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Exploring the relationships between students’ network characteristics, discussion topics and learning outcomes in a course discussion forum

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Abstract

Understanding the relationship between interactive behaviours and discourse content has critical implications for instructors' design and facilitation of collaborative discussion activities in the online discussion forum (ODF). This paper adopts social network analysis (SNA) and epistemic network analysis (ENA) methods to jointly investigate the relationships between students’ network characteristics, discussion topics, and learning outcomes in a course discussion forum. Discourse data from 207 participants were included in this study. The findings indicated that (1) the interactive network generated in the collaborative discussion activities was sparsely connected, and there was limited information exchange between instructors and students; (2) students’ discussion topics were mainly related to the learning content; (3) compared with the isolated group, students in the leader, mediator, and animator groups were more concerned about topics related to the learning content; and (4) students who discussed more topics related to the learning content performed better than the students who discussed more topics related to learning methods and social interactions. The learning outcomes of the influencer and leader groups were significantly higher than those of the peripheral and isolated groups. However, there was no significant correlation between students’ individual centrality and their learning outcomes. The findings enrich the ODF research on the comprehensive identification of interactive behaviours and discourse content in the process of collaborative discussion activities and on the discussion topic differences between different role groups. The study findings also have practical implications for instructors to design effective instructional interventions aimed at improving the quality of collaboration in the ODF.

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References

  • Ahmad, M., Junus, K., & Santoso, H. B. (2022). Automatic content analysis of asynchronous discussion forum transcripts: A systematic literature review. Education and Information Technologies, 1–56.

  • Akyol, Z., & Garrison, D. R. (2019). The development of a community of inquiry over time in an online course: Understanding the progression and integration of social, cognitive and teaching presence. Online Learning Journal, 12, 3–22.

    Google Scholar 

  • Almatrafi, O., Johri, A., & Rangwala, H. (2018). Needle in a haystack: Identifying learner posts that require urgent response in MOOC discussion forums. Computers & Education, 118, 1–9.

    Article  Google Scholar 

  • An, Y.-J., & Frick, T. (2006). Student perceptions of asynchronous computer-mediated communication in face-to-face courses. Journal of Computer-Mediated Communication, 11(2), 485–499.

    Article  Google Scholar 

  • Anderson, T., & Dron, J. (2011). Three generations of distance education pedagogy. International Review of Research in Open and Distance Learning, 12(3), 80–97.

    Article  Google Scholar 

  • Anderson, T., Rourke, L., Garrison, D. R., & Archer, W. (2001). Assessing teaching presence in a computer conferencing context. Journal of Asynchronous Learning Network, 5(2), 1–17.

    Google Scholar 

  • Andrist, S., Ruis, A. R., & Shaffer, D. W. (2018). A network analytic approach to gaze coordination during a collaborative task. Computers in Human Behavior, 89, 339–348.

    Article  Google Scholar 

  • Archer, W. (2010). Beyond online discussions: Extending the community of inquiry framework to entire courses. Internet and Higher Education, 13, 69–69.

    Article  Google Scholar 

  • Bandura, A. (2001). Social cognitive theory: An agentic perspective. Annual Review of Psychology, 52, 1–26.

    Article  Google Scholar 

  • Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022.

    Google Scholar 

  • Brown, P. F., Della Pietra, S. A., Della Pietra, V. J., Lai, J. C., & Mercer, R. L. (1992). An estimate of an upper bound for the entropy of English. Computational Linguistics, 18(1), 31–40.

    Google Scholar 

  • Chen, C. M., & You, Z. L. (2018). Community detection with opinion leaders’ identification for promoting collaborative problem-based learning performance. British Journal of Educational Technology, 0(0), 1–19.

  • Cleveland-Innes, M., & Campbell, P. (2012). Emotional presence, learning, and the online learning environment. International Review of Research in Open and Distance Learning, 13(4), 269–292.

    Article  Google Scholar 

  • Cleveland-Innes, M., Garrison, R., & Kinsel, E. (2009). The role of learner in an online community of inquiry: Responding to the challenges of first-time online learners. Solutions and Innovations in Web-Based Technologies for Augmented Learning: Improved Platforms, Tools, and Applications, 1, 1–14.

