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Abstract

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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References

  • Abrahamson, D., Blikstein, P., & Wilensky, U. (2007). Classroom model, model classroom: Computer-supported methodology for investigating collaborative-learning pedagogy. In Proceedings of the Computer Supported Collaborative Learning (CSCL) Conference 2007 (pp. 46–55). New Brunswick: The International Society of the Learning Sciences.

  • Antle, A. N., & Wise, A. F. (2013). Getting down to details: Using theories of cognition and learning to inform tangible user interface design. Interacting with Computers, 25(1), 1–20.

    Article  Google Scholar 

  • Arnseth, H. C., & Ludvigsen, S. (2006). Approaching institutional contexts: Systemic versus dialogic research in CSCL. International Journal of Computer-Supported Collaborative Learning, 1(2), 167–185.

    Article  Google Scholar 

  • Asterhan, C. S. C., & Bouton, E. (2016). Teenage peer-to-peer knowledge sharing through social network sites in secondary schools. Computers & Education, 110, 16–34.

    Article  Google Scholar 

  • Baker, M., & Andriessen, J. (2009). Socio-relational, affective and cognitive dimensions of CSCL interactions: Integrating theoretical-methodological perspectives. In Proceedings of Computer Supported Collaborative Learning (CSCL) Conference 2009 (pp. 31–33). Rhodes: The International Society of the Learning Sciences.

  • Baker, M. J., Quignard, M., Lund, K., & Séjourné, A. (2003). Computer-supported collaborative learning in the space of debate. In B. Wasson, S. Ludvigsen, & U. Hoppe (Eds.), Designing for change in networked learning environments (pp. 11–20). Dordrecht: Springer.

    Chapter  Google Scholar 

  • Baker, M., Andriessen, J., Lund, K., van Amelsvoort, M., & Quignard, M. (2007). Rainbow: A framework for analysing computer-mediated pedagogical debates. International Journal of Computer-Supported Collaborative Learning, 2(2), 315–357.

    Article  Google Scholar 

  • Barab, S. A., & Duffy, T. M. (2000). From practice fields to communities of practice. In D. H. Jonassen & S. M. Land (Eds.), Theoretical foundations of learning environments (pp. 25–55). Mahwah: Lawrence Erlbaum Associates.

    Google Scholar 

  • Barron, B. (2000). Achieving coordination in collaborative problem-solving groups. Journal of the Learning Sciences, 9(4), 403–436.

    Article  Google Scholar 

  • Barron, B. (2003). When smart groups fail. Journal of the Learning Sciences, 12(3), 307–359.

    Article  Google Scholar 

  • Beers, P. J., Boshuizen, H. P. E., Kirschner, P. A., & Gijselaers, W. H. (2005). Computer support for knowledge construction in collaborative learning environments. Computers in Human Behavior, 21(4), 623–643.

    Article  Google Scholar 

  • Bodemer, D., & Dehler, J. (2011). Group awareness in CSCL environments. Computers in Human Behavior, 27(3), 1043–1045.

    Article  Google Scholar 

  • Borge, M., Ong, Y. S., & Rosé, C. P. (2015). Activity design models to support the development of high quality collaborative processes in online settings. In Proceedings of Computer Supported Collaborative Learning (CSCL) Conference 2015 (pp. 427–434). Gothenburg: The International Society of the Learning Sciences.

  • boyd, d. (2014). It’s complicated: The social lives of networked teens. New Haven: Yale University Press.

    Google Scholar 

  • Brooks, C., Greer, J., & Gutwin, C. (2014). The data-assisted approach to building intelligent technology-enhanced learning environments. In J. A. Larusson & B. White (Eds.), Learning analytics: From research to practice (pp. 123–156). New York: Springer.

    Google Scholar 

  • Bruner, J. (1990). Culture and human development: A new look. Human Development, 33(6), 344–355.

    Article  Google Scholar 

  • Bull, S., & Kay, J. (2005). A framework for designing and analysing open learner modelling. In Proceedings of Workshop on Learner Modelling for Reflection, International Conference on Artificial Intelligence in Education, Amsterdam, Netherlands (pp. 81–90).

