Authors
- Patarakin Evgeniy Dmitrievich Doctor of Education Sciences, Associate Professor
- Burov Vasiliy V.
Annotation
To analyze the interactions of teachers within the project “Moscow Electronic School” (MES) system, we use the theoretical framework defined by the concept of “Invisible college”. The aim of the study is to map the structure of connections that are formed between teachers of the MES based on the analysis of digital traces. The objectives of the study were to: highlight the networks of the most influential work in the field of team science and collaboration analytics; to identify groups of teachers by means of network analysis, united by mutual copying of the scenarios of the lessons of the Moscow e-school; explain the network phenomena observed inside the Moscow school, supported by the results of modeling the behavior of teachers in an artificial community. The leading research methods were the methods of organizational network analysis and agent-based modeling. Mapping of groups of participants made it possible to establish that more than 75 % of participants, united by links of mutual copying of lesson scenarios, are part of a giant component. Experiments with variants of Team Assembly models made it possible to substantiate the assumption that new participants with a probability of more than 60 % tend to choose experienced participants for collaborative activities.
How to link insert
Patarakin, E. D. & Burov, V. V. (2022). “INVISIBLE COLLEGE” OF MES Bulletin of the Moscow City Pedagogical University. Series "Pedagogy and Psychology", 2022, №2 (60), 38. https://doi.org/10.25688/2072-9014.2022.60.2.04
References
1.
1. De Solla Price, D. J., & Beaver, D. (1966). Collaboration in an invisible college. American Psychologist, 11 (21), 1011–1018. https://doi.org/10.1037/h0024051
2.
2. Morehouse, J., & Saffer, A. J. (2019). Illuminating the invisible college: An analysis of foundational and prominent publications of engagement research in public relations. Public Relations Review, 5 (45), 101836. https://doi.org/10.1016/j.pubrev.2019.101836
3.
3. Goyanes, M., & Marcos, L. de. (2020). Academic influence and invisible colleges through editorial board interlocking in communication sciences: a social network analysis of leading journals. Scientometrics, 2 (123), 791–811. https://doi.org/10.1007/s11192-020-03401-z
4.
4. Sedita, S. R., Caloffi, A., & Lazzeretti, L. (2020). The invisible college of cluster research: a bibliometric core–periphery analysis of the literature. Industry and Innovation, 5 (27), 562–584. https://doi.org/10.1080/13662716.2018.1538872
5.
5. Vélez-Cuartas, G. (2018). Invisible Colleges 2.0. Eponymy as a Scientometric Tool, 3 (7), 5.
6.
6. García-Peña, C., Gutiérrez-Robledo, L. M., Cabrera-Becerril, A., & Fajardo-Ortiz, D. (2019). Team Assembly Mechanisms and the Knowledge Produced in the Mexico’s National Institute of Geriatrics: A Network Analysis and Agent-Based Modeling Approach. Scientifica, e9127657. https://doi.org/10.1155/2019/9127657
7.
7. Gómez-Zará, D., DeChurch, L. A., & Contractor, N. S. (2020). A Taxonomy of Team-Assembly Systems: Understanding How People Use Technologies to Form Teams. Proceedings of the ACM on Human-Computer Interaction, 4 (CSCW2), Article 181, 36 p. https://doi.org/10.1145/3415252
8.
8. Nardi, B. A., & Engeström, Y. (1999). A Web on the Wind: The Structure of Invisible Work. Comput. Supported Coop. Work, 1-2 (8), 1–8. https://doi.org/10.1023/A:1008694621289
9.
9. Börner, K., & Record, E. (2017). Macroscopes for Making Sense of Science. PEARC17. New York, NY, USA: ACM. Article 64, 2 p. https://doi.org/10.1145/3093338.3106387
10.
10. Galley, R., Conole, G., & Alevizou, P. (2014). Community indicators: a framework for observing and supporting community activity on Cloudworks. Interactive Learning Environments, 3 (22), 373–395.
11.
