The field of CSCL is at a critical moment in its development. Internally we face issues of fragmentation and questions about what progress is being made. Externally the rise of social media and a variety of research communities that study the interactions within it raise questions about our unique identity and larger impact on the world. To illuminate the complex issues involved and the multiple perspectives that exist on them, we conducted an iterative and generative consultation with members of the CSCL community through individual interviews and public interactive presentations. The result is a series of eight provocations for the field, each presented as a dialogue between the Provocateur/Provocatrice (who seeks to shake up the status quo) and the Conciliator (who seeks to build on the achievements of our current traditions). The provocations address the debated need for six things: one conceptual framework to unite our diverse tools and theories (#1), prioritization of learner agency over collaborative scripting (#2), scrupulous scrutiny of when “collaboration” and “community” are said to exist (#3), the pursuit of computational approaches to understand collaborative learning (#5), learning analytics and adaptive support to be a top priority in the field (#6), and the expansion of our focus to seriously address social media and large-scale learning environments (#7). In addition, the provocations highlight two areas in which perhaps we should desist: the attempt to reconcile analytical and interpretative approaches to understanding collaboration (#4), and the goal of achieving tangible change in the education system (#8). There are no resolutions offered in this paper; the interchanges presented are designed to lay out the complex constellation of issues involved and can be considered a dialogue that we are still in the process of having with ourselves as individuals and together as a community. We stress the urgency and importance for the field of CSCL to take up these questions and tensions, and critically, to work towards decisions and resultant actions. Our future as a scientific community — our very existence and identity, depends on it.
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We are extremely grateful for the strong support and contributions of the CSCL community to this project. The following individuals all gave kindly and willingly of their time and thinking (though we alone take responsibility for the final product and ideas ultimately represented here): Michael Baker, Marcela Borge, Allan Collins, Ulrike Cress, Pierre Dillenbourg, Yannis Dimitriadis, Frank Fischer, Cindy Hmelo-Silver, Yotam Hod, Ulrich Hoppe, Sanna Järvelä, Heisawn Jeong, Yael Kali, Timothy Koschmann, Sten Ludvigsen, Kristine Lund, Miguel Nussbaum, Peter Reimann, Carolyn Rosé, Nikol Rummel, William Sandoval, James Slotta, Gerry Stahl, Daniel Suthers, Anouschka van Leeuwen, Rupert Wegerif, and Jianwei Zhang. In addition, we particularly wish to thank Carolyn Rosé and the CSCL Committee within ISLS for the initial idea and instigation of the work, Nikol Rummel for the opportunity to share and receive feedback on these provocations-in-progress at CSCL 2017, and Sten Ludvigsen for being open to this somewhat unconventional form of scholarship and facilitating its review.
Full list of the themes used as prompts in the email interviews
Full list of the themes used as prompts in the email interviews
Theme 1: support tools
One of the most spectacular achievements of R&D programs aimed at facilitating Collaborative Learning is the elaboration of new practices that are difficult or impossible without the creation of dedicated technologies. CSCL tools enable the creation of shared spaces in which people inquire together. With these tools, our field’s founders have envisioned the constitution of communities and have revolutionized the world or learning: old metaphors (transmission, acquisition) have been replaced by new metaphors (participation, co-construction, co-creation) and new practices have been enabled by CSCL tools. The kinds of learning communities that have been envisioned (knowledge building communities, communities of practice, expansive learning community, et cetera) are intimately linked with the promotion of CSCL tools. An evaluative appraisal of these notable achievements and areas for future work is needed.
Theme 2: (scripting of) collaboration and argumentation and learning gains
The promotion of CSCL tools has led to the enactment of a profusion of collaborative practices (collaborative inquiry, on-going planning, peer assessment, et cetera). Also, argumentation that had been often seen as a confrontation was conceptualized as a collaborative activity with the elaboration of CSCL tools since disagreements are eventually resolved in a shared space. Novel argumentative practices are also multiple (text-based argumentation, summarizing discussions, co-construction of arguments, et cetera). Participation in collaborative and/or argumentative activities is prompted by scripts. An abundant literature shows how scripts and ontologies entail collaboration and argumentation. However, researchers have questioned the productivity of these ontologies and scripts during and after interaction. An update and critical assessment of this exciting area of research is needed.