  • Bull, S., & Pain, H. (1995). “Did I say what I think I said, and do you agree with me?” Inspecting and questioning the student model. In Proceedings of the 7th World Conference on Artificial Intelligence in Education (pp. 501–508). Charlottesville: AACE.

  • Chan, C. K. (2011). Bridging research and practice: Implementing and sustaining knowledge building in Hong Kong classrooms. International Journal of Computer-Supported Collaborative Learning, 6(2), 147–186.

    Article  Google Scholar 

  • Chen, B., & Zhang, J. (2016). Analytics for knowledge creation: Towards epistemic agency and design-mode thinking. Journal of Learning Analytics, 3(2), 139–163.

    Article  Google Scholar 

  • Chi, M. T., & Wylie, R. (2014). The ICAP framework: Linking cognitive engagement to active learning outcomes. Educational Psychologist, 49(4), 219–243.

    Article  Google Scholar 

  • Cobb, P., & Jackson, K. (2008). The consequences of experimentalism in formulating recommendations for policy and practice in mathematics education. Educational Researcher, 37(9), 573–581.

    Article  Google Scholar 

  • Cobb, P., Stephan, M., McClain, K., & Gravemeijer, K. (2001). Participating in classroom mathematical practices. Journal of the Learning Sciences, 10(1–2), 113–164.

    Article  Google Scholar 

  • Coburn, C. E., & Penuel, W. R. (2016). Research–practice partnerships in education: Outcomes, dynamics, and open questions. Educational Researcher, 45(1), 48–54.

    Article  Google Scholar 

  • Cole, M., & The Distributed Literacy Consortium (Eds.). (2006). The fifth dimension: An after-school program built on diversity. New York: Russell Sage.

    Google Scholar 

  • Collins, A. (1992). Toward a design science of education. In E. Lagemann & L. Shulman (Eds.), Issues in education research: Problems and possibilities (pp. 15–22). San Francisco: Jossey-Bass.

    Google Scholar 

  • Collins, A. (2017). What’s worth teaching? Rethinking curriculum in the age of technology. New York: Teachers College Press.

    Google Scholar 

  • Collins, A., & Halverson, R. (2009). Rethinking education in the age of technology: The digital revolution and schooling in America. New York: Teachers College Press.

    Google Scholar 

  • Cuban, L. (1986). Teachers and machines: The classroom use of technology since 1920. New York: Teachers College Press.

    Google Scholar 

  • Cuban, L. (2001). Oversold and underused: Reforming schools through technology, 1980–2000. Cambridge: Harvard University Press.

    Google Scholar 

  • Damşa, C., & Ludvigsen, S. (2016). Learning through interaction and the co-construction of knowledge objects in teacher education. Learning, Culture and Social Interaction, 11, 1–18.

    Article  Google Scholar 

  • Darling-Hammond, L. (1994). Professional development schools: Schools for developing a profession. New York: Teachers College Press.

    Google Scholar 

  • Dascalu, M., Trausan-Matu, S., McNamara, D. S., & Dessus, P. (2015). ReaderBench: Automated evaluation of collaboration based on cohesion and dialogism. International Journal of Computer-Supported Collaborative Learning, 10(4), 395–423.

    Article  Google Scholar 

  • Davies, J. (2007). Display, identity, and the everyday: Self-presentation through online image sharing. Discourse: Studies in the Cultural Politics of Education, 28(4), 549–564.

    Google Scholar 

  • Dehaene, S. (2009). Reading in the brain. New York: Penguin Books.

    Google Scholar 

  • Dillenbourg, P. (1999). What do you mean by collaborative learning? In P. Dillenbourg (Ed.), Collaborative-learning: Cognitive and computational approaches (pp. 1–19). Oxford: Elsevier.

    Google Scholar 

  • Dillenbourg, P. (2002). Over-scripting CSCL: The risks of blending collaborative learning with instructional design. In P. A. Kirschner (Ed.), Three worlds of CSCL. Can we support CSCL? (pp. 61–91). Heerlen: Open Universiteit.

    Google Scholar 

  • Dillenbourg, P. (2013). Design for classroom orchestration. Computers & Education, 69, 485–492.

