

Dowell, N. M., Skrypnyk, O., Joksimovic, S., Graesser, A. C., Dawson, S., Gasevic, D., Hennis, T. A., de Vries, P., & Kovanovic, V. (2015, June 26–29). Modeling learners’ social centrality and performance through language and discourse. In EDM ’15: Proceedings of the 8th International Conference on Educational Data Mining (pp. 250–257). International Educational Data Mining Society. https://files.eric.ed.gov/fulltext/ED560532.pdf

Duchastel, P. (1996). Learning interfaces. In T. Liao (Ed.), Advanced educational technology: Research issues and future potential (pp. 206–217). Springer. https://doi.org/10.1007/978-3-642-60968-8_13

Engelbart, D. C. (1962, October) Augmenting Human Intellect: A Conceptual Framework. SRI Sumary Report AFOSR-3223. Prepared for: Director of Information Sciences, Air Force Office of Scientific Research, Washington DC, Contract AF 49(638)-1024. SRI Project No. 3578.

Fathima, S., & Kore, S. K. (2021). Formulation of the challenges in brain–computer interfaces as optimization problems—a review. Frontiers in Neuroscience, 14, Article 546656. https://doi.org/10.3389/fnins.2020.546656

Feng, Y., Chen, D., Zhao, Z., Chen, H., & Xi, P. (2015, August). The impact of students and TAs’ participation on students’ academic performance in MOOC. In J. Pei, F. Silvestri., & J. Tang (Eds.), Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015 (pp. 1149–1154). Association for Computing Machinery. https://doi.org/10.1145/2808797.2809428

Fu, E. L. F., van Aalst, J., & Chan, C. K. K. (2016). Toward a classification of discourse patterns in asynchronous online discussions. International Journal of Computer-Supported Collaborative Learning, 11, 441–478. https://doi.org/10.1007/s11412-016-9245-3

Galikyan, I., Admiraal, W., & Kester, L. (2021). MOOC discussion forums: The interplay of the cognitive and the social. Computers & Education, 165, Article 104133. https://doi.org/10.1016/j.compedu.2021.104133

Gardner, J., & Brooks, C. (2018). Student success prediction in MOOCs. User Modeling and User-Adapted Interaction, 28(2), 127–203. https://doi.org/10.1007/s11257-018-9203-z

Garrison, D., & Akyol, Z. (2012). The Community of Inquiry theoretical framework. In M. G. Moore (Ed.), Handbook of distance education (3rd ed., pp. 104–120). Taylor & Francis. https://doi.org/10.4324/9780203803738.ch7

Gay, G. H., & Betts, K. (2020). From discussion forums to eMeetings: Integrating high touch strategies to increase student engagement, academic performance, and retention in large online courses. Online Learning, 24(1), 92–117. https://files.eric.ed.gov/fulltext/EJ1249245.pdf

Gee, J. P., & Green, J. L. (1998). Chapter 4: Discourse analysis, learning, and social practice: A methodological study. Review of Research in Education, 23(1), 119–169. https://doi.org/10.3102/0091732X023001119

Gilbert, P., & Dabbagh, N. (2004). How to structure online discussions for meaningful discourse: A case study. British Journal of Educational Technology, 36(1), 5–18. https://doi.org/10.1111/j.1467-8535.2005.00434.x

Goshtasbpour, F., Swinnerton, B., & Pickering, J. (2021). Twelve tips for engaging learners in online discussions. Medical Teacher, 44(3), 244–248. https://doi.org/10.1080/0142159x.2021.1898571

Hammond, M. (2005). A review of recent papers on online discussion in teaching and learning in higher education. Journal of Asynchronous Learning Networks, 9(3), 9–23. https://doi.org/10.24059/olj.v9i3.1782

Hill, J., Song, L. and West, R. (2009). Social learning theory and web-based learning environments: A review of research and discussion of implications. American Journal of Distance Education, 23(2), 88-103. https://doi.org/10.1080/08923640902857713

Jan, S. K., Vlachopoulos, P., & Parsell, M. (2019). Social network analysis and learning communities in higher education online learning: A systematic literature review. Online Learning Journal, 23, 249–264. https://doi.org/10.24059/olj.v23i1.1398

Jiang, S., Fitzhugh, S.M., & Warschauer, M. (2014). Social positioning and performance in MOOCs. EDM.

