Advertisement

Learners’ agency and CSCL technologies: towards an emancipatory perspective

  • Pierre TchounikineEmail author
Article

Abstract

This squib continues the ongoing conversation about the direction and future of CSCL, initiated by Wise and Schwarz (International Journal of Computer-Supported Collaborative Learning, 12, 423–467, 2017) and the ijCSCL editors. It argues that CSCL should take an emancipatory perspective to learners’ agency and its technological substratum. The implication is that learners should be empowered to select, change, inter-operate and/or adapt not only the software applications they use, but more generally, the support they obtain from these technologies. This raises many exciting questions and challenges for CSCL in terms of educational, social, design and technical considerations.

Keywords

Learners’ agency Emancipation Adaptability Design of computational artifacts 

Notes

References

  1. Betbeder, M.-L., & Tchounikine, P. (2003). Symba: A framework to support collective activities in an educational context. In International Conference on Computers in Education (ICCE 2003), Hong-Kong (China) (pp. 188–196).Google Scholar
  2. Dillenbourg, P. (2013). Design for classroom orchestration. Computers & Education, 69, 485–492.CrossRefGoogle Scholar
  3. Dillenbourg, P., & Hong, F. (2008). The mechanics of CSCL macro scripts. International Journal of Computer-Supported Collaborative Learning, 3(1), 5–23.CrossRefGoogle Scholar
  4. Dillenbourg, P., & Tchounikine, P. (2007). Flexibility in macro-scripts for CSCL. Journal of Computer Assisted Learning, 23(1), 1–13.CrossRefGoogle Scholar
  5. Fischer, F., Kollar, I., Mandl, H., & Haake, J. M. (2007). Scripting computer-supported collaborative learning: Cognitive, computational and educational perspectives (Vol. 6). Springer Science & Business Media.Google Scholar
  6. Jermann, P., Soller, A., & Muehlenbrock, M. (2001). From mirroring to guiding: A review of the state of art technology for supporting collaborative learning. In European Conference on Computer-Supported Collaborative Learning EuroCSCL-2001 (pp. 324–331).Google Scholar
  7. Ludvigsen, S., Cress, U., Law, N., et al. (2017). Future direction for the CSCL field: Methodologies and eight controversies. International Journal of Computer-Supported Collaborative Learning, 12(4), 337–341.CrossRefGoogle Scholar
  8. Magnisalis, I., Demetriadis, S., & Karakostas, A. (2011). Adaptive and intelligent systems for collaborative learning support: A review of the field. IEEE Transactions on Learning Technologies, 4(1), 5–20.CrossRefGoogle Scholar
  9. Moguel, P., Tchounikine, P., & Tricot, A. (2011). Interfaces leading groups of learners to make their shared problem-solving organization explicit. IEEE Transactions on Learning Technologies, 5(3), 199–212.CrossRefGoogle Scholar
  10. Mørch, A. I. (1997). Three levels of end-user tailoring: Customization, integration, and extension. In M. Kyng & L. Mathiassen (Eds.), Computers and design in context (pp. 51–76). Cambridge: MIT Press.Google Scholar
  11. Näykki, P., Isohätälä, J., Järvelä, S., et al. (2017). Facilitating socio-cognitive and socio-emotional monitoring in collaborative learning with a regulation macro script – an exploratory study. International Journal of Computer-Supported Collaborative Learning, 12(3), 251–279.CrossRefGoogle Scholar
  12. Prieto, L. P., Asensio-Perez, J. I., Munoz-Cristóbal, J. A., Dimitriadis, Y. A., Jorrín-Abellán, I. M., & Gomez-Sanchez, E. (2013). Enabling teachers to deploy CSCL designs across distributed learning environments. IEEE Transactions on Learning Technologies, 6(4), 324–336.CrossRefGoogle Scholar
  13. Rummel, N. (2018). One framework to rule them all? Carrying forward the conversation started by wise and Schwarz. International Journal of Computer-Supported Collaborative Learning, 13(1), 123–129.CrossRefGoogle Scholar
