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Collaborative group engagement in a computer-supported inquiry learning environment

  • Suparna Sinha
  • Toni Kempler Rogat
  • Karlyn R. Adams-Wiggins
  • Cindy E. Hmelo-Silver
Article
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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.

Keywords

Engagement Computer-supported collaborative learning Social interactions Technological affordances 
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Notes

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.

References

  1. 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.CrossRefGoogle Scholar
  2. Azevedo, R. (2005). Using hypermedia as a metacognitive tool for enhancing student learning? The role of self-regulated learning. Educational Psychologist, 40, 199–209.CrossRefGoogle Scholar
  3. Barron, B. (2003). When smart groups fail. The Journal of the Learning Sciences, 12, 307–359.CrossRefGoogle Scholar
  4. 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.Google Scholar
  5. 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.CrossRefGoogle Scholar
  6. 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.Google Scholar
  7. 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.Google Scholar
  8. 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.CrossRefGoogle Scholar
  9. 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.Google Scholar
  10. 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.Google Scholar
  11. 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.Google Scholar
  12. Eilam, B., & Aharon, I. (2003). Students planning in the process of self-regulated learning. Contemporary Educational Psychology, 28, 304–334.CrossRefGoogle Scholar
  13. 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.CrossRefGoogle Scholar
  14. Firestone, W. A. (1993). Alternative arguments for generalizing from data as applied to qualitative research. Educational Researcher, 22, 16–23.CrossRefGoogle Scholar
  15. 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.CrossRefGoogle Scholar
  16. 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.Google Scholar
  17. 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.CrossRefGoogle Scholar
  18. 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.Google Scholar
  19. Guzdial, M., & Turns, J. (2000). Effective discussion through a computer-mediated anchored forum. Journal of the Learning Sciences, 9, 437–469.CrossRefGoogle Scholar
  20. Hakkarainen, K., & Sintonen, M. (2002). Interrogative approach on inquiry and computer-supported collaborative learning. Science & Education, 11, 25–43.CrossRefGoogle Scholar
  21. Harasim, L. (1993). Global networks: Computers and communication. Cambridge: MIT Press.Google Scholar
  22. Herrenkohl, L. R., & Guerra, M. R. (1998). Participant structures, scientific discourse, and student engagement in fourth grade. Cognition and Instruction, 16, 433–475.CrossRefGoogle Scholar
  23. 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.Google Scholar
  24. Hmelo-Silver, C. E. (2004). Problem-based learning: What and how do students learn? Educational Psychology Review, 16, 235–266.CrossRefGoogle Scholar
  25. Hmelo-Silver, C. E., & Azevedo, R. (2006). Understanding complex systems: Some core challenges. The Journal of the Learning Sciences, 15, 53–61.CrossRefGoogle Scholar
  26. 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.CrossRefGoogle Scholar
  27. 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.Google Scholar
  28. 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.CrossRefGoogle Scholar
  29. Järvelä, S., & Hadwin, A. F. (2013). New frontiers: regulating learning in CSCL. Educational Psychologist, 48, 25–39.CrossRefGoogle Scholar
  30. Järvelä, S., & Salovaara, H. (2004). The interplay of motivational goals and cognitive strategies in a new pedagogical culture. European Psychologist, 9, 232–244.CrossRefGoogle Scholar
  31. 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.CrossRefGoogle Scholar
  32. 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.CrossRefGoogle Scholar
  33. 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.Google Scholar
  34. 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.Google Scholar
  35. 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.CrossRefGoogle Scholar
  36. 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.Google Scholar
  37. 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.Google Scholar
  38. 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.Google Scholar
  39. Kumpulainen, K., & Mutanen, M. (1999). The situated dynamics of peer group interaction: An introduction to analytic framework. Learning and Instruction, 9, 443–479.CrossRefGoogle Scholar
