Exploring Cognitive Presence in Online Collaborative Knowledge-Building: Structural, Temporal, and Social Perspectives
DOI:
https://doi.org/10.19173/irrodl.v27i1.8931Keywords:
online collaborative knowledge building, cognitive presence, structural pattern, temporal sequence, social connectionAbstract
Collaborative knowledge-building is an important mode of learning in which students’ cognitive presence has a significant impact on learning outcomes. To better understand how cognitive presence influences collaborative learning, this study applied three complementary analytic approaches: epistemic network analysis, which maps how ideas are connected in discussions; sequential pattern mining, which identifies temporal sequences; and social network analysis, which examines the interaction patterns and roles among group members. Using data from 37 students divided into 8 groups in a university course on academic reading and writing, we compared high-performing groups (HPGs) and low-performing groups (LPGs). The results showed that HPGs demonstrated stronger exploratory, integrative, and problem-solving abilities in their cognitive networks, with members actively exchanging ideas, questioning, and summarizing. In contrast, LPGs relied more on encouragement and reminders to sustain discussions. Furthermore, HPGs displayed more complex and varied behavioral sequences and clearer leadership and facilitation roles within their social networks, whereas LPGs showed simpler and less developed interaction patterns and lacked core members in their networks. These findings provide insights for instructors on how to better design and guide group knowledge-building to enhance online collaborative learning outcomes.
References
Ba, S., Hu, X., Stein, D., & Liu, Q. (2023). Assessing cognitive presence in online inquiry‐based discussion through text classification and epistemic network analysis. British Journal of Educational Technology, 54(1), 247–266. https://doi.org/10.1111/bjet.13285
Baanqud, N. S., Al-Samarraie, H., Alzahrani, A. I., & Alfarraj, O. (2020). Engagement in cloud-supported collaborative learning and student knowledge construction: A modeling study. International Journal of Educational Technology in Higher Education, 17, Article 56. https://doi.org/10.1186/s41239-020-00232-z
Bhutoria, A., & Aljabri, N. (2022). Patterns of cognitive returns to information and communication technology (ICT) use of 15-year-olds: Global evidence from a hierarchical linear modeling approach using PISA 2018. Computers & Education, 181, Article 104447. https://doi.org/10.1016/j.compedu.2022.104447
Chen, X., Xie, H., Qin, S. J., Wang, F. L., & Hou, Y. (2025). Artificial intelligence‐supported student engagement research: Text mining and systematic analysis. European Journal of Education, 60(1), Article e70008. https://doi.org/10.1111/ejed.70008
Chen, X., Zou, D., Cheng, G., & Xie, H. (2022). Understanding classroom interaction using epistemic and social network analysis. In R. C. Li, S. K. S. Cheung, P. H. F. Ng, L. P. Wong, & F. L. Wang (Eds.), Blended learning: Engaging students in the new normal era. ICBL 2022. Springer. https://doi.org/10.1007/978-3-031-08939-8_14
Cherbow, K., & McNeill, K. L. (2022). Planning for student-driven discussions: A revelatory case of curricular sensemaking for epistemic agency. Journal of the Learning Sciences, 31(3), 408–457. https://doi.org/10.1080/10508406.2021.2024433
Elmoazen, R., Saqr, M., Hirsto, L., & Tedre, M. (2024). Capturing temporal pathways of collaborative roles: A multilayered analytical approach using community of inquiry. International Journal of Computer-Supported Collaborative Learning, 20, 41–77. https://doi.org/10.1007/s11412-024-09431-6
Fougt, S. S., Siebert-Evenstone, A., Eagan, B., Tabatabai, S., & Misfeldt, M. (2018, March). Epistemic network analysis of students’ longer written assignments as formative/summative evaluation. In A. Pardo, K. Bartimote-Aufflick, & G. Lynch (Eds.), Proceedings of the 8th international conference on learning analytics and knowledge (pp. 126–130). ACM. https://doi.org/10.1145/3170358.3170414
Gao, Q., Zhang, S., Cai, Z., Liu, K., Hui, N., & Tong, M. (2022). Understanding student teachers’ collaborative problem solving competency: Insights from process data and multidimensional item response theory. Thinking Skills and Creativity, 45, Article 101097. https://doi.org/10.1016/j.tsc.2022.101097
He, Q., Borgonovi, F., & Paccagnella, M. (2021). Leveraging process data to assess adults’ problem-solving skills: Using sequence mining to identify behavioral patterns across digital tasks. Computers & Education, 166, Article 104170. https://doi.org/10.1016/j.compedu.2021.104170
