Résumés
Abstract
The network is a key concept which has been highly valued in connectivism. Research about the static characteristics of social networks in connectivist learning has been carried out in recent years, however, little knowledge exists regarding the principles of network evolution from a dynamic perspective. This article chose the first connectivist massive open and online course (cMOOC) in China, “Internet plus Education: Dialogue between Theory and Practice” as the research object, using the dynamic analysis method of social networks which is based on stochastic actor-oriented models, to reveal the influence of the individual attributes and network structural attributes on the dynamic evolution of social networks in a cMOOC. We found that: 1) the learners with the same sex, the same social identity, and the same type of behaviour tendency found it much easier to interact with each other; 2) there is a heterogeneous phenomenon with course identity, meaning that compared to communicating with other learners, learners are more inclined to reply to a facilitator; and 3) the reciprocity and transitivity have significant effects on social network evolution. This study is valuable for understanding the network evolution and has implications for the improvement of cMOOC design, in turn improving the online learning experience for cMOOC learners.
Keywords:
- cMOOC,
- social network,
- SIENA,
- evolution,
- interaction,
- connectivism
Résumé
Le réseau est un concept essentiel qui a été fortement valorisé dans le connectivisme. Quelques recherches ont été menées ces dernières années sur les caractéristiques statiques des réseaux sociaux dans l'apprentissage connectiviste. Cependant, il existe peu de connaissances sur les principes d'évolution des réseaux d'un point de vue dynamique. Cet article a choisi le premier cours connectiviste de formation en ligne ouverte à tous (cMOOC) en Chine, "Internet plus Éducation : Dialogue entre la théorie et la pratique" comme objet de recherche, en utilisant la méthode d'analyse dynamique des réseaux sociaux qui est basée sur des modèles stochastiques orientés vers les acteurs, pour révéler l'influence des attributs individuels et celles des attributs structurels du réseau sur l'évolution dynamique des réseaux sociaux dans un cMOOC. Nous avons constaté que : (1) les apprenants ayant le même sexe, la même identité sociale et le même type de tendance comportementale trouvent qu'il est beaucoup plus facile d'interagir les uns avec les autres ; (2) il existe un phénomène hétérogène avec l'identité du cours, ce qui signifie que par rapport à la communication avec d'autres apprenants, les apprenants sont plus susceptibles de répondre à un facilitateur ; (3) la réciprocité et la transitivité ont des effets significatifs sur l'évolution des réseaux sociaux. Cette étude est utile pour comprendre l'évolution du réseau et a des implications pour l'amélioration de la conception du cMOOC, améliorant à son tour l'expérience d'apprentissage en ligne pour les apprenants du cMOOC.
Mots-clés :
- cMOOC,
- réseau social,
- SIENA,
- connectivisme,
- interaction,
- évolution
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Bibliography
- Albert, R., & Barabási, A.-L. (2001). Statistical mechanics of complex networks. Reviews of Modern Physics, 74(1), 47-97. https://doi.org/10.1103/RevModPhys.74.47
- Anderson, T. (2009). The dance of technology and pedagogy in self-paced distance education. http://hdl.handle.net/2149/2210
- Arvaja, M., Rasku-Puttonen, H., Häkkinen, P., & Eteläpelto, A. (2003). Constructing Knowledge through a Role-Play in a Web-Based Learning Environment. Journal of Educational Computing Research, 28(4), 319–341. https://www.learntechlib.org/p/69173
- Buder, J., Schwind, C., Rudat, A., & Bodemer, D. (2015). Selective reading of large online forum discussions: The impact of rating visualizations on navigation and learning. Computers in Human Behavior, 44, 191–201. http://doi.org/10.1016/j.chb.2014.11.043
- Chen, L., Lu, H., & Zheng, Q.H. (2019). Conceptualizing knowledge in “Internet + education”: the nature of knowledge and knowledge evolution. Distance Education in China, (07), 10-18+92. https://doi.org/10.13541/j.cnki.chinade.2019.07.003.
- Downes, S. (2012). Connectivism and Connective Knowledge: Essays on meaning and learning networks. National Research Council Canada. https://www.oerknowledgecloud.org/record705
- Downes, S. (2017). Towards Personal Learning: Reclaiming a role for humanity in a world of commercialism and automation. https://www.downes.ca/files/books/Toward%20Personal%20Learning%20v09.pdf
- Dron, J. (2013). Soft is hard and hard is easy: learning technologies and social media. Open Journal, 13(1), 32-43. http://doi.org/10.13128/formare-12613
- Duan, J. J., Xie, K., Hawk, N., Yu, S., & Wang, M. (2019). Exploring a personal social knowledge network (pskn) to aid the observation of connectivist interaction for high and lower forming learners in connectivist massive open online courses. British journal of educational technology, 50(1), 199-217. https://doi.org/10.1111/bjet.12687
- Durant, K. T., Mccray, A. T., & Safran, C. (2012). Identifying gender-preferred communication styles within online cancer communities: A retrospective, longitudinal analysis. PLOS ONE, 7(11), e49169. http://doi.org/10.1371/journal.pone.0049169
- Guo, Y. J., Chen, L., Xu, L., & Gao, X. F. (2020). Social network characteristics of learners in connectivist learning. Distance Education in China, 2, 32-39+67+76-77. https://doi.org/10.13541/j.cnki.chinade.2020.02.004
- Kellogg, S., Booth, S., & Oliver, K. (2014). A social network perspective on peer supported learning in MOOCs for educators. The International Review of Research in Open and Distributed Learning, 15(5), 263–289. https://www.webofscience.com/wos/alldb/full-record/WOS:000347627100011
- Kossinets, G., & Watts, D. J. (2006). Empirical analysis of an evolving social network. Science, 311(5757), 88-90. https://doi.org/10.1126/science.1116869
- Lazarsfeld, P. F., & Merton, R. K. (1954). Friendship as a social process: A substantive and methodological analysis. In M. Berger (Ed.), Freedom and control in modern society (pp. 18–66). New York, NY: Van Nostrand.