11. Eck, N. J. van, & Waltman, L. (2009). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 2 (84), 523–538. https://doi.org/10.1007/s11192-009-0146-3
12.
12. Polley, D. E. (2015). Visualizing the topical coverage of an institutional repository using VOSviewer. In Data Visualization: A Guide to Visual Storytelling for Librarians. Rowman & Littlefield.
13.
13. Patarakin, E. D. (2017). Wikigrams-Based Social Inquiry. In Digital Tools and Solutions for Inquiry-Based STEM Learning (pp. 112–138). IGI Global.
14.
14. Vachkova, S., Petryaeva, E., & Patarakin, E. (2021). Typology of schools operating in the Moscow Electronic School system based on the analysis of network indicators. In SHS Web of Conferences, (98), 03001. https://doi.org/10.1051/shsconf/20219803001
15.
15. Patarakin, E., Vachkova, S., & Burov, V. (2021). Agent-based modeling of teacher interaction within a repository of digital objects. In SHS Web of Conferences, (98), 05013. https://doi.org/10.1051/shsconf/20219805013
16.
16. Patarakin, Y. D., & Yarmakhov, B. B. (2021). Data farming for virtual school laboratories. RUDN Journal of Informatization in Education, 4 (18), 347–359. https://doi.org/10.22363/2312-8631-2021-18-4-347-359
17.
17. De Caux, R. (2017). An agent-based approach to modelling long-term systemic risk in networks of interacting banks. Thesis for the degree of Doctor of Philosophy. University of Southampton.
18.
18. Sayama, H., Cramer, C., Sheetz, L., & Uzzo, S. (2017). NetSciEd: Network Science and Education for the Interconnected World. ArXiv:1706.00115 [physics].
19.
19. Secchi, D., & Neumann, M. (Es) (2016). Agent-Based Simulation of Organizational Behavior. Cham: Springer International Publishing. 348 p.
20.
20. Rakić, K., Rosić, M., & Boljat, I. (2020). A Survey of Agent-Based Modelling and Simulation Tools for Educational Purpose. Tehnički vjesnik, 3 (27), 1014–1020. https://doi.org/10.17559/TV-20190517110455
21.
21. Guimera, R., Uzzi, B., Spiro, J., & Amaral, L. A. N. (2005). Team assembly mechanisms determine collaboration network structure and team performance. Science, 5722 (308), 697–702.
22.
22. Zu, C., Zeng, H., & Zhou, X. (2019). Computational Simulation of Team Creativity: The Benefit of Member Flow. Frontiers in Psychology, (10). https://doi.org/10.3389/fpsyg.2019.00188
23.
23. Railsback, S. F., & vGrimm, V. (2019). Agent-Based and Individual-Based Modeling. A Practical Introduction (Second Edition). Princeton University Press. 359 p.
24.
24. Gmür, M. (2003). Co-citation analysis and the search for invisible colleges: A methodological evaluation. Scientometrics, 1 (57). https://doi.org/10.1023/A:1023619503005
25.
25. Nardi, B. A., & Engeström, Y. (1999). A Web on the Wind: The Structure of Invisible Work. Comput. Supported Coop. Work, 1-2 (8), 1–8. https://doi.org/10.1023/A:1008694621289
26.
26. Palacios-Núñez, G., Vélez-Cuartas, G., & Botero, J. D. (2018). Developmental tendencies in the academic field of intellectual property through the identification of invisible colleges. Scientometrics, 3 (115), 1561–1574. https://doi.org/10.1007/s11192-018-2648-3
27.
27. Yang, D. (2019). Computational Social Roles. Carnegie Mellon University.
28.
28. Henderson, K., Gallagher, B., Eliassi-Rad, T., Tong, H., Basu, S., Akoglu, L.,
Koutra, D., Faloutsos, C., & Li, L. (2012). RolX: structural role extraction and mining in large graphs KDD ’12 / Beijing, China: Association for Computing Machinery, 1231–1239. https://doi.org/10.1145/2339530.2339723