    Article  Google Scholar 

  • Dillenbourg, P., & Jermann, P. (2007). Designing integrative scripts. In F. Fischer, I. Kollar, H. Mandl, & J. M. Haake (Eds.), Scripting computer-supported collaborative learning: Cognitive, computational and educational perspectives (pp. 275–301). New York: Springer.

    Chapter  Google Scholar 

  • Dillenbourg, P., Huang, J., & Cherubini, M. (2008). Interactive artifacts and furniture supporting collaborative work and learning. New York: Springer.

    Google Scholar 

  • Duggan, M. (2017). Online harassment 2017. Washington, DC: Pew Research Center.

    Google Scholar 

  • Edwards, J. R. (2001). Multidimensional constructs in organizational behavior research: An integrative analytical framework. Organizational Research Methods, 4, 144–192.

    Article  Google Scholar 

  • Erkens, M., Bodemer, D., & Hoppe, H. U. (2016). Improving collaborative learning in the classroom: Text mining based grouping and representing. International Journal of Computer-Supported Collaborative Learning, 11(4), 387–415.

    Article  Google Scholar 

  • Fischer, F., Kollar, I., Stegmann, K., & Wecker, C. (2013). Toward a script theory of guidance in computer-supported collaborative learning. Educational Psychologist, 48(1), 56–66.

    Article  Google Scholar 

  • Fishman, B. J., Penuel, W. R., Allen, A. R., Cheng, B. H., & Sabelli, N. (2013). Design-based implementation research: An emerging model for transforming the relationship of research and practice. In B. J. Fishman, W. R. Penuel, A. Allen, & B. H. Cheng (Eds.), Design-based implementation research: Theories, methods, and exemplars (pp. 136–156). New York: Teachers College Record.

    Google Scholar 

  • Gašević, D., Dawson, S., Rogers, T., & Gašević, D. (2016). Learning analytics should not promote one size fits all: The effects of instructional conditions in predicting academic success. The Internet and Higher Education, 28, 68–84.

    Article  Google Scholar 

  • Gweon, G., Jain, M., McDonough, J., Raj, B., & Rosé, C. P. (2013). Measuring prevalence of other-oriented transactive contributions using an automated measure of speech style accommodation. International Journal of Computer Supported Collaborative Learning, 8(2), 245–265.

    Article  Google Scholar 

  • Habermas, J. (1972). Knowledge and human interests. London: Heinemann Educational Books.

    Google Scholar 

  • Hakkarainen, K. (2009). A knowledge-practice perspective on technology-mediated learning. International Journal of Computer-Supported Collaborative Learning, 4(2), 213–231.

    Article  Google Scholar 

  • Hemsley, J., Garcia-Murillo, M. A., & MacInnes, I. P. (2017). Retweets for policy advocates: Tweet diffusion in the policy discussion space of universal basic income. In Proceedings of the 8th International Conference on Social Media & Society. New York: ACM. https://doi.org/10.1145/3097286.3097294.

  • Herring, S. C. (2001). Computer-mediated discourse. In D. Schiffrin, D. Tannen, & H. Hamilton (Eds.), The handbook of discourse analysis (pp. 612–634). Oxford: Blackwell Publishers.

    Google Scholar 

  • Herring, S. C. (2007). A faceted classification scheme for computer-mediated discourse. Language@Internet, 4(1), 1–37.

    Google Scholar 

  • Hogan, K. (1999a). Sociocognitive roles in science group discourse. International Journal of Science Education, 21(8), 855–882.

    Article  Google Scholar 

  • Hogan, K. (1999b). Thinking aloud together: A test of an intervention to foster students’ collaborative scientific reasoning. Journal of Research in Science Teaching, 36(10), 1085–1109.

    Article  Google Scholar 

  • Hoppe, H. U. & Gassner, K. (2002). Integrating collaborative concept mapping tools with group memory and retrieval functions. In Proceedings of Computer Supported Collaborative Learning (CSCL) Conference 2002 (pp. 716–725). Boulder: The International Society of the Learning Sciences.

  • Hoppe, H. U., & Ploetzner, R. (1999). Can analytic models support learning in groups. In P. Dillenbourg (Ed.), Collaborative-learning: Cognitive and computational approaches (pp. 147–168). Oxford: Elsevier.