Theme 3: dialogism & democratic talk and equity
The enactment of collaborative and argumentative practices is a remarkable achievement in CSCL but the community is not always fully aware of the ideational repercussions of such a success. The CSCL community has first seen the beneficial role of collaboration from the point of view of preparation to adult life at the workplace, or from the point of view of learning gains. The ethical dimensions — dialogic and democratic, have not been brought to the forefront nearly as often. Terms such as “democracy,” “dialogism,” or “intersubjectivity” are borrowed perhaps too easily from the philosophical realm in our community without true alignment to the underlying principles. The idea of equity can be critiqued as mere lip service. The articulation [philosophy (of education) ➔ theory ➔ educational practice] can be crucial in the case of CSCL and this articulation should be highly critical and not only top-down.
Theme 4: scalability & sustainability in authentic environments
The successes in the enactment of novel CSCL practices have been found in contexts in which small groups of students interact often in highly designed/constrained learning environments. The orchestration of collaborative work by a teacher in real classes is quite rare. The impression is that the use of CSCL tools has not been studied sufficiently in authentic contexts. Instead of taking the complexity of the class as a given and focusing on ways to adapt the use of CSCL tools to this complexity, the contrary happens: researchers find ways to propose activities that interrupt what is generally done in educational institutions and that look like experiments rather than real forerunners of change in educational contexts. In this situation, issues of scalability or of sustainability are out of scope in CSCL studies. The issues of scalability, sustainability, and authenticity are critical for the CSCL community if it aims at being relevant to educational change. Here also, an appraisal of what has been done (regarding authenticity) and whether the issues of authenticity, scalability, and sustainability are in the scope or beyond the scope of CSCL needs to be addressed.
Theme 5: workplace learning
Although the terms CSCL and CSCW allude to germane fields, so far, the CSCL community has marginally inquired how CSCL tools can be capitalized in the workplace. This is quite surprising since, in contrast with the school context in which collaboration is proposed or imposed in order to provide a novel setting for learning tasks, collaboration at the workplace is recognized as a necessity. The field of vocational education, which could have represented a bridge between CSCL and CSCW, is underdeveloped too. Studies in CSCL in workplaces or in vocational education are rare. We need to reflect on what has been done so far, and on whether this direction should constitute a major objective to the CSCL community or if it is outside of our primary scope.
Theme 6: computational approaches to understanding collaborative learning
Manual examination of collaborative learning in the form of discourse analysis, content analysis, and the like has been a foundation of CSCL research for many years; however, the emergence of new (and larger) datasets in combination with the increased accessibility of sophisticated computational analytic techniques (for example, data mining methods, SNA, and NLP just to name a few) have opened a new world of possibilities for CSCL methods (both in terms of coding and modelling). Key questions to consider here include how and when such methods are warranted and how they can be applied in theoretically thoughtful ways, questions about the ground-truth of claims, and how such new methods can articulate with, and both inform and be informed by existing manual and qualitative approaches. There is great opportunity in our ability to look in fine-grained ways for complex patterns that might never be detected by hand. There is also danger that the power of computational methods used across large quantities of data without close attention to the context(s) of collaboration in which it was collected or purely atheoretical approaches that produce empirical predictions but are not able to build on or contribute to a growing understanding and knowledge base about collaborative learning. Attention must also be paid to collecting rich and robust data sources in both virtual and physical environments (e.g., via eye tracking, motion capture, et cetera). There is a need to consider where and when such computational approaches are appropriate and how they should articulate with the existing set of approaches used in the field.
Theme 7: moment-to-moment meaning-making (microgenesis) and multi-level temporality
A spectacular and now classical achievement CSCL researchers have undertaken is the fine-grained observation of moments of interaction to describe and define learning processes in collaboration as moments of meaning-making. These are usually moment-to-moment observations that lasted seconds, minutes, or hours. However, another scale of time is badly missing in most CSCL studies — to include observations that encompass weeks or months of use or activity (though there are a few notable exceptions). It seems, however, that since learning and collaboration occur over time, the very expensive microgenetic methods are difficult to apply when considering the intertwining of scales of time to understand learning and development through the use of CSCL tools. While in the past quantitative approaches in CSCL have overwhelmingly relied on aggregated analyses that ignore or smooth out the occurrence of patterns over time, a variety of new quantitative methods that are temporally aware are now available to researchers — examining both flow and sequence. While again this presents exciting opportunities to understanding critical and common patterns in collaborative learning, it also requires the use of sophisticated modelling methods that are new to many CSCL researchers. The relative value of the different approaches to studying collaborative learning over time needs to be assessed and priorities set.