Joksimović, S., Manataki, A., Gašević, D., Dawson, S., Kovanović, V., & De Kereki, I. F. (2016). Translating network position into performance: importance of centrality in different network configurations. In Proceedings of the Sixth International Conference on Learning Analytics & Knowledge (pp.314-323). ACM.

Joksimović, S., Poquet, O., Kovanović, V., Dowell, N., Mills, C., Gašević, D., Dawson, S., Graesser, A. C., & Brooks, C. (2017). How do we model learning at scale? A systematic review of research on MOOCs. Review of Educational Research, 88(1), 43–86. https://doi.org/10.3102/0034654317740335

Kim, M. K., & Ketenci, T. (2019). Learner participation profiles in an asynchronous online collaboration context. The Internet and Higher Education, 41, 62–76. https://doi.org/10.1016/j.iheduc.2019.02.002

Kimmons, R., Rosenberg, J., & Allman, B. (2021). Trends in educational technology: What Facebook, Twitter, and Scopus can tell us about current research and practice. TechTrends, 65(2), 125–136. https://doi.org/10.1007/s11528-021-00589-6

Kloos, C. D., Alario-Hoyos, C., Muñoz-Merino, P. J., Ibáñez, M. B., Estévez-Ayres, I., & Fernández-Panadero, C. (2020). Educational technology in the age of natural interfaces and deep learning. IEEE Revista Iberoamericana de Tecnologias del Aprendizaje, 15(1), 26–33. https://doi.org/10.1109/RITA.2020.2979165

Koschmann, T. D. (2011). Theories of learning and studies of instructional practice. Springer.

Latora, V., & Marchiori, M. (2001). Efficient behavior of small-world networks. Physical Review Letters, 87(19), Article 198701. https://doi.org/10.1103/PhysRevLett.87.198701

Lee, J. and Recker, M., 2021. The effects of instructors' use of online discussions strategies on student participation and performance in university online introductory mathematics courses. Computers & Education, 162, 104084.

Lemay, D. J., & Doleck, T. (2020). Constructivist educational technology: Re-examining the foundations and state of the literature. British Journal of Educational Technology, 51(6), 1905-1906. https//doi.org/10.1111/bjet.13042

Lemay, D. J., Doleck, T., & Bazelais, P. (2021). Transition to online teaching during the COVID-19 pandemic. Interactive Learning Environments, 1-12. https//doi.org/10.1080/10494820.2021.1871633

Liao, T. (1996). Advanced educational technology: Research issues and future potential. Springer.

Loizzo, J., & Ertmer, P. (2016). MOOCocracy: The learning culture of massive open online courses. Educational Technology Research and Development, 64, 1013–1032. https://doi.org/10.1007/s11423-016-9444-7

Marra, R. (2006). A review of research methods for assessing content of computer-mediated discussion forums. Journal of Interactive Learning Research, 17(3), 243–267. https://www.learntechlib.org/primary/p/6290/

Marx, K. (1973). Fragment on the machines. In Grundrisse: Foundations of the critique of political economy (M. Nicolaus, Trans.; ch. 13). Marxists Internet Archive. https://www.marxists.org/archive/marx/works/1857/grundrisse/ch13.htm (Original work published 1939–1941)

Moore, R., Yen, C., & Powers, F. (2020). Exploring the relationship between clout and cognitive processing in MOOC discussion forums. British Journal of Educational Technology, 52(1), 482–497. https://doi.org/10.1111/bjet.13033

O’Riordan, T., Millard, D., & Schulz, J. (2020). Is critical thinking happening? Testing content analysis schemes applied to MOOC discussion forums. Computer Applications in Engineering Education, 29(4), 690–709. https://doi.org/10.1002/cae.22314