    Google Scholar 

  • Csanadi, A., Eagan, B., Kollar, I., Shaffer, D. W., & Fischer, F. (2018). When coding-and-counting is not enough: Using epistemic network analysis (ENA) to analyze verbal data in CSCL research. International Journal of Computer-Supported Collaborative Learning, 13(4), 419–438.

    Article  Google Scholar 

  • Dado, M., & Bodemer, D. (2017). A review of methodological applications of social network analysis in computer-supported collaborative learning. Educational Research Review, 22, 159–180.

    Article  Google Scholar 

  • Ding, L., Er, E., & Orey, M. (2018). An exploratory study of student engagement in gamified online discussions. Computers and Education, 120, 213–226.

    Article  Google Scholar 

  • 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, 104124.

    Article  Google Scholar 

  • Flor, N., Gunawardena, C., Gómez, D., & Sánchez, D. (2016). Analyzing social construction of knowledge online by employing interaction analysis, learning analytics, and social network analysis. The Quarterly Review of Distance Education, 17(3), 35–60.

    Google Scholar 

  • Fu, X., Yang, K., Huang, J. Z., & Cui, L. (2015). Dynamic non-parametric joint sentiment topic mixture model. Knowledge-Based Systems, 82, 102–114.

    Article  Google Scholar 

  • Galikyan, I., Admiraal, W., & Kester, L. (2021). MOOC discussion forums: The interplay of the cognitive and the social. Computers & Education, 165, 104133.

    Article  Google Scholar 

  • Garrison, D. R. (2007). Online community of inquiry review: Social, cognitive, and teaching presence issues. Journal of Asynchronous Learning Networks, 11(1), 61–72.

    Google Scholar 

  • Garrison, D. R. (2012). Theoretical foundations and epistemological insights of the Community of Inquiry. Educational Communities of Inquiry: Theoretical Framework, Research and Practice, 1, 1–11.

    Google Scholar 

  • Garrison, D. R., & Akyol, Z. (2009). Role of instructional technology in the transformation of higher education. Journal of Computing in Higher Education, 21(1), 19–30.

    Article  Google Scholar 

  • Garrison, D. R., & Akyol, Z. (2015). Toward the development of a metacognition construct for communities of inquiry. Internet and Higher Education, 24, 66–71.

    Article  Google Scholar 

  • Garrison, D. R., Anderson, T., & Archer, W. (2001). Critical thinking, cognitive presence, and computer conferencing in distance education. International Journal of Phytoremediation, 21(1), 7–23.

    Google Scholar 

  • Garrison, D. R., & Cleveland-Innes, M. (2005). Facilitating cognitive presence in online learning: Interaction is not enough. International Journal of Phytoremediation, 21(1), 133–148.

    Google Scholar 

  • Garrison, D. R., & Vaughan, N. D. (2013). Institutional change and leadership associated with blended learning innovation: Two case studies. Internet and Higher Education, 18, 24–28.

    Article  Google Scholar 

  • Garrison, R., Anderson, T., & Archer, W. (2000). Critical Inquiry in a text-based environment. The Internet and Higher Education, 2(2), 87–105.

    Google Scholar 

  • Gašević, D., Joksimović, 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, 562–577.

    Article  Google Scholar 

  • Haythornthwaite, C. (1996). Social network analysis: An approach and technique for the study of information exchange. Library and Information Science Research, 18(4), 323–342.

    Article  Google Scholar 

  • Heilporn, G., Lakhal, S., & Bélisle, M. (2021). An examination of teachers’ strategies to foster student engagement in blended learning in higher education. International Journal of Educational Technology in Higher Education, 18(1), 1–25.

    Article  Google Scholar 

  • Himelboim, I., Smith, M. A., Rainie, L., Shneiderman, B., & Espina, C. (2017). Classifying twitter topic-networks using social network analysis. Social Media and Society, 3(1), 1–13.

    Google Scholar 

  • Joksimović, S., Dowell, N., Poquet, O., Kovanović, V., Gašević, D., Dawson, S., & Graesser, A. C. (2018). Exploring development of social capital in a CMOOC through language and discourse. The Internet and Higher Education, 36, 54–64.

    Article  Google Scholar 

  • Kadushin, C. (2012). Understanding social networks: Theories, concepts, and findings. Oxford University Press.

