Abstract
Computer-supported collaborative learning environments provide opportunities for students to collaborate in inquiry-based practices to solve authentic problems, using technological tools as a resource. However, we have limited understanding of the quality of engagement fostered in these contexts, in part due to the narrowness of engagement measures. To help judge the quality of engagement, we extend existing engagement frameworks, which have studied this construct as a stable and decontextualized individual difference. We conceptualize engagement as multi-faceted (including behavioral, social, cognitive and conceptual-to-consequential forms), dynamic, contextualized and collective. Using our newly developed observational measure, we examine the variation of engagement quality for ten groups. Subsequently, we differentiate low and high quality collaborative engagement through a close qualitative analysis of two groups. Here, we explore the interrelationships among engagement facets and how these relations unfolded over the course of group activity during a lesson. Our results suggest that the quality of behavioral and social engagement differentiated groups demonstrating low quality engagement, but cognitive and conceptual-to-consequential forms are required for explaining high quality engagement. Examination of interrelations indicate that behavioral and social engagement fostered high quality cognitive engagement, which then facilitated consequential engagement. Here, engagement is evidenced as highly interrelated and mutually influencing interactions among all four engagement facets. These findings indicate the benefits of studying engagement as a multi-faceted phenomenon and extending existing conceptions to include consequential engagement, with implications for designing technologies that scaffold high quality cognitive and conceptual-to-consequential engagement in a computer-supported collaborative learning environment.
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References
Arvaja, M., Salovaara, H., Hakkinen, P., & Järvelä, S. (2007). Combining individual and group-level perspectives for studying collaborative knowledge construction in context. Learning and Instruction, 17, 448–459.
Azevedo, R. (2005). Using hypermedia as a metacognitive tool for enhancing student learning? The role of self-regulated learning. Educational Psychologist, 40, 199–209.
Barron, B. (2003). When smart groups fail. The Journal of the Learning Sciences, 12, 307–359.
Blumenfeld, P., & Meece, J. L. (1988). Task factors, teacher behavior, and students’ involvement and use of learning strategies in science. Elementary School Journal, 46, 26–43.
Blumenfeld, P., Soloway, E., Marx, R., Krajcik, J., Guzdial, M., & Palincsar, A. (1991). Motivating project-based learning: Sustaining the doing, supporting the learning. Educational Psychologist, 26, 369–398.
Blumenfeld, P. C., Kempler, T. M., & Krajcik, J. S. (2006). Motivation and cognitive engagement in learning environments. In K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (pp. 475–488). New York: Cambridge University Press.
Chan, C. K. K. (2013). Collaborative knowledge building: Toward a knowledge creation perspective. In C. E. Hmelo-Silver et al. (Eds.), International handbook of collaborative learning (pp. 437–461). New York: Routledge.
Chernobilsky, E., DaCosta, M. C., & Hmelo-Silver, C. E. (2004). Learning to talk the educational psychology talk through a problem-based course. Instructional Science, 32, 319–356.
Connell, J. P., & Wellborn, J. G. (1990). Competence, autonomy and relatedness: A motivational analysis of self-system processes. In M. R. Gunnar & L. A. Sroufe (Eds.), The Minnesota symposium on child psychology (Vol. 22, (pp. 43–77). Hillsdale, NJ: Erlbaum.
Dillenbourg, P., Järvelä, S., & Fischer, F. (2009). The evolution of research on computer-supported collaborative learning. In Technology-enhanced learning (pp. 3–19). Springer Netherlands.
Eberbach, C., Hmelo-Silver, C., Jordan, R., Sinha, S., & Goel, A. (2012). Multiple trajectories for understanding ecosystems. In J. van Aalst, K. Thompson, M. J. Jacobson & P. Reimann (Eds.), The future of learning: Proceedings of the 10th International Conference of the Learning Sciences (ICLS 2012) – Volume 1 (pp. 411–418). Sydney, Australia: ISLS.
Eilam, B., & Aharon, I. (2003). Students planning in the process of self-regulated learning. Contemporary Educational Psychology, 28, 304–334.
Engle, R. A., & Conant, F. R. (2002). Guiding principles for fostering productive disciplinary engagement: explaining an emergent argument in a community of learners classroom. Cognition and Instruction, 20, 399–483.
Firestone, W. A. (1993). Alternative arguments for generalizing from data as applied to qualitative research. Educational Researcher, 22, 16–23.
Fredricks, J., Blumenfeld, P., & Paris, P. (2004). A school engagement potential of the concept and state of the evidence. Review of Educational Research, 74, 59–109.
Greeno, J. G. (2006). Learning in activity. In R. K. Sawyer (Ed.), Cambridge handbook of the learning sciences (pp. 79–96). New York: Cambridge University Press.