    Google Scholar 

  • Introne, J., Iandoli, L., DeCook, J., Yildirim, I. G., & Elzeini, S. (2017). The collaborative construction and evolution of pseudo-knowledge in online conversations. In Proceedings of the 8th International Conference on Social Media & Society. New York: ACM. https://doi.org/10.1145/3097286.3097297.

  • Järvelä, S., & Hadwin, A. F. (2013). New frontiers: Regulating learning in CSCL. Educational Psychologist, 48(1), 25–39.

    Article  Google Scholar 

  • Järvelä, S., Malmberg, J., Sobocinski, M., Haataja, E., & Kirschner, P. (2016). What multimodal data can tell us about the self-regulated learning process? Manuscript submitted for publication.

  • Jeong, H., & Hmelo-Silver, C. E. (2016). Seven affordances of computer-supported collaborative learning: How to support collaborative learning? How can technologies help? Educational Psychologist, 51(2), 247–265.

    Article  Google Scholar 

  • Jeong, H., Hmelo-Silver, C. E., & Yu, Y. (2014). An examination of CSCL methodological practices and the influence of theoretical frameworks 2005–2009. International Journal of Computer-Supported Collaborative Learning, 9(3), 305–334.

    Article  Google Scholar 

  • Kafai, Y. B., Peppler, K. A., & Chapman, R. N. (2009). The computer clubhouse: Constructionism and creativity in youth communities. New York: Teachers College Press.

    Google Scholar 

  • Kali, Y., Eylon, B.-S., McKenney, S., & Kidron, A. (in press). Design-centric research-practice partnerships: Building productive bridges between theory and practice. In M. Spector, B. Lockee, & M. Childress (Eds.), Learning, design, and technology: An international compendium of theory, research, practice and policy. New York: Springer.

  • Kay, J. (2001). Learner control. User Modeling and User-Adapted Interaction, 11(1), 111–127.

    Article  Google Scholar 

  • Kollar, I., Fischer, F., & Hesse, F. W. (2006). Collaboration scripts: A conceptual analysis. Educational Psychology Review, 18(2), 159–185.

    Article  Google Scholar 

  • Konert, J., Burlak, D., & Steinmetz, R. (2014). The group formation problem: An algorithmic approach to learning group formation. In Proceeding of the 9th European Conference on Technology Enhanced Learning (EC-TEL) (pp. 221–234). New York: Springer.

  • Koschmann, T. (2003). CSCL, argumentation, and Deweyan inquiry. In J. Andriessen, M. Baker, & D. Suthers (Eds.), Arguing to learn: Confronting cognitions in computer-supported collaborative learning environments. Dordrecht: Springer.

    Google Scholar 

  • Krippendorff, K. (1980). Content analysis: An introduction to its methodology. Beverly Hills: Sage.

    Google Scholar 

  • Lakoff, G., & Johnson, M. (2008). Metaphors we live by. Chicago: University of Chicago Press.

    Google Scholar 

  • Law, N., Miyake, N., Looi, C. K., Vuorikari, R., Punie, Y., & Linn, M. (2013). Are CSCL and learning sciences research relevant to large-scale educational reform? In Proceedings of Computer Supported Collaborative Learning (CSCL) Conference 2013 (pp. 572–579). Madison: The International Society of the Learning Sciences.

    Google Scholar 

  • Linn, M. C., Clark, D., & Slotta, J. D. (2003). WISE design for knowledge integration. Science Education, 87(4), 517–538.

    Article  Google Scholar 

  • Looi, C. K., So, H. J., Toh, Y., & Chen, W. (2011). The Singapore experience: Synergy of national policy, classroom practice and design research. International Journal of Computer-Supported Collaborative Learning, 6(1), 9–37.

    Article  Google Scholar 

  • Ludvigsen, S., Rasmussen, I., Krange, I., Moen, A., & Middleton, D. (2011). Intersecting trajectories of participation: Temporality and learning. In S. Ludvigsen, A. Lund, I. Rasmussen, & R. Säljö (Eds.), Learning across sites: New tools, infrastructures and practices (pp. 105–122). New York: Routledge.

