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Towards a micro-ecological approach to CSCL

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Abstract

In this paper, we argue that how we use theories may be preventing us from developing a deeper understanding of computer supported collaborative learning (CSCL) contexts. We focus the argument on our understanding of orchestration processes and draw on common theories to show how they prioritize a mono-ecological approach: the examination of collaborative processes at a single level of an ecological system. We argue that doing so prevents us from seeing the full complexity of the types of decisions that teachers and learners make when implementing collaborative learning activities in technologically enhanced, real-world contexts. To address this problem, we propose a micro-ecological framework that recognizes collaborative learning as a complex, cognitively nested, ecological phenomenon and analyzes interactions in a way that aligns with this view. Our approach focuses on the microanalysis of interactions between individuals, learning objects, the small group, and the classroom community. The purpose of this analysis is to identify critical points in the learning process where actions at one level of cognitive activity propagate to influence other levels of individual and joint activity. We call these events transecological disruptions. We argue that these disruptions can provide opportunities to understand how the learning ecology develops over time through teacher orchestration and learner engagement. To illustrate our framework, we pursue the following research question: “How can a micro-ecological framework help us better understand the CSCL ecology?”

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Authors and Affiliations

  1. College of Education, The Pennsylvania State University, 301C Keller Building, University Park, PA, 16802-6823, USA

    Marcela Borge

  2. College of Education, 1310 S. 6th Street, Champaign, IL, 61820, USA

    Emma Mercier

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  1. Marcela Borge
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  2. Emma Mercier
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Correspondence to Marcela Borge.

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Borge, M., Mercier, E. Towards a micro-ecological approach to CSCL. Intern. J. Comput.-Support. Collab. Learn 14, 219–235 (2019). https://doi.org/10.1007/s11412-019-09301-6

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