Analyze dynamique de l'évolution du réseau social dans le cMOOC basé sur le mode RSiena

Auteurs-es

  • Yaqian Xu Beijing Normal University
  • Junlei Du Beijing Normal University

DOI :

https://doi.org/10.21432/cjlt28256

Mots-clés :

cMOOC, réseau social , SIENA, connectivisme, interaction, évolution

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.

Bibliographies de l'auteur-e

Yaqian Xu, Beijing Normal University

XU Yaqian, is a PhD student majoring in distance education in Beijing Normal University, China. Her research interests include connectivism, online learning analysis, and instructional interaction and she has published 10 papers in journals

Junlei Du, Beijing Normal University

DU Junlei, is a graduate student majoring in software engineering in Beijing Jiaotong University. His research interests include knowledge graph, data analysis and machine learning and he has published 7 papers in journals.

Références

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

Téléchargements

Publié-e

2022-11-29

Numéro

Vol. 48 No. 4 (2022): Special Issue

Rubrique

Articles

Licence

© Yaqian Xu, Junlei Du 2022

Licence Creative Commons

Cette œuvre est sous licence Creative Commons Attribution - Pas d'Utilisation Commerciale 4.0 International.

Droits d’auteur

Les auteurs conservent le droit d'auteur et accordent le droit de la première publication de la revue avec le travail simultanément sous une licence Creative Commons Attribution - Pas d’Utilisation Commerciale 4.0 International (CC-BY-NC 4.0) qui permet aux autres de partager le travail avec une reconnaissance de la paternité de l'œuvre et la publication initiale dans ce journal.