    Google Scholar 

  • Ludvigsen, S. et al., (2015). The school of the future: Renewal of subjects and competences (Official Norwegian Reports NOU 2015: 8). Oslo: Norwegian Ministry of Education and Research.

  • Lund, K., Molinari, G., Séjourné, A., & Baker, M. (2007). How do argumentation diagrams compare when student pairs use them as a means for debate or as a tool for representing debate? International Journal of Computer-Supported Collaborative Learning, 2(2), 273–295.

    Article  Google Scholar 

  • Lund, K., Rosé, C. P., Suthers, D. D., & Baker, M. (2013). Epistemological encounters in multivocal settings. In D. D. Suthers, K. Lund, C. P. Rosé, C. Teplovs, & N. Law (Eds.), Productive multivocality in the analysis of group interactions (pp. 659–682). New York: Springer.

    Chapter  Google Scholar 

  • Marwick, A. E., & boyd, d. (2011). I tweet honestly, I tweet passionately: Twitter users, context collapse, and the imagined audience. New Media & Society, 13(1), 114–133.

    Article  Google Scholar 

  • McKenney, S., & Reeves, T. C. (2012). Conducting educational design research. London: Routledge.

    Google Scholar 

  • Mu, J., Stegmann, K., Mayfield, E., Rosé, C., & Fischer, F. (2012). The ACODEA framework: Developing segmentation and classification schemes for fully automatic analysis of online discussions. International Journal of Computer-Supported Collaborative Learning, 7(2), 285–305.

    Article  Google Scholar 

  • Nelson, H. G., & Stolterman, E. (2012). The design way: Intentional change in an unpredictable world: Foundations and fundamentals of design competence (2nd ed.). Cambridge: The MIT Press.

    Google Scholar 

  • Nokes-Malach, T. J., Richey, J. E., & Gadgil, S. (2015). When is it better to learn together? Insights from research on collaborative learning. Educational Psychology Review, 27(4), 645–656.

    Article  Google Scholar 

  • OECD. (2016). PISA 2015 assessment and analytical framework: Science, reading, mathematic, financial literacy and collaborative problem solving. Paris: OECD Publishing.

    Book  Google Scholar 

  • Paolucci, M., Suthers, D., & Weiner, A. (1995). Belvedere: Stimulating students’ critical discussion. In Proceedings of the CHI ‘95 Conference Companion on Human Factors in Computing Systems (pp. 123–124). New York: ACM.

  • Penuel, W. R., Fishman, B., Cheng, B., & Sabelli, N. (2011). Organizing research and development and the intersection of learning, implementation and design. Educational Researcher, 40(7), 331–337.

    Article  Google Scholar 

  • Perret-Clermont, A. N., Perret, J. F., & Bell, N. (1991). The social construction of meaning and cognitive activity in elementary school children. In L. B. Resnick, J. M. Levine, & S. D. Teasley (Eds.), Perspectives on socially shared cognition (pp. 41–62). Washington, DC: APA.

    Chapter  Google Scholar 

  • Rainie, L., Anderson, J., & Albright, J. (2017). The future of free speech, trolls, anonymity and fake news online. Washington, DC: Pew Research Center.

    Google Scholar 

  • Rathnayake, C., & Suthers, D. D. (2017). Twitter issue response hashtags as affordances for momentary connectedness. In Proceedings of the 8th International Conference on Social Media & Society. New York: ACM. https://doi.org/10.1145/3097286.3097302.

  • Reich, J. (2015). Rebooting MOOC research. Science, 347(6217), 34–35.

    Article  Google Scholar 

  • Reimann, P. (2009). Time is precious: Variable- and event-centred approaches to process analysis in CSCL research. International Journal of Computer-Supported Collaborative Learning, 4(3), 239–257.

    Article  Google Scholar 

  • Resta, P., & Laferrière, T. (2007). Technology in support of collaborative learning. Educational Psychology Review, 19, 65–83.

    Article  Google Scholar 

  • Roschelle, J., Tatar, D., Chaudhury, S. R., Dimitriadis, Y., Patton, C., & DiGiano, C. (2007). Ink, improvisation, and interactive engagement: Learning with tablets. Computer, 40(9), 38–44.