    Google Scholar 

  • Katrina, D., & Meyer, A. (2003). Face-to-face versus threaded discussions: The role of time and higher-order thinking. Journal of Asynchronous Learning Network, 7(3), 55–65.

    Google Scholar 

  • Kelley, T. L. (1939). The selection of upper and lower groups for the validation of test items. Journal of Educational Psychology, 30(1), 17–24.

    Article  Google Scholar 

  • Knaub, A. V., Henderson, C., & Fisher, K. Q. (2018). Finding the leaders: An examination of social network analysis and leadership identification in STEM education change. International Journal of STEM Education, 5(1), 5–26.

    Article  Google Scholar 

  • Koszalka, T. A., Pavlov, Y., & Wu, Y. (2021). The informed use of pre-work activities in collaborative asynchronous online discussions: The exploration of idea exchange, content focus, and deep learning. Computers & Education, 161, 104067.

    Article  Google Scholar 

  • Lazega, E., Wasserman, S., & Faust, K. (2006). Social network analysis: Methods and applications. Cambridge University Press.

    Google Scholar 

  • Lee, J., & Martin, L. (2017). Investigating students’ perceptions of motivating factors of online class discussions. International review of research in open and distributed learning. International Review of Research in Open and Distributed Learning, 18(5), 148–172.

    Article  Google Scholar 

  • Lee, J., & 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.

    Article  Google Scholar 

  • Li, J., Wong, S. C., Yang, X., & Bell, A. (2020). Using feedback to promote student participation in online learning programs: Evidence from a quasi-experimental study. Educational Technology Research and Development, 68(1), 485–510.

    Article  Google Scholar 

  • Lin, X., Hu, X., Hu, Q., & Liu, Z. (2016). A social network analysis of teaching and research collaboration in a teachers’ virtual learning community. British Journal of Educational Technology, 47(2), 302–319.

    Article  Google Scholar 

  • Liu, C. C., Chen, Y. C., & Diana Tai, S. J. (2017). A social network analysis on elementary student engagement in the networked creation community. Computers & Education, 115(300), 114–125.

    Article  Google Scholar 

  • Liu, Z., Kong, X., Liu, S., Yang, Z., & Zhang, C. (2022a). Looking at MOOC discussion data to uncover the relationship between discussion pacings, learners’ cognitive presence and learning achievements. Education and Information Technologies, 2, 1–24.

    Google Scholar 

  • Liu, Z., Mu, R., Yang, Z., Peng, X., Liu, S., & Chen, J. (2022b). Modeling temporal cognitive topic to uncover learners’ concerns under different cognitive engagement patterns. Interactive Learning Environments, 1, 1–18.

    Google Scholar 

  • Marcos-García, J. A., Martínez-Monés, A., & Dimitriadis, Y. (2015). DESPRO: A method based on roles to provide collaboration analysis support adapted to the participants in CSCL situations. Computers & Education, 82, 335–353.

    Article  Google Scholar 

  • Marra, R. M., Moore, J. L., & Klimczak, A. K. (2004). Content analysis of online discussion forums: A comparative analysis of protocols. Educational Technology Research and Development, 52(2), 23–40.

    Article  Google Scholar 

  • Nash, P., & Shaffer, D. W. (2013). Epistemic trajectories: Mentoring in a game design practicum. Instructional Science, 41(4), 745–771.

    Article  Google Scholar 

  • Oh, E. G., Huang, W. H. D., Hedayati Mehdiabadi, A., & Ju, B. (2018). Facilitating critical thinking in asynchronous online discussion: Comparison between peer- and instructor-redirection. Journal of Computing in Higher Education, 30(3), 489–509.

    Article  Google Scholar 

  • Ouyang, F., Chen, S., & Li, X. (2021). Effect of three network visualizations on students’ social-cognitive engagement in online discussions. British Journal of Educational Technology, 0, 1–21.

  • Ouyang, F., & Chang, Y. H. (2019). The relationships between social participatory roles and cognitive engagement levels in online discussions. British Journal of Educational Technology, 50(3), 1396–1414.

    Article  Google Scholar 

  • Ouyang, F., & Scharber, C. (2017). The influences of an experienced instructor’s discussion design and facilitation on an online learning community development: A social network analysis study. Internet and Higher Education, 35, 34–47.