  14. Rummel, N., Walker, E., & Aleven, V. (2016). Different futures of adaptive collaborative learning support. International Journal of Artificial Intelligence in Education, 26(2), 784–795.CrossRefGoogle Scholar
  15. Soller, A., Martínez, A., Jermann, P., & Muehlenbrock, M. (2005). From mirroring to guiding: A review of state of the art technology for supporting collaborative learning. International Journal of Artificial Intelligence in Education, 15(4), 261–290.Google Scholar
  16. Stahl, G. (2016). The group as paradigmatic unit of analysis: The contested relationship of CSCL to the learning sciences. In M. A. Evans, M. J. Packer, & R. K. Sawyer (Eds.), Reflections on the learning sciences (ch. 5). New York: Cambridge University Press.Google Scholar
  17. Stahl, G., Koschmann, T., & Suthers, D. (2006). Computer-supported collaborative learning: An historical perspective. In R. K. Sawyer (Ed.), Cambridge handbook of the learning sciences (pp. 409–426). Cambridge: Cambridge University Press.Google Scholar
  18. Tchounikine, P. (2008). Operationalizing macro-scripts in CSCL technological settings. International Journal of Computer-Supported Collaborative Learning, Springer, 3(2), 193–133.CrossRefGoogle Scholar
  19. Tchounikine, P. (2011). Computer science and educational software design – A resource for multidisciplinary work in technology enhanced learning. Springer.  https://doi.org/10.1007/978-3-642-20003-8_6.
  20. Tchounikine, P. (2013). Clarifying design for orchestration: Orchestration and orchestrable technology, scripting and conducting. Computers & Education, 69, 500–503.CrossRefGoogle Scholar
  21. Tchounikine, P. (2016). Contribution to a theory of CSCL scripts: Taking into account the appropriation of scripts by learners. International Journal of Computer-Supported Collaborative Learning, 11(3), 349–369.CrossRefGoogle Scholar
  22. Tchounikine, P. (2017). Designing for appropriation: A theoretical account. Human–Computer Interaction, 32(4), 155–195.CrossRefGoogle Scholar
  23. Tchounikine, P. (2019). Framing design for appropriation with zones of proximal evolution: Email for PIM. International Journal of Human-Computer Studies, 123, 18–28.CrossRefGoogle Scholar
  24. Tchounikine, P., Rummel, N., & McLaren, B. M. (2010). Computer supported collaborative learning and intelligent tutoring systems. In R. Nkambou, J. Bourdeau, & R. Mizoguchi (Eds.), Advances in intelligent tutoring systems. Studies in computational intelligence (Vol. 308). Berlin, Heidelberg: Springer.Google Scholar
  25. Wang, X., Kollar, I., & Stegmann, K. (2017). Adaptable scripting to foster regulation processes and skills in computer-supported collaborative learning. International Journal of Computer-Supported Collaborative Learning, 12(2), 153–172.CrossRefGoogle Scholar
  26. Wecker, C., Stegmann, K., Bernstein, F., Huber, M. J., Kalus, G., Rathmeyer, S., Kollar, I., & Fischer, F. (2010). S-COL: A Copernican turn for the development of flexibly reusable collaboration scripts. International Journal of Computer-Supported Collaborative Learning, 5(3), 321–343.CrossRefGoogle Scholar
  27. Weinberger, A., Stegmann, K., & Fischer, F. (2010). Learning to argue online: Scripted groups surpass individuals (unscripted groups do not). Computers in Human Behavior, 26(4), 506–515.CrossRefGoogle Scholar
  28. Wild, F., Kalz, M. & Palmér, M. (2008). Mash-Up Personal Learning Environments. Proceedings of 1st Workshop MUPPLE’08, Maastricht, The Netherlands, September 17, 2008, CEUR Workshop Proceedings, ISSN 1613-0073, online CEUR-WS.org/Vol-388/.
  29. Wise, A., & Schwarz, B. (2017). Visions of CSCL: Eight provocations for the future of the field. International Journal of Computer-Supported Collaborative Learning, 12, 423–467.CrossRefGoogle Scholar
  30. Wright, E. O. (2010). Envisioning real utopias. London: Verso.Google Scholar

Copyright information

© International Society of the Learning Sciences, Inc. 2019

Authors and Affiliations

  1. 1.University Grenoble Alpes, CNRS, Grenoble INP, LIGGrenobleFrance

Personalised recommendations