  • Rosé, C. P., & Ferschke, O. (2016). Technology support for discussion based learning: From computer supported collaborative learning to the future of massive open online courses. International Journal of Artificial Intelligence in Education, 26(2), 660–678.

    Article  Google Scholar 

  • Rosé, C., Wang, Y. C., Cui, Y., Arguello, J., Stegmann, K., Weinberger, A., & Fischer, F. (2008). Analyzing collaborative learning processes automatically: Exploiting the advances of computational linguistics in computer-supported collaborative learning. International Journal of Computer-Supported Collaborative Learning, 3(3), 237–271.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Sacks, H., Schegloff, E. A., & Jefferson, G. (1974). A simplest systematics for the organization of turn-taking for conversation. Language, 50(4), 696–735.

    Article  Google Scholar 

  • Scardamalia, M. (2002). Collective cognitive responsibility for the advancement of knowledge. Liberal Education in a Knowledge Society, 97, 67–98.

    Google Scholar 

  • Scardamalia, M., & Bereiter, C. (1993). Technologies for knowledge-building discourse. Communications of the ACM, 36(5), 37–41.

    Article  Google Scholar 

  • Scardamalia, M., & Bereiter, C. (1994). Computer support for knowledge-building communities. Journal of the Learning Sciences, 3, 265–283.

    Article  Google Scholar 

  • Scardamalia, M., & Bereiter, C. (2003). Knowledge building. In J. W. Guthrie (Ed.), Encyclopedia of education (2nd ed., pp. 1370–1373). New York: Macmillan Reference.

    Google Scholar 

  • Scardamalia, M., & Bereiter, C. (2006). Knowledge building: Theory, pedagogy, and technology. In R. K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (2nd ed., pp. 97–118). New York: Cambridge University Press.

    Google Scholar 

  • Schlager, M., Fusco, J., & Schank, P. (2002). Evolution of an online education community of practice. In K. A. Renninger & W. Shumar (Eds.), Building virtual communities: Learning and change in cyberspace (pp. 129–158). Cambridge: Cambridge University Press.

    Chapter  Google Scholar 

  • Schneider, B., & Pea, R. (2013). Real-time mutual gaze perception enhances collaborative learning and collaboration quality. International Journal of Computer-Supported Collaborative Learning, 8(4), 375–397.

    Article  Google Scholar 

  • Schneider, B., & Pea, R. (2014). Toward collaboration sensing. International Journal of Computer-Supported Collaborative Learning, 9(4), 371–395.

    Article  Google Scholar 

  • Schneider, B., & Pea, R. (2015). Does seeing one another’s gaze affect group dialogue? A computational approach. Journal of Learning Analytics, 2(2), 107–133.

    Article  Google Scholar 

  • Schwartz, D. L. (1999). The productive agency that drives collaborative learning. In P. Dillenbourg (Ed.), Collaborative learning: Cognitive and computational approaches (pp. 197–218). Oxford: Elsevier Science.

    Google Scholar 

  • Schwarz, B. B., & Asterhan, C. S. (2011). E-moderation of synchronous discussions in educational settings: A nascent practice. Journal of the Learning Sciences, 20(3), 395–442.

    Article  Google Scholar 

  • Schwarz, B. B., & Caduri, G. (2016). Novelties in the use of social networks by leading teachers in their classes. Computers & Education, 102, 35–51.

    Article  Google Scholar 

  • Schwarz, B. B., de Groot, R., Mavrikis, M., & Dragon, T. (2015). Learning to learn together with CSCL tools. International Journal of Computer-Supported Collaborative Learning, 10(3), 239–271.

    Article  Google Scholar 

  • Schwarz, B. B., Prusak, N., Swidan, O., Livny, A. & Gal, K. (2017a). Orchestrating deep learning: A case study in a geometry class. Manuscript submitted for publication.

  • Schwarz, B. B., Rosenberg, H., & Asterhan, C. S. C. (Eds.) (2017b). Breaking down barriers? Teachers, students and social network sites (in Hebrew). Tel Aviv: MOFET Books.