    Article  Google Scholar 

  • Peng, X., & Xu, Q. (2020). Investigating learners’ behaviors and discourse content in MOOC course reviews. Computers & Education, 143, 1–14.

    Article  Google Scholar 

  • Poquet, O., Nguyen, Q., Kovanovic, V., Brooks, C., Dawson, S., & Biotteau, A. (2022). Grade-based similarity prevails in online course forums at scale. Computers & Education, 178, 104401.

    Article  Google Scholar 

  • Reychav, I., Raban, D. R., & McHaney, R. (2018). Centrality measures and academic achievement in computerized classroom social networks: An empirical investigation. Journal of Educational Computing Research, 56(4), 589–618.

    Article  Google Scholar 

  • Rolim, V., Ferreira, R., Lins, R. D., & Gǎsević, D. (2019). A network-based analytic approach to uncovering the relationship between social and cognitive presences in communities of inquiry. The Internet and Higher Education, 42, 53–65.

    Article  Google Scholar 

  • Rourke, L., Anderson, T., Garrison, D. R., & Archer, W. (1999). Assessing social presence in asynchronous text-based computer conferencing. Journal of Distance Education, 14(2), 50–71.

    Google Scholar 

  • Rourke, L., & Kanuka, H. (2007). Barriers to online critical discourse. International Journal of Computer-Supported Collaborative Learning, 2(1), 105–126.

    Article  Google Scholar 

  • Saqr, M., Fors, U., & Nouri, J. (2018a). Using social network analysis to understand online problem-based learning and predict performance. PLoS ONE, 13(9), 1–20.

    Article  Google Scholar 

  • Saqr, M., Fors, U., Tedre, M., & Nouri, J. (2018b). How social network analysis can be used to monitor online collaborative learning and guide an informed intervention. PLoS ONE, 13(3), 1–23.

    Article  Google Scholar 

  • Shaffer, D. W., Collier, W., & Ruis, A. R. (2016). A tutorial on epistemic network analysis: Analyzing the structure of connections in cognitive, social, and interaction data. Journal of Learning Analytics, 3(3), 9–45.

    Article  Google Scholar 

  • Shaffer, D. W., Hatfield, D., Svarovsky, G. N., Nash, P., Nulty, A., Bagley, E., Frank, K., Rupp, A. A., & Mislevy, R. (2009). Epistemic network analysis: A prototype for 21st-century assessment of learning. International Journal of Learning and Media, 1(2), 33–53.

    Article  Google Scholar 

  • Shaffer, D. W., & Ruis, A. R. (2017). Epistemic network analysis: A worked example of theory-based learning analytics. In C. Lang, G. Siemens, A. Wise, & D. Gasevic (Eds.), Handbook of learning analytics (pp. 175–187). Society for Learning Analytics Research (SoLAR).

    Chapter  Google Scholar 

  • Stepanyan, K., Mather, R., & Dalrymple, R. (2014). Culture, role and group work: A social network analysis perspective on an online collaborative course. British Journal of Educational Technology, 45(4), 676–693.

    Article  Google Scholar 

  • Tobarra, L., Robles-Gómez, A., Ros, S., Hernández, R., & Caminero, A. C. (2014). Analyzing the students’ behavior and relevant topics in virtual learning communities. Computers in Human Behavior, 31(1), 659–669.

    Article  Google Scholar 

  • Tsiotakis, P., & Jimoyiannis, A. (2016). Critical factors towards analysing teachers’ presence in on-line learning communities. Internet and Higher Education, 28, 45–58.

    Article  Google Scholar 

  • Vaughan, N. D. (2010). A blended community of inquiry approach: Linking student engagement and course redesign. Internet and Higher Education, 13, 60–65.

    Article  Google Scholar 

  • Vaughan, N., & Garrison, D. R. (2005). Creating cognitive presence in a blended faculty development community. Internet and Higher Education, 8(1), 1–12.

    Article  Google Scholar 

  • Vaughan, N., & Garrison, R. (2013). A blended faculty community of inquiry: Linking leadership, course redesign, and evaluation. Canadian Journal of University Continuing Education, 32(2), 66–92.

    Article  Google Scholar 

  • Wise, A. F., & Cui, Y. (2018). Learning communities in the crowd: Characteristics of content related interactions and social relationships in MOOC discussion forums. Computers & Education, 122, 221–242.

