Advertisement

Providing different types of group awareness information to guide collaborative learning

  • Lenka SchnaubertEmail author
  • Daniel Bodemer
Article

Abstract

Cognitive group awareness tools are a means to guide collaborative learning activities by providing knowledge-related information to the learners. While positive effects of such tools are firmly established, there is no consistency with regard to the awareness information used and a wide range of target concepts exist. However, attempts to compare and integrate the effects of different types of group awareness information are rare. To reduce this gap, our study aims to compare metacognitive and cognitive group awareness information, combining CSCL research and research on metacognition. In our experimental study, 260 university students discussed assumptions on blood-sugar regulation and diabetes mellitus in dyads. We tested the effects of providing cognitive group awareness information on the learners’ assumptions (factor 1) and metacognitive group awareness information on their confidence (factor 2) on individual metacognitive and cognitive outcome measures and on the learners’ regulation of the collaborative process, i.e., the selection of discussion topics based on confidence in knowledge (confidence-based regulation) and based on agreement regarding assumptions (conflict-based regulation). We found that visualizing information strongly impacts joint regulation and that learners seem to integrate the information provided to steer their learning. However, while the learners gained knowledge and confidence during collaboration, providing group awareness information did not have the expected impact on learning outcomes. Reasons and implications of these results in light of previous research on metacognition and group awareness are discussed.

Keywords

Computer-supported collaborative learning Group awareness Guidance Metacognition Self-regulated learning 

Notes

Acknowledgements

We would like to thank Christian Schlusche, M.Sc., for the extensive technical support he provided.

