Mengtong Xiang
ABSTRACT
The concept of knowledge convergence refers to building a shared cognitive understanding among individuals through social interaction. It is considered as a crucial aspect of collaborative learning and plays a significant role in the process of consensus building. However, there is a lack of research exploring knowledge convergence in the context of online learning, especially in cross-cultural settings. Collaborative learning primarily focuses on constructing cognitive knowledge representations at the individual level, while online learning emphasizes the social mechanism of knowledge diffusion and flow at the collective level. This study aims to investigate individual online knowledge convergence through content analysis of social annotations within a cross-cultural course and using Simulation Investigation for Empirical Network Analysis (SIENA) to depict the collective social interaction. The findings reveal that online knowledge convergence exhibits distinct characteristics, quick consensus building could foster a harmonious community and similar experiences compensated for limited interactions, triggering deep consensus. Individual convergence leads to emergent properties such as reciprocity and transitivity within a dynamic collective interactive network, which can serve as novel indicators for evaluating knowledge convergence at the collective level. By approaching knowledge convergence from multifaceted perspectives, this study contributes to a comprehensive understanding of the concept across diverse learning contexts.
- [1] America, B.Y.U., United States of et al. 2021. Student belongingness in higher education: Lessons for Professors from the COVID-19 pandemic. Journal of University Teaching and Learning Practice. 18, 4 (2021), 8–20. DOI:https://doi.org/10.53761/1.18.4.2.Google Scholar
Cross Ref
- [2] Arvaja, M. and Hämäläinen, R. 2021. Dialogicality in making sense of online collaborative interaction: A conceptual perspective. The Internet and Higher Education. 48, (2021), 100771. DOI:https://doi.org/10.1016/j.iheduc.2020.100771.Google ScholarCross Ref
- [3] Chan, C.K.K. 2001. Peer collaboration and discourse patterns in learning from incompatible information. Instructional Science. 29, 6 (2001), 443–479. DOI:https://doi.org/10.1023/a:1012099909179.Google ScholarCross Ref
- [4] Chen, B. and Poquet, O. 2022. Networks in Learning Analytics: Where Theory, Methodology, and Practice Intersect. Journal of learning analytics. 9, 1 (2022), 1–12. DOI:https://doi.org/10.18608/jla.2022.7697.Google ScholarCross Ref
- [5] Farrokhnia, M. et al. 2019. Computer-supported collaborative concept mapping: The effects of different instructional designs on conceptual understanding and knowledge co-construction. Computers & Education. 142, (2019), 103640. DOI:https://doi.org/10.1016/j.compedu.2019.103640.Google ScholarDigital Library
- [6] Haro, A.V. et al. 2022. Argumentation Competence: Students’ Argumentation Knowledge, Behavior and Attitude and their Relationships with Domain-Specific Knowledge Acquisition. Journal of Constructivist Psychology. 35, 1 (2022), 123–145. DOI:https://doi.org/10.1080/10720537.2020.1734995.Google ScholarCross Ref
- [7] Ickes, W. and Gonzalez, R. 1994. “Social” Cognition and Social Cognition. Small Group Research. 25, 2 (1994), 294–315. DOI:https://doi.org/10.1177/1046496494252008.Google ScholarCross Ref
- [8] Jeong, H. and Chi, M.T.H. 2007. Knowledge convergence and collaborative learning. Instructional Science. 35, 4 (2007), 287–315. DOI:https://doi.org/10.1007/s11251-006-9008-z.Google ScholarCross Ref
- [9] Jo, I. et al. 2017. Three interaction patterns on asynchronous online discussion behaviours: A methodological comparison. Journal of Computer Assisted Learning. 33, 2 (2017), 106–122. DOI:https://doi.org/10.1111/jcal.12168.Google ScholarDigital Library
- [10] Li, S. et al. 2023. Diversified resource access paths in MOOCs: Insights from network analysis. Computers & Education. 204, (2023), 104869. DOI:https://doi.org/10.1016/j.compedu.2023.104869.Google ScholarDigital Library
- [11] Magnanini, S. et al. 2022. Collaborate as a flock in the organization: how selection and synthesis influence knowledge convergence within a complex adaptive system. Journal of Knowledge Management. 26, 11 (2022), 142–165. DOI:https://doi.org/10.1108/jkm-07-2021-0533.Google ScholarCross Ref
- [12] Mamun, M.A.A. et al. 2022. Exploration of learner-content interactions and learning approaches: The role of guided inquiry in the self-directed online environments. Computers & Education. 178, (2022), 104398. DOI:https://doi.org/10.1016/j.compedu.2021.104398.Google ScholarDigital Library
- [13] Mercier, E.M. 2017. The influence of achievement goals on collaborative interactions and knowledge convergence. Learning and Instruction. 50, (2017), 31–43. DOI:https://doi.org/10.1016/j.learninstruc.2016.11.006.Google ScholarCross Ref
- [14] Oleksandra,P and Shane,D 2016. Untangling MOOC Learner Networks. In Proceedings of the Sixth International Conference on Learning Analytics & Knowledge (2016), 208–212.Google Scholar