  • Slakmon, B., & Schwarz, B. B. (2017). “You will be a polis”: Political (democratic?) education, public space and CSCL discussions. The Journal of the Learning Sciences, 26(2), 184–225.

    Article  Google Scholar 

  • Stahl, G. (2009). Studying virtual math teams. New York: Springer.

    Book  Google Scholar 

  • Stahl, G. (2015). Constructing dynamic triangles together: The development of mathematical group cognition. New York: Cambridge University Press.

    Google Scholar 

  • Stahl, G. (2016). The group as paradigmatic unit of analysis: The contested relationship of computer-supported collaborative learning to the learning sciences. In M. A. Evans, M. J. Packer, & R. K. Sawyer (Eds.), Reflections on the learning sciences (pp. 76–102). Cambridge: Cambridge University Press.

    Google Scholar 

  • Stahl, G., & Hesse, F. (2011). Let a hundred flowers bloom; let a hundred schools of thought contend. International Journal of Computer-Supported Collaborative Learning, 6(2), 139–145.

    Article  Google Scholar 

  • Stahl, G., Koschmann, T., & Suthers, D. (2006). Computer-supported collaborative learning: A historical perspective. In R. K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (2nd ed., pp. 409–426). Cambridge: Cambridge University Press.

    Google Scholar 

  • Stahl, G., Ludvigsen, S., Law, N., & Cress, U. (2014). CSCL artifacts. International Journal of Computer-Supported Collaborative Learning, 9(3), 237–245.

    Article  Google Scholar 

  • Stegmann, K., & Fischer, F. (2011). Quantifying qualities in collaborative knowledge construction: The analysis of online discussions. In S. Puntambekar, G. Erkens, & C. Hmelo-Silver (Eds.), Analyzing interactions in CSCL (pp. 247–268). New York: Springer.

    Chapter  Google Scholar 

  • Stromer-Galley, J., & Muhlberger, P. (2009). Agreement and disagreement in group deliberation: Effects on deliberation satisfaction, future engagement, and decision legitimacy. Political Communication, 26(2), 173–192.

    Article  Google Scholar 

  • Suthers, D. D. (2003). Representational guidance for collaborative inquiry. In J. Andriessen, M. Baker, & D. Suthers (Eds.), Arguing to learn: Confronting cognitions in computer-supported collaborative learning environments (pp. 27–46). Norwell: Kluwer.

    Chapter  Google Scholar 

  • Suthers, D. D. (2006). Technology affordances for intersubjective meaning-making: A research agenda for CSCL. International Journal of Computer-Supported Collaborative Learning, 1(3), 315–337.

    Article  Google Scholar 

  • Suthers, D. D. (2015). From contingencies to network-level phenomena: Multilevel analysis of activity and actors in heterogeneous networked learning environments. In Proceedings of the Fifth International Conference on Learning Analytics and Knowledge (pp. 368–377). ACM.

  • Suthers, D. D., Lund, K., Rosé, C. P., Teplovs, C., & Law, N. (2013). Productive multivocality in the analysis of group interactions. New York: Springer.

    Book  Google Scholar 

  • Tabak, I. (2004). Synergy: A complement to emerging patterns of distributed scaffolding. The Journal of the Learning Sciences, 13(3), 305–335.

    Article  Google Scholar 

  • Tang, K. Y., Tsai, C. C., & Lin, T. C. (2014). Contemporary intellectual structure of CSCL research (2006–2013): A co-citation network analysis with an education focus. International Journal of Computer-Supported Collaborative Learning, 9(3), 335.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • ten Have, P. (1990). Methodological issues in conversation analysis 1. Bulletin of Sociological Methodology, 27(1), 23–51.

    Article  Google Scholar 

  • The Design-Based Research Collective. (2003). Design-based research: An emerging paradigm for educational inquiry. Educational Researcher, 32(1), 5–8.

    Article  Google Scholar 

  • Tomasello, M. (1995). Joint attention as social cognition. In C. Moore & P. J. Dunham (Eds.), Joint attention: Its origins and role in development (pp. 103–130). Hillsdale: Erlbaum Associates.

