Abstract
The use of data from computer-based learning environments has been a long-standing feature of CSCL. Learning Analytics (LA) can enrich this established work in CSCL. This chapter outlines synergies and tensions between the two fields. Drawing on examples, we discuss established work to use learning analytics as a research tool (analytics of collaborative learning—ACL). Beyond this potential though, we discuss the use of analytics as a mediational tool in CSCL—collaborative learning analytics (CLA). This shift raises important challenges regarding the role of the computer—and analytics—in supporting and developing human agency and learning. LA offers a new tool for CSCL research. CSCL offers important contemporary perspectives on learning for a knowledge society, and as such is an important site of action for LA research that both builds our understanding of collaborative learning and supports that learning.
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Notes
- 1.
Rosé (2018) discusses many of the same tensions existing between learning analytics and the learning sciences more broadly: for example, the need to consider the relative value of model accuracy versus interpretability, and top-down (theory-driven) versus bottom-up (data-driven) approaches. A key differentiator for CSCL in addressing these tensions is a long-standing history of considering the role of computers and computation in learning, which has been a central part of the fiber of the CSCL community from the beginning.
- 2.
For an overview of learning analytics, readers may refer to the Journal of Learning Analytics (learning-analytics.info—including a special section in Spring 2021 on collaborative learning analytics), the International Conference on Learning Analytics & Knowledge (LAK, www.solaresearch.org/events/lak/), and the Handbook of Learning Analytics (1st edition available at http://solaresearch.org/publications/hla-17 second edition forthcoming). There are also examples of learning analytics work in CSCL, including via the following excellent NAPLES resources:
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Further Readings
Erkens, G. (n.d.). Gijsbert Erkens: Automated argumentation analyses. Retrieved September 16, 2019, from ISLS NAPLES Network website: http://isls-naples.psy.lmu.de/intro/all-webinars/erkens/index.html. An example of how analyses of argumentation can be automated for insight.
Janssen, J. (2013). Jeroen Janssen: Group awareness tools. Retrieved September 16, 2019, from ISLS NAPLES Network website: http://isls-naples.psy.lmu.de/intro/all-webinars/janssen_video/index.html. An overview of a classic CSCL area with parallels in emerging learning analytics dashboard work.
Jermann, P. (n.d.). Patrick Jermann: Physiological measures in learning sciences research. Retrieved September 16, 2019, from ISLS NAPLES Network website: http://isls-naples.psy.lmu.de/intro/all-webinars/jermann/index.html. A specific example of how physiological measures can give insight into constructs of interest to the CSCL community.
Rosé, C. P. (2014). Carolyn Rosé: Learning analytics and educational data mining in learning discourses. Retrieved September 16, 2019, from ISLS NAPLES Network website: http://isls-naples.psy.lmu.de/intro/all-webinars/rose_all/index.html. Potential for CLA in applying learning analytics and educational data mining to learning discourses.
Williamson Shaffer, D. (n.d.). David Williamson Shaffer: Tools of Quantitative Ethnography: Epistemic Network Analysis and nCoder. Retrieved September 16, 2019, from ISLS NAPLES Network website: http://isls-naples.psy.lmu.de/intro/all-webinars/shaffer_video/index.html. An example learning analytics approach grounded in the learning sciences, which demonstrates moving through analytic lenses.
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Wise, A.F., Knight, S., Shum, S.B. (2021). Collaborative Learning Analytics. In: Cress, U., Rosé, C., Wise, A.F., Oshima, J. (eds) International Handbook of Computer-Supported Collaborative Learning. Computer-Supported Collaborative Learning Series, vol 19. Springer, Cham. https://doi.org/10.1007/978-3-030-65291-3_23
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