    Article  Google Scholar 

  • Wise, A. F., & Hsiao, Y. T. (2019). Self-regulation in online discussions: Aligning data streams to investigate relationships between speaking, listening, and task conditions. Computers in Human Behavior, 96, 273–284.

    Article  Google Scholar 

  • Wu, B., & Wu, C. (2021). Research on the mechanism of knowledge diffusion in the MOOC learning forum using ERGMs. Computers & Education, 173, 104295.

    Article  Google Scholar 

  • Wu, J. Y., & Nian, M. W. (2021). The dynamics of an online learning community in a hybrid statistics classroom over time: Implications for the question-oriented problem-solving course design with the social network analysis approach. Computers & Education, 166, 104120.

    Article  Google Scholar 

  • Xie, K., Di Tosto, G., Lu, L., & Cho, Y. S. (2018). Detecting leadership in peer-moderated online collaborative learning through text mining and social network analysis. The Internet and Higher Education, 38, 9–17.

    Article  Google Scholar 

  • Xu, B., Chen, N. S., & Chen, G. (2020). Effects of teacher role on student engagement in WeChat-Based online discussion learning. Computers & Education, 157, 103956.

    Article  Google Scholar 

  • Yang, B., Tang, H., Hao, L., & Rose, J. R. (2022). Untangling chaos in discussion forums: A temporal analysis of topic-relevant forum posts in MOOCs. Computers & Education, 178, 104402.

    Article  Google Scholar 

  • Yang, X., Li, J., & Xing, B. (2018). Behavioral patterns of knowledge construction in online cooperative translation activities. The Internet and Higher Education, 36, 13–21.

    Article  Google Scholar 

  • Ye, D., & Pennisi, S. (2022). Analysing interactions in online discussions through social network analysis. Journal of Computer Assisted Learning, 38(3), 784–796.

    Article  Google Scholar 

  • Zhang, S., Liu, Q., & Cai, Z. (2019). Exploring primary school teachers’ technological pedagogical content knowledge (TPACK) in online collaborative discourse: An epistemic network analysis. British Journal of Educational Technology, 50(6), 3437–3455.

    Article  Google Scholar 

  • Zhang, S., Liu, Q., Chen, W., Wang, Q., & Huang, Z. (2017). Interactive networks and social knowledge construction behavioral patterns in primary school teachers’ online collaborative learning activities. Computers & Education, 104, 1–17.

    Article  Google Scholar 

  • Zhang, S., Wen, Y., & Liu, Q. (2022). Exploring student teachers’ social knowledge construction behaviors and collective agency in an online collaborative learning environment. Interactive Learning Environments, 30(3), 539–551.

    Article  Google Scholar 

  • Zheng, L., Zhen, Y., Niu, J., & Zhong, L. (2022). An exploratory study on fade-in versus fade-out scaffolding for novice programmers in online collaborative programming settings. Journal of Computing in Higher Education, 19, 1–28.

    Google Scholar 

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Acknowledgements

This work was supported by the Research Funds from National Natural Science Foundation of China [grant number 61977030, 61937001], National Key Research & Development Program of China [grant number 2017YFB1401303], Hubei Provincial Natural Science Foundation of China [grant number 2018CFB518] and the Fundamental Research Funds of the Central Universities [grant number CCNU20TS032].

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  1. National Engineering Research Center for E-Learning, Central China Normal University, Luoyu Road, Wuhan, 430079, Hubei, People’s Republic of China

    Sannyuya Liu, Lingyun Kang, Zhi Liu, Liang Zhao, Zongkai Yang & Zhu Su

  2. National Engineering Laboratory for Educational Big Data, Central China Normal University, Hubei, Wuhan, People’s Republic of China

    Sannyuya Liu, Zhi Liu, Liang Zhao, Zongkai Yang & Zhu Su

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  1. Sannyuya Liu
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Table 8 Summary statistics of the discussion topics in the course
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Liu, S., Kang, L., Liu, Z. et al. Exploring the relationships between students’ network characteristics, discussion topics and learning outcomes in a course discussion forum. J Comput High Educ 35, 487–520 (2023). https://doi.org/10.1007/s12528-022-09335-0

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