Hong, Y.-C., & Choi, I. (2019). Relationship between student designers’ reflective thinking and their design performance in bioengineering project: Exploring reflection patterns between high and low performers. Educational Technology Research and Development, 67, 337–360. https://doi.org/10.1007/s11423-018-9618-6
Jiang, J.-P., Hu, J.-Y., Zhang, Y.-B., & Yin, X.-C. (2023). Fostering college students’ critical thinking skills through peer assessment in the knowledge building community. Interactive Learning Environments, 31(10), 6480–6496. https://doi.org/10.1080/10494820.2022.2039949
Liu, S., Kang, L., Liu, Z., Zhao, L., Yang, Z., & Su, Z. (2023). Exploring the relationships between students’ network characteristics, discussion topics and learning outcomes in a course discussion forum. Journal of Computing in Higher Education, 35(3), 487–520. https://doi.org/10.1007/s12528-022-09335-0
Liu, Z., Zhang, N., Peng, X., Liu, S., Yang, Z., Peng, J., Su, Z., & Chen, J. (2022). Exploring the relationship between social interaction, cognitive processing and learning achievements in a MOOC discussion forum. Journal of Educational Computing Research, 60(1), 132–169. https://doi.org/10.1177/07356331211027300
Maranna, S., Willison, J., Joksimovic, S., Parange, N., & Costabile, M. (2022). Factors that influence cognitive presence: A scoping review. Australasian Journal of Educational Technology, 38(4), 95–111. https://doi.org/10.14742/ajet.7878
Moon, J., McNeill, L., Edmonds, C. T., Banihashem, S. K., & Noroozi, O. (2024). Using learning analytics to explore peer learning patterns in asynchronous gamified environments. International Journal of Educational Technology in Higher Education, 21, 45. https://doi.org/10.1186/s41239-024-00476-z
Morueta, R. T., López, P. M., Gómez, Á. H., & Harris, V. W. (2016). Exploring social and cognitive presences in communities of inquiry to perform higher cognitive tasks. The Internet and Higher Education, 31, 122–131. https://doi.org/10.1016/j.iheduc.2016.07.004
Norz, L.-M., Dornauer, V., Hackl, W. O., & Ammenwerth, E. (2023). Measuring social presence in online-based learning: An exploratory path analysis using log data and social network analysis. The Internet and Higher Education, 56, Article 100894. https://doi.org/10.1016/j.iheduc.2022.100894
Onrubia, J., Roca, B., & Minguela, M. (2022). Assisting teacher collaborative discourse in professional development: An analysis of a facilitator's discourse strategies. Teaching and Teacher Education, 113, Article 103667. https://doi.org/10.1016/j.tate.2022.103667
Sharma, P., Akgun, M., & Li, Q. (2024). Understanding student interaction and cognitive engagement in online discussions using social network and discourse analyses. Educational Technology Research and Development, 72(5), 2631–2654. https://doi.org/10.1007/s11423-023-10261-w
Shea, P., Richardson, J., & Swan, K. (2022). Building bridges to advance the community of inquiry framework for online learning. Educational Psychologist, 57(3), 148–161. https://doi.org/10.1080/00461520.2022.2089989
Song, Y., Cheng, B., Zhu, J., & Hu, X. (2022). Exploring the collective process of classroom dialogue using sequential pattern mining technique. International Journal of Educational Research, 115, Article 102050. https://doi.org/10.1016/j.ijer.2022.102050
Vygotsky, L. S. (1978). Mind in society: The development of higher psychological process. Harvard University Press.
Wilson, E. C., & Berge, Z. L. (2023). Educational experience and instructional design effectiveness within the Community of Inquiry framework. The International Review of Research in Open and Distributed Learning, 24(1), 159–174. https://doi.org/10.19173/irrodl.v24i1.6751
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. https://doi.org/10.1016/j.chb.2018.01.034
Wu, B., & Wu, C. (2021). Research on the mechanism of knowledge diffusion in the MOOC learning forum using ERGMs. Computers & Education, 173, Article 104295. https://doi.org/10.1016/j.compedu.2021.104295
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. https://doi.org/10.1016/j.iheduc.2017.08.003
Zhang, S., Gao, Q., Sun, M., Cai, Z., Li, H., Tang, Y., & Liu, Q. (2022). Understanding student teachers’ collaborative problem solving: Insights from an epistemic network analysis (ENA). Computers & Education, 183, Article 104485. https://doi.org/10.1016/j.compedu.2022.104485
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, Article 106582. https://doi.org/10.1016/j.chb.2020.106582
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