- [15] Padrós, A. et al. 2012. Measuring the Knowledge Convergence Process in the Collaborative Game MetaVals. Procedia Computer Science. 15, (2012), 193–202. DOI:https://doi.org/10.1016/j.procs.2012.10.071.Google ScholarCross Ref
- [16] Rheingold, H. 1993. A slice of life in my virtual community. Global networks: Computers and international communication. (Aug. 1993), 57–80.Google Scholar
- [17] Roschelle, J. 1992. Learning by Collaborating: Convergent Conceptual Change. Journal of the Learning Sciences. 2, 3 (1992), 235–276. DOI:https://doi.org/10.1207/s15327809jls0203_1.Google ScholarCross Ref
- [18] Saqr, M. and López-Pernas, S. 2021. Modelling diffusion in computer-supported collaborative learning: a large scale learning analytics study. International Journal of Computer-Supported Collaborative Learning. 16, 4 (2021), 441–483. DOI:https://doi.org/10.1007/s11412-021-09356-4.Google ScholarCross Ref
- [19] Scardamalia, M. and Bereiter, C. 1994. Computer Support for Knowledge-Building Communities. Journal of the Learning Sciences. 3, 3 (1994), 265–283. DOI:https://doi.org/10.1207/s15327809jls0303_3.Google ScholarCross Ref
- [20] Stahl, G. 2000. Collaborative information environments to support knowledge construction by communities. AI & SOCIETY. 14, 1 (2000), 71–97. DOI:https://doi.org/10.1007/bf01206129.Google ScholarDigital Library
- [21] Tawil, S. and Rita, L. 2015. Rethinking Education: Towards a Global Comon Good. UNESCO.Google Scholar
- [22] Weinberger, A. et al. 2007. Knowledge convergence in collaborative learning: Concepts and assessment. Learning and Instruction. 17, 4 (2007), 416–426. DOI:https://doi.org/10.1016/j.learninstruc.2007.03.007.Google ScholarCross Ref
- [23] Weinberger, A. and Fischer, F. 2006. A framework to analyze argumentative knowledge construction in computer-supported collaborative learning. Methodological Issues in Researching CSCL. 46, 1 (Jan. 2006), 71–95. DOI:https://doi.org/10.1016/j.compedu.2005.04.003.Google ScholarCross Ref
- [24] Wu, B. and Wu, C. 2021. Research on the mechanism of knowledge diffusion in the MOOC learning forum using ERGMs. Computers & Education. 173, (2021), 104295. DOI:https://doi.org/10.1016/j.compedu.2021.104295.Google ScholarDigital Library
- [25] Yoon, S.A. et al. 2020. Encouraging collaboration and building Community in Online Asynchronous Professional Development: designing for social capital. International Journal of Computer-Supported Collaborative Learning. 15, 3 (2020), 351–371. DOI:https://doi.org/10.1007/s11412-020-09326-2.Google ScholarCross Ref
- [26] Zhang, J. et al. 2009. Designs for Collective Cognitive Responsibility in Knowledge-Building Communities. Journal of the Learning Sciences. 18, 1 (2009), 7–44. DOI:https://doi.org/10.1080/10508400802581676.Google ScholarCross Ref
- [27] Zhang, J. et al. 2016. Understanding the dynamics of MOOC discussion forums with simulation investigation for empirical network analysis (SIENA). Distance Education. 37, 3 (2016), 270–286. DOI:https://doi.org/10.1080/01587919.2016.1226230.Google ScholarCross Ref
- [28] Zheng, L. et al. 2022. Effects of a learning analytics‐based real‐time feedback approach on knowledge elaboration, knowledge convergence, interactive relationships and group performance in CSCL. British Journal of Educational Technology. 53, 1 (2022), 130–149. DOI:https://doi.org/10.1111/bjet.13156.Google ScholarCross Ref
- [29] Zottmann, J.M. et al. 2013. Computer-supported collaborative learning with digital video cases in teacher education: The impact of teaching experience on knowledge convergence. Computers in Human Behavior. 29, 5 (2013), 2100–2108. DOI:https://doi.org/10.1016/j.chb.2013.04.014.Google ScholarCross Ref
Index Terms
- Understanding Knowledge Convergence in a Cross-cultural Online Context: An Individual and Collective Approach
Recommendations
Quantitative Approach in Measuring Knowledge Convergence in Serious Games
Games and Learning AllianceAbstractCollaborative Serious Games (SG) aims to promote knowledge convergence, the process by which two or more people may reach mutual understanding after having interacted together. However, the analysis of knowledge convergence has been mostly ...
The Impact of Cross-Age Peer Tutors on Knowledge Elaboration, Knowledge Convergence, and Group Performance in Computer Supported Collaborative Learning
ICALT '15: Proceedings of the 2015 IEEE 15th International Conference on Advanced Learning TechnologiesIn this study, the impact of cross-age peer tutors on knowledge elaboration, knowledge convergence, and learning performance in CSCL are investigated. 128 college students who were randomly divided into 32 groups participated in this study. The ...
Cross-cultural knowledge communication in online communities of practice
Concepts associated with knowledge communication add value and complexity to the traditional Western educational models, which have focused mainly on the individual as the unit of analysis. This paper extends past the individual level and builds on the ...
Comments