- Liang, Y. Z. (2018). Evaluation of individual importance in online interactive networks and its impact on learning outcomes. China Educational Technology, 11, 94-102. https://doi.org/10.3969/j.issn.1006-9860.2018.11.014
- Liu, Q. T., Zhang, N., & Zhu, J. J. (2018). Social network analysis of collaborative knowledge construction in teacher workshops. Distance Education in China, 11, 61-69+80. https://doi.org/10.13541/j.cnki.chinade.20181108.004
- McPherson, M., Smith-Lovin, L., & Cook, J. M. (2001). Birds of a feather: Homophily in social networks. Annual Review of Sociology, 27, 415-444. https://doi.org/10.1146/annurev.soc.27.1.415
- Ripley, R. M., Snijders, T. A. B., Boda, Z., Voros, A., & Preciado, P. (2015). Manual for SIENA (version May 22, 2015). Oxford: University of Oxford. https://www.stats.ox.ac.uk/~snijders/siena/RSiena_Manual.pdf
- Rogers, P. J. (1995). Diffusion of innovations (4th ed.). New York, NY. Free Press.
- Siemens, G. (2005). Connectivism: A learning theory for the digital age. International Journal of Instructional Technology and Distance Learning, 2(1), 3-10. http://www.hetl.org/wp-content/uploads/gravity_forms/2-298b245759ca2b0fab82a867d719cbae/2013/01/Connectivism-hand-out.pdf
- Snijders, T. A., van de Bunt, G. G., & Steglich, C. E. (2010). Introduction to stochastic actor-based models for network dynamics. Social Networks, 32(1), 44–60. https://doi.org/10.1016/j.socnet.2009.02.004
- Song, X. F., Zhao, W., Gao, L., & He, C. L. (2014). Content analysis and social network of knowledge sharing in question and answer sites: Taking Zhihu community “online education” topic as an example. Modern Educational Technology, 24(6), 70-77. https://wenku.baidu.com/view/139b54bdbc1e650e52ea551810a6f524cdbfcbc7.html
- Waite, M., Mackness, J., Roberts, G., & Lovegrove, E. (2013). Liminal participants and skilled orienteers: Learner participation in a MOOC for new lecturers. Journal of Online Learning and Teaching, 9, 200–215. http://jolt.merlot.org/
- Wang, Z., Anderson, T., & Chen, L. (2018). How learners participate in connectivist learning: an analysis of the interaction traces from a cMOOC. International Review of Research in Open & Distance Learning, 19(1), 44-67. https://www.webofscience.com/wos/alldb/full-record/WOS:000428755100004
- Wang, Z., Walther, J. B., Pingree, S., & Hawkins, R. P. (2008). Health Information, Credibility, Homophily, and Influence via the Internet: Web Sites Versus Discussion Groups. Health Communication, 23(4), 358-368. http://doi.org/10.1080/10410230802229738
- Wang, Z. H., Xie, Y. Y., & Li, F. (2012). A study on “educational technology” microblog circle based on social network analysis. Modern Educational Technology, 22(5), 83-87. https://wenku.baidu.com/view/8f86ab2d4b649b6648d7c1c708a1284ac9500551.html
- Wang, Z. J., & Chen, L. (2017). Conceptual interaction and learning assessment in distance learning. Distance Education in China, 12, 12-20+79. https://doi.org/10.13541/j.cnki.chinade.20171222.008
- Wu, J., Chen, J., & Jin, M. (2016). Analysis of evolution of interactive patterns in blended collaborative learning. Journal of Distance Education, 34(1), 61-68. https://doi.org/10.15881/j.cnki.cn33-1304/g4.2016.01.007
- Wu, J., Li, S. S., Zhou, L. S., Shi, L., & Chen, J. (2017). Research on dynamic evolution of user’s relationships network in online health community based on stochastic actor-oriented model. Journal of the China Society for Scientific and Technical Information, 36(2), 213-220. https://doi.org/10.3772/j.issn.1000-0135.2017.02.012
- Xu, Y. (2020). Research on the relationship between individual Status in social network and corresponding concept network characteristics in cMOOC. (Master’s dissertation, Beijing: Beijing Normal University). http://etd.lib.bnu.edu.cn/Detail?dbID=5&dbCode=ETD1&sysID=76127
- Xu, Y., & Chen, L. (2019). Research on the relationship between individual social network status and corresponding concept network characteristics in connective learning: a partial analysis of interaction in a cMOOC. Distance Education in China, 10, 9-19+51+92. https://doi.org/10.13541/j.cnki.chinade.2019.10.003
- Xu, Y., & Du, J. (2021). What participation types of learners are there in connectivist learning: an analysis of a cMOOC from the dual perspectives of social network and concept network characteristics. Interactive Learning Environments, 1-18. https://doi.org/10.1080/10494820.2021.2007137
- Yang, D., Sinha, T., Adamson, D., & Rosé, C. P. (2013). Turn on, tune in, drop out: Anticipating student dropouts in massive open online courses. Paper presented at the NIPS Data-Driven Education Workshop, Lake Tahoe, NV. https://www.researchgate.net/publication/266203181_Turn_on_Tune_in_Drop_out_Anticipating_Student_Dropouts_in_Massive_Open_Online_Courses
- Zhang, J. J., Skryabin, M., & Song, X. W. (2016). Understanding the dynamics of MOOC discussion forums with simulation investigation for empirical network analysis SIENA. Distance education, 37(3), 270-286. https://doi.org/10.1080/01587919.2016.1226230