  40. Lee, O., & Anderson, C. W. (1993). Task engagement and conceptual change in middle school science classrooms. American Educational Research Journal, 30, 585–610.CrossRefGoogle Scholar
  41. Lee, O., & Brophy, J. (1996). Motivational patterns observed in sixth‐grade science classrooms. Journal of Research in Science Teaching, 33, 303–318.CrossRefGoogle Scholar
  42. 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. Google Scholar
  43. Linnenbrink-Garcia, L., Rogat, T. K., & Koskey, K. L. (2011). Affect and engagement during small group instruction. Contemporary Educational Psychology, 36, 13–24.CrossRefGoogle Scholar
  44. 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.CrossRefGoogle Scholar
  45. 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.CrossRefGoogle Scholar
  46. NGSS Lead States. (2013). Next generation science standards: For states, by states. Washington, DC: The National Academies Press.Google Scholar
  47. 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.CrossRefGoogle Scholar
  48. Pintrich, P. R., & De Groot, E. V. (1990). Motivational and self-regulated comparison of classroom academic performance. Journal of Educational Psychology, 82, 33–40.CrossRefGoogle Scholar
  49. 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.CrossRefGoogle Scholar
  50. 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.
  51. 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.CrossRefGoogle Scholar
  52. 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.CrossRefGoogle Scholar
  53. 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.CrossRefGoogle Scholar
  54. 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.Google Scholar
  55. Rogat, T. K., & Adams-Wiggins, K. R. (2014). Other-regulation in collaborative groups: Implications for regulation quality. Instructional Science, 42, 879–904.CrossRefGoogle Scholar
  56. 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.Google Scholar
  57. 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.CrossRefGoogle Scholar
  58. 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.Google Scholar
  59. 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.CrossRefGoogle Scholar
  60. Ryu, S., & Lombardi, D. (2015). Coding classroom interactions for collective and individual engagement. Educational Psychologist, 50, 70–83.CrossRefGoogle Scholar
  61. Salomon, G., & Globerson, T. (1989). When teams do not function the way they ought to. International Journal of Educational Research, 13, 89–99.CrossRefGoogle Scholar
  62. Scardamalia, M., & Bereiter, C. (1996). Engaging students in a knowledge society. Educational Leadership, 54, 6–10.Google Scholar
  63. 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.Google Scholar
  64. 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.Google Scholar
  65. 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.Google Scholar
  66. Stahl, G. (2013). Theories of cognition in collaborative learning. The International Handbook of Collaborative Learning, 74–90.Google Scholar
  67. 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.Google Scholar
  68. 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.CrossRefGoogle Scholar
  69. 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.CrossRefGoogle Scholar
  70. 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.CrossRefGoogle Scholar
  71. 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.Google Scholar
  72. 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.CrossRefGoogle Scholar
  73. Veermans, M., & Järvelä, S. (2004). Generalized achievement goals and situational coping in inquiry learning. Instructional Science, 32, 269–291.CrossRefGoogle Scholar
  74. Volet, S., Vauras, M., & Salonen, P. (2009). Self-and social regulation in learning contexts: An integrative perspective. Educational Psychologist, 44, 215–226.CrossRefGoogle Scholar
  75. 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.Google Scholar
  76. Zimmerman, B. J. (1990). Self-regulated learning and academic achievement: An overview. Educational Psychologist, 25, 3–17.CrossRefGoogle Scholar
  77. 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.CrossRefGoogle Scholar

Copyright information

© International Society of the Learning Sciences, Inc. 2015

Authors and Affiliations

  • Suparna Sinha
    • 1
  • Toni Kempler Rogat
    • 2
  • Karlyn R. Adams-Wiggins
    • 1
  • Cindy E. Hmelo-Silver
    • 3
  1. 1.Center for Math, Science & Computer EducationRutgers UniversityPiscatawayUSA
  2. 2.Purdue UniversityWest LafayetteUSA
  3. 3.Indiana UniversityBloomingtonUSA

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