Theme 8: collaborative learning analytics
Building on the sophisticated computational analysis approaches discussed above, this theme refers to the growing field of analytics for learning; that is, devising metrics, indices, and visualization of the learning process that are useful not only to researchers in understanding collaborative learning but can be shown back to learners (and teachers) as a real-time diagnostic aid to support that very collaborative learning activity itself. This can be used to support students’ self−/co−/socially shared regulation and teacher’s orchestration. While opportunities here are great, the translation of sophisticated models and metrics to a form that is useful to teachers and students in the classroom is non-trivial. Questions of how non-researchers can both make sense of the analytics (interpret their meaning in the context of a specific learning context and goals) and take action based on them require an additional knowledge base beyond that traditionally generated in CSCL work. Goals and focus for the creation of CSCL analytics, and perhaps basic questions of if and how this is worth doing, need to be addressed.
Theme 9: adaptive support for CSCL
Similar to the prior theme, the results of computational analysis can be used to create automatic adaptations to CSCL based on the activity that has occurred. This can be thought of in terms of responsive scripting, et cetera. Important questions arise here not simply about what kinds of support can we provide, but what kinds should we provide. With the notion that CSCL often seeks to build not only students’ (individual and collective) understanding of specific ideas/content areas but also their ability to learn in collaboration, there is a concern that too much automated adaptation might rob students of the opportunity to self-regulate and learn skills for future collaboration. In one sense this might lead us to ask whether we see the future of scripting as a scaffold to eventually be faded or a performance support tool to be used in perpetuity. When and how adaptive CSCL support should be given and what it should look like are open areas for debate.
Theme 10: expanding contexts and definitions for collaborative learning
While for many years the CSCL community has held a high standard for what can be considered collaboration (e.g., variations on the definition as a persistent attempt by a small group of learners to maintain a shared problem space), emerging work in the field has looked in more varied contexts and with a somewhat broader definition of collaborative learning; for example, large-scale learning environments such as MOOCs, social networking, gaming, and mobile contexts in which environments were not designed with any particular vision of collaboration in mind and there is not necessarily a meaningful persistent “group” to talk about per se. There are important questions to address here about whether such environments are of interest to our community and if so, does this carry implications for a loosening of the criteria for considering something a collaborative activity. Alternatively, perhaps our community might hold to the existing definition of collaboration but expand the framing of our interest to also include learning in such “social contexts” more broadly. A third option would be to not concern ourselves with these new environments, though this raises questions about our community’s ability to impact the world.
Theme 11: methodological diversity in CSCL
CSCL research is methodologically diverse with techniques drawn from the domains of experimental psychology, computer science, and cultural anthropology, among others. Jeong et al. (2014) note that while the immense methodological diversity in CSCL research is exciting and gives the field richness, it does not facilitate the synthesis of findings into a coherent body of knowledge. Work from different methodological traditions co-exists, but the findings remain disjointed. The problem is compounded by the fact that different methodologies link to different research traditions and standards of evidence (Arnseth and Ludvigsen 2006; Cobb and Jackson 2008). Thus there are critical questions to address about whether (or when) multiple stories about the same collaborative event (which may produce incommensurate claims) are valuable and how (or even if) we can begin to combine these different kinds of knowledge products constructively.
Theme 12: CSCL as ideology
The charge has been made that, for some, the belief in collaborative learning at the heart of CSCL and the claim that it is occurring is not a consequence of analysis but an ideology of sorts. This raises the question of whether our research seeks to answer questions of if collaborative learning is occurring (and if so if it is better than some other alternative) or how collaborative learning can best be supported (presuming that it is a desirable goal). People who are co-present while learning together are not necessarily collaborating and groups of learners do not always comprise a community. More and more voices have begun to raise these questions. There is thus a need for CSCL researchers to become more critical of the foundational premise of collaboration and when it is an appropriate learning strategy. In this way, we can more clearly define the scope and limitations of our field.
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Wise, A.F., Schwarz, B.B. Visions of CSCL: eight provocations for the future of the field. Intern. J. Comput.-Support. Collab. Learn 12, 423–467 (2017). https://doi.org/10.1007/s11412-017-9267-5
- Computer-supported collaborative learning
- CSCL theory
- CSCL methodology
- Adaptive support
- Collaboration scripts
- Conceptual frameworks
- Design principles
- Educational data mining
- Educational impact
- Formal schooling
- Informal learning environments
- Large scale learning
- Learner agency
- Learning analytics
- Mass collaboration
- Online communities
- Qualitative research approaches
- Quantitative research approaches
- Social networks
- Tool design