Raković, M., Marzouk, Z., Liaqat, A., Winne, P. H., & Nesbit, J. C. (2020). Fine grained analysis of students’ online discussion posts. Computers & Education, 157, Article 103982. https://doi.org/10.1016/j.compedu.2020.103982

Reed, M. S., Evely, A. C., Cundill, G., Fazey, I., Glass, J., Laing, A., Newig, J., Parrish, B., Prell, C., Raymond C., & Stringer, L. C. (2010). What is social learning? Ecology and Society, 15(4), Article r1. http://www.ecologyandsociety.org/vol15/iss4/resp1/

Rosé, C. (2017). Discourse analytics. In C. Lang, G. Siemens, A. F. Wise, & D. Gaevic (Eds.), Handbook of learning analytics (pp. 105–114). Society for Learning Analytics Research (SoLAR). https://doi.org/10.18608/hla17.009

Rosé, C., Wang, Y., Cui, Y., Arguello, J., Stegmann, K., Weinberger, A. and Fischer, F.(2008). Analyzing collaborative learning processes automatically: Exploiting the advances of computational linguistics in computer-supported collaborative learning. International Journal of Computer-Supported Collaborative Learning, 3(3), 237-271. https://doi.org/10.1007/s11412-007-9034-0

Rossi, L. A., & Gnawali, O. (2014, August 13–15). Language independent analysis and classification of discussion threads in Coursera MOOC forums. In J. Joshi, E. Bertino, B. Thuraisingham, & L. Liu (Eds.), Proceedings of the IEEE International Conference on Information Reuse and Integration (IEEE IRI 2014) (pp. 654–661). IEEE Systems, Man, and Cybernetics Society (SMC). https://doi.org/10.1109/IRI.2014.7051952

Rovai, A. (2007). Facilitating online discussions effectively. The Internet and Higher Education, 10(1), 77–88. https://doi.org/10.1016/j.iheduc.2006.10.001

Ruipérez-Valiente, J., Halawa, S., Slama, R., & Reich, J. (2020). Using multi-platform learning analytics to compare regional and global MOOC learning in the Arab world. Computers & Education, 146, Article 103776. https://doi.org/10.1016/j.compedu.2019.103776

Santos, J. L., Klerkx, J., Duval, E., Gago, D., & Rodríguez, L. (2014, March). Success, activity and drop-outs in MOOCs an exploratory study on the UNED COMA courses. In Proceedings of the Fourth International Conference on Learning Analytics and Knowledge (pp. 98–102). Association for Computing Machinery. https://doi.org/10.1145/2567574.2567627

Scardamalia, M., & Bereiter, C. (2014). Knowledge building and knowledge creation: Theory, pedagogy, and technology. In R. Sawyer (Ed.), The Cambridge handbook of the learning sciences (pp. 397–417). Cambridge University Press. https://doi.org/10.1017/CBO9781139519526.025

Soter, A. O., Wilkinson, I. A., Murphy, P. K., Rudge, L., Reninger, K., & Edwards, M. (2008). What the discourse tells us: Talk and indicators of high-level comprehension. International Journal of Educational Research, 47(6), 372–391. https://doi.org/10.1016/j.ijer.2009.01.001

Tang, H., Xing, W., & Pei, B. (2018). Exploring the temporal dimension of forum participation in MOOCs. Distance Education, 39(3), 353–372. https://doi.org/10.1080/01587919.2018.1476841

Thomas, M. (2002). Learning within incoherent structures: The space of online discussion forums. Journal of Computer Assisted Learning, 18(3), 351–366. https://doi.org/10.1046/j.0266-4909.2002.03800.x

Tirado, R., Hernando, A., & Aguaded, J. I. (2012). The effect of centralization and cohesion on the social construction of knowledge in discussion forums. Interactive Learning Environments, 23(3), 293–316. https://doi.org/10.1080/10494820.2012.745437

Tseng, S., Tsao, Y., Yu, L., Chan, C., & Lai, K. (2016). Who will pass? Analyzing learner behaviors in MOOCs. Research and Practice in Technology Enhanced Learning, 11, Article 8. https://doi.org/10.1186/s41039-016-0033-5

Turkle, S. (2011). Alone together: Why we expect more from technology and less from each other. Basic Books.