Gresalfi, M., & Barab, S. (2010). Learning for a reason: Supporting forms of engagement by designing tasks and orchestrating environments. Theory Into Practice, 50, 300–310.
Gresalfi, M., Barab, S., Siyahhan, S., & Christensen, T. (2009). Virtual worlds, conceptual understanding, and me: Designing for consequential engagement. On the Horizon, 17, 21–34.
Guzdial, M., & Turns, J. (2000). Effective discussion through a computer-mediated anchored forum. Journal of the Learning Sciences, 9, 437–469.
Hakkarainen, K., & Sintonen, M. (2002). Interrogative approach on inquiry and computer-supported collaborative learning. Science & Education, 11, 25–43.
Harasim, L. (1993). Global networks: Computers and communication. Cambridge: MIT Press.
Herrenkohl, L. R., & Guerra, M. R. (1998). Participant structures, scientific discourse, and student engagement in fourth grade. Cognition and Instruction, 16, 433–475.
Hmelo, C. E., Guzdial, M., & Turns, J. (1998). Computer-support for collaborative learning: Learning to Support Student Engagement. Journal of Interactive Learning Research, 9, 107--130.
Hmelo-Silver, C. E. (2004). Problem-based learning: What and how do students learn? Educational Psychology Review, 16, 235–266.
Hmelo-Silver, C. E., & Azevedo, R. (2006). Understanding complex systems: Some core challenges. The Journal of the Learning Sciences, 15, 53–61.
Hmelo-Silver, C. E., Marathe, S., & Liu, L. (2007). Fish swim, rocks sit, and lungs breathe: Expert-novice understanding of complex systems. The Journal of the Learning Sciences, 16, 307–331.
Hmelo-Silver, C. E., Jordan, R., Honwad, S., Eberbach, C., Sinha, S., Goel, A., Rugaber, S., & Joyner, D. (2011). Foregrounding behaviors and functions to promote ecosystem understanding. Proceedings of Hawaii International Conference on Education (pp. 2005–2013). Honolulu HI: HICE.
Iiskala, T., Vauras, M., Lehtinen, E., & Salonen, P. (2011). Socially shared metacognition of dyads of pupils in collaborative mathematical problem-solving processes. Learning and Instruction, 21, 379–393.
Järvelä, S., & Hadwin, A. F. (2013). New frontiers: regulating learning in CSCL. Educational Psychologist, 48, 25–39.
Järvelä, S., & Salovaara, H. (2004). The interplay of motivational goals and cognitive strategies in a new pedagogical culture. European Psychologist, 9, 232–244.
Järvelä, S., Volet, S., & Järvonoja, H. (2010). Research on motivation in collaborative learning: moving beyond the cognitive–situative divide and combining individual and social processes. Educational Psychologist, 45, 15–27.
Jordan, R. C., Hmelo-Silver, C. E., Liu, L., & Gray, S. (2013). Fostering reasoning about complex systems: using the aquarium as a model ecosystem. Applied Environmental Education and Communication, 12, 55–64.
Jordan, R.C., Brooks, W.R., Hmelo-Silver, C.E. Eberbach, C. and Sinha, S. (2014). Balancing broad ideas with context: An evaluation of student accuracy in describing ecosystem processes after a system-level intervention. Journal of Biological Education.
Kapur, M., Voiklis, J., & Kinzer, C. K. (2011). A complexity-grounded model for the emergence of convergence in CSCL groups. In Analyzing Interactions in CSCL (pp. 3–23). Springer US.
Khosa, D. K., & Volet, S. E. (2014). Productive group engagement in cognitive activity and metacognitive regulation during collaborative learning: Can it explain differences in students’ conceptual understanding? Metacognition and Learning, 9, 287–307.
Koschmann, T., Zemel, A., Conlee-Stevens, M., Young, N., Robbs, J., & Barnhart, A. (2003). Problematizing the problem. In Designing for Change in Networked Learning Environments (pp. 37–46). Springer Netherlands.
Koshmann, T., Zemel, A., Conlee-Stevens, M., Young, N. P., Robbs, J. E., & Barnhart, A. (2005). How do people learn?. In Barriers and Biases in Computer-Mediated Knowledge Communication (pp. 265–294). Springer US.
Krejins, K., Kirschner, P. A., & Jochems, W. (2002). The sociability of computer-supported collaborative learning environments. Journal of Education Technology and Society, 5, 8–22.
Kumpulainen, K., & Mutanen, M. (1999). The situated dynamics of peer group interaction: An introduction to analytic framework. Learning and Instruction, 9, 443–479.
Lee, O., & Anderson, C. W. (1993). Task engagement and conceptual change in middle school science classrooms. American Educational Research Journal, 30, 585–610.
Lee, O., & Brophy, J. (1996). Motivational patterns observed in sixth‐grade science classrooms. Journal of Research in Science Teaching, 33, 303–318.