    Google Scholar 

  • van Leeuwen, A. (2015). Learning analytics to support teachers during synchronous CSCL: Balancing between overview and overload. Journal of Learning Analytics, 2(2), 138–162.

    Article  Google Scholar 

  • Walker, E., Rummel, N., & Koedinger, K. (2014). Adaptive intelligent support to improve peer tutoring in algebra. International Journal of Artificial Intelligence in Education, 24(1), 33–61.

    Article  Google Scholar 

  • Wang, Y., Leon, P. G., Scott, K., Chen, X., Acquisti, A., & Cranor, L. F. (2013). Privacy nudges for social media: An exploratory Facebook study. In Proceedings of the 22nd International Conference on World Wide Web (pp. 763–770). New York: ACM. https://doi.org/10.1145/2487788.2488038.

  • Webb, N. M., & Palincsar, A. S. (1996). Group processes in the classroom. Englewood Cliffs: Prentice-Hall International.

    Google Scholar 

  • Wegerif, R. (2008). Dialogic or dialectic? The significance of ontological assumptions in research on educational dialogue. British Educational Research Journal, 34(3), 347–361.

    Article  Google Scholar 

  • Wegerif, R. (2013). Dialogic: Education for the internet age. London: Routledge.

    Google Scholar 

  • Wegerif, R., Postlethwaite, K., Skinner, N., Mansour, N., Morgan, A., & Hetherington, L. (2013). Dialogic science education for diversity. In N. Mansour & R. Wegerif (Eds.), Science education for diversity (pp. 3–22). Dordrecht: Springer.

    Chapter  Google Scholar 

  • Weinberger, A., Stegmann, K., & Fischer, F. (2007). Knowledge convergence in collaborative learning: Concepts and assessment. Learning and Instruction, 17(4), 416–426.

    Article  Google Scholar 

  • Wise, A. F., & Chiu, M. M. (2011). Analyzing temporal patterns of knowledge construction in a role-based online discussion. International Journal of Computer-Supported Collaborative Learning, 6(3), 445–470.

    Article  Google Scholar 

  • Wise, A. F., & Cui, Y. (2017). Finding community in the crowd: The importance of tie definition and networking partitioning in examining social learning in MOOCs. Manuscript submitted for publication.

  • Wise, A. F., & Shaffer, D. W. (2015). Why theory matters more than ever in the age of big data. Journal of Learning Analytics, 2(2), 5–13.

    Article  Google Scholar 

  • Wise, A., Zhao, Y., & Hausknecht, S. (2014). Learning analytics for online discussions: Embedded and extracted approaches. Journal of Learning Analytics, 1(2), 48–71.

    Article  Google Scholar 

  • Wise, A. F., Azevedo, R., Stegmann, K., Malmberg J., Rosé C. P., & Fischer, F. (2015). CSCL and learning analytics: Opportunities to support social interaction, self-regulation and socially shared regulation. In Proceedings of Computer Supported Collaborative Learning (CSCL) Conference 2015 (pp. 607–614). Gothenburg: The International Society of the Learning Sciences.

  • Wise, A. F., Vytasek, J. M., Hausknecht, S., & Zhao, Y. (2016). Developing learning analytics design knowledge in the “middle space”: The student tuning model and align design framework for learning analytics use. Online Learning, 20(2), 1–28.

  • Wise, A. F., Cui, Y., Jin, W. Q., & Vytasek, J. M. (2017). Mining for gold: Identifying content-related MOOC discussion threads across domains through linguistic modeling. The Internet and Higher Education, 32, 11–28.

    Article  Google Scholar 

  • Zhang, J., Scardamalia, M., Reeve, M., & Messina, R. (2009). Designs for collective cognitive responsibility in knowledge-building communities. Journal of the Learning Sciences, 18(1), 7–44.

    Article  Google Scholar 

  • Ziegler, M. F., Paulus, T., & Woodside, M. (2015). Informal learning as group meaning-making: Visible talk in online communities. In O. Mejiuni, P. Cranton, & O. Táíwò (Eds.), Measuring and analyzing informal learning in the digital age (pp. 180–196). Hershey: IGI Global.

    Chapter  Google Scholar 

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Acknowledgements

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.

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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

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