Vygotsky. L. S. (1986). Thought and language. MIT Press. https://mitpress.mit.edu/books/thought-and-language

Wang, X., Yang, D., Wen, M., Koedinger, K., & Rosé, C. P. (2015, June 26–29). Investigating how student’s cognitive behavior in MOOC discussion forums affect learning gains. In EDM ’15: Proceedings of the 8th International Conference on Educational Data Mining (pp. 226–233). International Educational Data Mining Society. https://files.eric.ed.gov/fulltext/ED560568.pdf

Wertsch, J. V. (1985). Vygotsky and the social formation of mind. Harvard University Press. https://www.hup.harvard.edu/catalog.php?isbn=9780674943513

Winne, P. (2017). Learning analytics for self-regulated learning. In C. Lang, G. Siemens, A. F. Wise, & D. Gaevic (Eds.), Handbook of learning analytics (pp. 241–249). Society for Learning Analytics Research (SoLAR). https://doi.org/10.18608/hla17.021

Wise, A. F., Azevedo, R., Stegmann, K., Malmberg, J., Rosé, C. P., Mudrick, N., Taub, M., Martin, S. A., Farnsworth, J., Mu, J., Järvenoja, H., Järvelä, S., Wen, M., Yang, D., & Fischer, F. (2015). CSCL and learning analytics: Opportunities to support social interaction, self-regulation and socially shared regulation. In CSCL 2015 Proceedings (pp. 607–614). International Society of the Learning Sciences.

Wise, A & Cui, Y. (2018). Unpacking the relationship between discussion forum participation and learning in MOOCs: content is key. In Proceedings of the 8th International Conference on Learning Analytics and Knowledge (pp.330-339). Association for Computing Machinery. https//doi.org/10.1145/3170358.3170403

Wise, A. F., Cui, Y., Jin, W., & Vytasek, J. (2017). Mining for gold: Identifying content-related MOOC discussion threads across domains through linguistic modeling. The Internet and Higher Education, 32, 11–28, https://doi.org/10.1016/j.iheduc.2016.08.001

Wise, A., Hausknecht, S. and Zhao, Y., (2014). Attending to others’ posts in asynchronous discussions: Learners’ online “listening” and its relationship to speaking. International Journal of Computer-Supported Collaborative Learning, 9(2), 185-209. https://doi.org/10.1007/s11412-014-9192-9

Wu, D., & Hiltz, S. (2004). Predicting learning from asynchronous online discussions. Journal of Asynchronous Learning Networks, 8(2), 139–152. https://doi.org/10.24059/olj.v8i2.1832

Zhu, M., Bergner, Y., Zhang, Y., Baker, R., Wang, Y., & Paquette, L. (2016, April 25–29). Longitudinal engagement, performance, and social connectivity: A MOOC case study using exponential random graph models. In LAK ’16, Proceedings of the Sixth International Conference on Learning Analytics & Knowledge (pp. 223–230). Association for Computing Machinery. https://doi.org/10.1145/2883851.2883934

Zhu, M., Sari, A., & Lee, M. (2020). A comprehensive systematic review of MOOC research: Research techniques, topics, and trends from 2009 to 2019. Educational Technology Research and Development, 68(4), 1685–1710. https://doi.org/10.1007/s11423-020-09798-x

Zou, W., Hu, X., Pan, Z., Li, C., Cai, Y. and 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, Article 106582. https://doi.org/10.1016/j.chb.2020.106582

Published

2022-11-01

How to Cite

Lemay, D., Doleck, T., & Brinton, C. (2022). SLOAN: Social Learning Optimization Analysis of Networks. The International Review of Research in Open and Distributed Learning, 23(4), 93–122. https://doi.org/10.19173/irrodl.v23i4.6484

Issue

Section

Research Articles