Lee, A., O’Donnell, A.M., & Rogat, T.K. (2015). Exploration of the Cognitive Regulatory Sub-processes Employed by Groups Characterized by Socially Shared and Other-regulation in a CSCL context. Computers in Human Behavior.
Linnenbrink-Garcia, L., Rogat, T. K., & Koskey, K. L. (2011). Affect and engagement during small group instruction. Contemporary Educational Psychology, 36, 13–24.
Lipponen, L., Rahikainen, M., Lallimo, J., & Hakkarainen, K. (2003). Patterns of participation and discourse in elementary students’ computer-supported collaborative learning. Learning and Instruction, 13, 487–509.
Molenaar, I., & Chiu, M. M. (2014). Dissecting sequences of regulation and cognition: Statistical discourse analysis of primary school children’s collaborative learning. Metacognition and learning, 9, 137–160.
NGSS Lead States. (2013). Next generation science standards: For states, by states. Washington, DC: The National Academies Press.
Pintrich, P. (2000). The role of goal orientation in self–regulated learning. In M. Boekaerts, P. Pintrich, & M. Zeidner (Eds.), Handbook of self–regulation (pp. 451–502). San Diego: Academic Press.
Pintrich, P. R., & De Groot, E. V. (1990). Motivational and self-regulated comparison of classroom academic performance. Journal of Educational Psychology, 82, 33–40.
Pintrich, P. R., Conley, A. M., & Kempler, T. M. (2003). Current issues in achievement goal theory and research. International Journal of Educational Research, 39, 319–337.
Quellmalz, E.S., Timms, M.J., & Schneider, S.A. (2009). Assessment of student learning in science simulations and games. Paper commissioned for the National Research Council Workshop on Gaming and Simulations, October 6–7, Washington, DC. Available: http://www7.nationalacademies.org/bose/Schneider_Gaming_Com- missionedPaper.pdf.
Quintana, C., Reiser, B. J., Davis, E. A., Krajcik, J., Fretz, E., & Golan, R. (2004). A scaffolding design framework for software to support science inquiry. Journal of the Learning Sciences, 13, 337–386.
Renninger, K. A., & Shumar, W. (2002). Community building with and for teachers: The Math Forum as a resource for teacher professional development. In K. A. Renninger & W. Shumar (Eds.), Building virtual communities: Learning and change in cyberspace (pp. 60–95). New York: Cam- bridge University Press.
Renninger, K. A., & Shumar, W. (2004). The centrality of culture and community to participant learning at and with the Math Forum. In S. A. Barab, R. Kling, & J. H. Gray (Eds.), Designing for virtual communities in the service of learning (pp. 181–209). New York: Cambridge University Press.
Rochelle, J. (1996). Learning by collaborating: Convergent conceptual change. In Koschmann, T. (Ed.), CSCL: Theory and practice of an emerging paradigm (pp. 171–186). New Jersey: Lawrence Erlbaum Associates Inc.
Rogat, T. K., & Adams-Wiggins, K. R. (2014). Other-regulation in collaborative groups: Implications for regulation quality. Instructional Science, 42, 879–904.
Rogat, T. K., & Adams-Wiggins, K. R. (2015). Interrelation between regulatory and socioemotional processes within collaborative groups characterized by facilitative and directive other-regulation. Computers in Human Behavior. doi:10.1016/j.chb.2015.01.026.
Rogat, T. K., & Linnenbrink-Garcia, L. (2011). Socially shared regulation in collaborative groups: An analysis of the interplay between quality of social regulation and group processes. Cognition and Instruction, 29, 375–415.
Rogat, T. K., & Linnenbrink-Garcia, L. (2013). Understanding the quality variation of socially shared regulation: A focus on methodology. In M. Vauras & S. Volet (Eds.), Interpersonal regulation of learning and motivation: Methodological advances (pp. 102–125). London: Routledge.
Roschelle, J., & Teasley, S. (1995). The construction of shared knowledge in collaborative problem solving. In C. O’Malley (Ed.), Computer-supported collaborative learning (pp. 69–197). Berlin: Springer.
Ryu, S., & Lombardi, D. (2015). Coding classroom interactions for collective and individual engagement. Educational Psychologist, 50, 70–83.
Salomon, G., & Globerson, T. (1989). When teams do not function the way they ought to. International Journal of Educational Research, 13, 89–99.
Scardamalia, M., & Bereiter, C. (1996). Engaging students in a knowledge society. Educational Leadership, 54, 6–10.
Soloway, E., Guzdial, M., Brade, K., Hohmann, L., Tabak, I., Weingrad, P., & Blumenfeld, P. (1992). Technological support for the learning and doing of design. In M Jones, & P.H. Winner (Eds.), Adaptive learning environments (pp. 173–200). Springer Berlin Heidelberg.
Stahl, G. (2000). A model of collaborative knowledge-building. Paper presented at the Fourth International Conference of the Learning Sciences (ICLS '00). Ann Arbor, MI. Proceedings pp. 70–77: Lawrence Erlbaum Associates.
Stahl, G. (2004). Building collaborative knowing: Contributions to a social theory of CSCL. In J. W. Strijbos, P. Kirschner, & R. L. Martens (Eds.), What we know about CSCL in higher education. Amsterdam: Kluwer.
Stahl, G. (2013). Theories of cognition in collaborative learning. The International Handbook of Collaborative Learning, 74–90.
Stahl, G., Koschmann, T., & Suthers, D. D. (2006). Computer-supported collaborative learning. In K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (pp. 409–426). New York: Cambridge University Press.
Suthers, D. D. (2006). Technology affordances for intersubjective meaning making: A research agenda for CSCL. International Journal of Computer-Supported Collaborative Learning, 1, 315–337.
Teasley, S. D. (2011). Thinking about methods to capture effective collaborations. In S. Puntambekar, G. Erkens, & C. Hmelo-Silver (Eds.), Analyzing collaborative interactions in CSCL: Methods, approaches, and issues (pp. 131–142). New York: Springer.
Van den Bossche, P., Gijselaers, W. H., Segers, M., & Kirschner, P. A. (2006). Social and cognitive factors driving teamwork in collaborative learning environments team learning beliefs and behaviors. Small Group Research, 37, 490–521.
Vattam, S., Goel, A. K., Rugaber, S., Hmelo-Silver, C. E., Jordan, R., Gray, S., & Sinha, S. (2011). Understanding complex natural systems by articulating structure-behavior- function models. Educational Technology and Society, 14, 66–81.
Vauras, M., Iiskala, T., Kajamies, A., Kinnunen, R., & Lehtinen, E. (2003). Shared-regulation and motivation of collaborating peers: A case analysis. Psychologia: An International Journal of Psychology in the Orient, 46, 19–37.
Veermans, M., & Järvelä, S. (2004). Generalized achievement goals and situational coping in inquiry learning. Instructional Science, 32, 269–291.
Volet, S., Vauras, M., & Salonen, P. (2009). Self-and social regulation in learning contexts: An integrative perspective. Educational Psychologist, 44, 215–226.
Webb, N. M., Ing, M., Kersting, N., & Nemer, K. M. (2006). Help seeking in cooperative learning groups. Strategic help seeking: Implications for learning and teaching, 45–115.
Zimmerman, B. J. (1990). Self-regulated learning and academic achievement: An overview. Educational Psychologist, 25, 3–17.
Zimmerman, B. J. (2000). Attaining self-regulation: A social cognitive perspective. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 13–39). San Diego: Academic Press.
Acknowledgments
This research was funded by IES grant # R305A090210. Conclusions or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of IES. We also thank the teachers and students who participated in this research.
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1 Linnenbrink-Garcia et al. (2011) refer to social-behavioral engagement, integrating the facets of behavioral and social engagement into a single dimension. We separate behavioral and social engagement because we are interested in studying the influence of independent facets for engagement quality within collaborative groups, rather than have an implicit assumption that withdrawal of participation and disrespect necessarily co-occur.
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Appendix
Example Application of Scoring Criteria
To illustrate how the coding was applied to student drawings, we examine the pre and post-test drawings of a participating student (See example in figure below). We applied the Macro/Micro code as Level 1 in the pre-test example because all structures (e.g., fish, coral, seaweed) are macroscopic, whereas the posttest example is coded as Level 3 because the student identifies relations between macro and micro levels (e.g., fish and ammonia, algae and oxygen). We applied the Biotic/Abiotic code as Level 1 in the pre-test example because the student drew a largely biotic scene and included only one abiotic structure (ocean floor).
In the posttest example, we coded this as Level 3 because the student included examples of biotic and abiotic structure relations (e.g., algae and sunlight; bacteria and nitrate). In both drawings, no structures were deemed irrelevant so Extraneous Structures was coded as Level 1 for each. For SBF, the pre-test example was coded as Level 2 because the student related components and mechanism relations (e.g., starfish eats the clams; fish lives in the coral). In the posttest example, the student reached Level 3 of the SBF code (e.g., sunlight causes algae to grow links to algae makes oxygen for fish).
* Note: Student’s drawing at pretest (left) and posttest (right) with student’s explanatory labels in red.
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Sinha, S., Rogat, T.K., Adams-Wiggins, K.R. et al. Collaborative group engagement in a computer-supported inquiry learning environment. Intern. J. Comput.-Support. Collab. Learn 10, 273–307 (2015). https://doi.org/10.1007/s11412-015-9218-y
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DOI: https://doi.org/10.1007/s11412-015-9218-y