References

  1. Apache Software Foundation. (2013). Apache CouchDB [computer program]. Wakefield: Apache Software Foundation.Google Scholar
  2. Ariel, R., Dunlosky, J., & Bailey, H. (2009). Agenda-based regulation of study-time allocation: When agendas override item-based monitoring. Journal of Experimental Psychology: General, 138(3), 432–447.  https://doi.org/10.1037/a0015928.CrossRefGoogle Scholar
  3. Bodemer, D. (2011). Tacit guidance for collaborative multimedia learning. Computers in Human Behavior, 27(3), 1079–1086.  https://doi.org/10.1016/j.chb.2010.05.016.CrossRefGoogle Scholar
  4. Bodemer, D., & Dehler, J. (2011). Group awareness in CSCL environments. Computers in Human Behavior, 27(3), 1043–1045.  https://doi.org/10.1016/j.chb.2010.07.014.CrossRefGoogle Scholar
  5. Bodemer, D., Janssen, J., & Schnaubert, L. (2018). Group awareness tools for computer-supported collaborative learning. In F. Fischer, C. E. Hmelo-Silver, S. R. Goldman, & P. Reimann (Eds.), International handbook of the learning sciences (pp. 351–358). New York: Routledge/Taylor & Francis.Google Scholar
  6. Bodemer, D., & Scholvien, A. (2014). Providing knowledge-related partner information in collaborative multimedia learning: Isolating the core of cognitive group awareness tools. In C.-C. Liu, H. Ogata, S. C. Kong, & A. Kashihara (Eds.), Proceedings of the 22nd international conference on computers in education ICCE 2014 (pp. 171–179). Nara: APSCE.Google Scholar
  7. Buchs, C., Butera, F., Mugny, G., & Darnon, C. (2004). Conflict elaboration and cognitive outcomes. Theory Into Practice, 43(1), 23–30.  https://doi.org/10.1207/s15430421tip4301_4.CrossRefGoogle Scholar
  8. Buder, J. (2011). Group awareness tools for learning: Current and future directions. Computers in Human Behavior, 27(3), 1114–1117.  https://doi.org/10.1016/j.chb.2010.07.012.CrossRefGoogle Scholar
  9. Buder, J., & Bodemer, D. (2008). Supporting controversial CSCL discussions with augmented group awareness tools. International Journal of Computer-Supported Collaborative Learning, 3(2), 123–139.  https://doi.org/10.1007/s11412-008-9037-5.CrossRefGoogle Scholar
  10. Butler, D. L., & Winne, P. H. (1995). Feedback and self-regulated learning: A theoretical synthesis. Review of Educational Research, 65(3), 245–281.CrossRefGoogle Scholar
  11. Butterfield, B., & Metcalfe, J. (2001). Errors committed with high confidence are hypercorrected. Journal of Experimental Psychology. Learning, Memory & Cognition, 27(6), 1491–1494.  https://doi.org/10.1037/0278-7393.27.6.1491.CrossRefGoogle Scholar
  12. Butterfield, B., & Metcalfe, J. (2006). The correction of errors committed with high confidence. Metacognition and Learning, 1(1), 69–84.  https://doi.org/10.1007/s11409-006-6894-z.CrossRefGoogle Scholar
  13. Clark, H. H., & Brennan, S. E. (1991). Grounding in communication. In L. B. Resnick, J. M. Levine, & S. D. Teasley (Eds.), Perspectives on socially shared cognition (pp. 127–149). Washington, DC: American Psychological Association.CrossRefGoogle Scholar
  14. Clark, H. H., & Murphy, G. L. (1982). Audience design in meaning and reference. Advances in Psychology, 9(C), 287–299.  https://doi.org/10.1016/S0166-4115(09)60059-5.CrossRefGoogle Scholar
  15. Crano, W., & Prislin, R. (2006). Attitudes and persuasion. Annual Review of Psychology, 57, 345–374.  https://doi.org/10.1146/annurev.psych.57.102904.190034.CrossRefGoogle Scholar
  16. Creedon, P. J., Hayes, A. F. (2015). Small sample mediation analysis: How far can you push the bootstrap? Presented at the Annual conference of the Association for Psychological Science, New York, NY, US.Google Scholar
  17. Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297–334.  https://doi.org/10.1007/BF02310555.CrossRefGoogle Scholar
  18. Dehler, J., Bodemer, D., & Buder, J. (2007). Fostering audience design of computer-mediated knowledge communication by knowledge mirroring. In C. Chinn, G. Erkens, & S. Puntambekar (Eds.), Proceedings of the 7th computer supported collaborative learning conference (pp. 168–170). New Brunswick: International Society of the Learning Sciences.Google Scholar
  19. Dehler, J., Bodemer, D., Buder, J., & Hesse, F. W. (2009). Providing group knowledge awareness in computer-supported collaborative learning: Insights into learning mechanisms. Research and Practice in Technology Enhanced Learning, 4(2), 111–132.  https://doi.org/10.1142/S1793206809000660.CrossRefGoogle Scholar
  20. Dehler, J., Bodemer, D., Buder, J., & Hesse, F. W. (2011). Guiding knowledge communication in CSCL via group knowledge awareness. Computers in Human Behavior, 27(3), 1068–1078.  https://doi.org/10.1016/j.chb.2010.05.018.CrossRefGoogle Scholar
  21. Dillenbourg, P., & Bétrancourt, M. (2006). Collaboration load. In J. Elen & R. E. Clark (Eds.), Handling complexity in learning environments: Research and theory (pp. 142–163). Amsterdam: Elsevier.Google Scholar
  22. Dillenbourg, P., & Evans, M. (2011). Interactive tabletops in education. International Journal of Computer-Supported Collaborative Learning, 6(4), 491–514.  https://doi.org/10.1007/s11412-011-9127-7.CrossRefGoogle Scholar
  23. Dillenbourg, P., & Hong, F. (2008). The mechanics of CSCL macro scripts. International Journal of Computer-Supported Collaborative Learning, 3(1), 5–23.  https://doi.org/10.1007/s11412-007-9033-1.CrossRefGoogle Scholar
  24. Dillenbourg, P., Järvelä, S., & Fischer, F. (2009). The evolution of research on computer-supported collaborative learning. In N. Balacheff, S. Ludvigsen, T. de Jong, A. Lazonder, & S. Barnes (Eds.), Technology-enhanced learning: Principles and products (pp. 3–19). Dordrecht: Springer.CrossRefGoogle Scholar
  25. Dinsmore, D. L., Alexander, P. A., & Loughlin, S. M. (2008). Focusing the conceptual lens on metacognition, self-regulation, and self-regulated learning. Educational Psychology Review, 20(4), 391–409.  https://doi.org/10.1007/s10648-008-9083-6.CrossRefGoogle Scholar
  26. Dinsmore, D. L., & Parkinson, M. M. (2013). What are confidence judgments made of? Students’ explanations for their confidence ratings and what that means for calibration. Learning and Instruction, 24, 4–14.  https://doi.org/10.1016/j.learninstruc.2012.06.001.CrossRefGoogle Scholar
  27. Doise, W., Mugny, G. (1984). Sociocognitive conflict. In M. Argyle (Ed.), The social development of the intellect (Vol. 10, pp. 77–101). Amsterdam: Pergamon. Retrieved from https://www.sciencedirect.com/science/article/pii/B978008030215750010X.
  28. Doise, W., Mugny, G., & Perret-Clermont, A.-N. (1975). Social interaction and the development of cognitive operations. European Journal of Social Psychology, 5(3), 367–383.  https://doi.org/10.1002/ejsp.2420050309.CrossRefGoogle Scholar
  29. Dourish, P., & Bellotti, V. (1992). Awareness and coordination in shared workspaces. In M. Mantel & R. Baecker (Eds.), Proceedings of the 1992 ACM conference on computer-supported cooperative work (pp. 107–114). Toronto: ACM Press.  https://doi.org/10.1145/143457.143468.CrossRefGoogle Scholar
  30. Drachsler, H., & Greller, W. (2012). The pulse of learning analytics understandings and expectations from the stakeholders. In Proceedings of the 2Nd international conference on learning analytics and knowledge (pp. 120–129). New York: ACM.  https://doi.org/10.1145/2330601.2330634.CrossRefGoogle Scholar
  31. Dunlosky, J., & Hertzog, C. (2000). Updating knowledge about encoding strategies: A componential analysis of learning about strategy effectiveness from task experience. Psychology and Aging, 15(3), 462–474.  https://doi.org/10.1037/0882-7974.15.3.462.CrossRefGoogle Scholar
  32. Dunlosky, J., & Metcalfe, J. (2009). Metacognition. Los Angeles: Sage Publications.Google Scholar
  33. Dunlosky, J., & Rawson, K. A. (2012). Overconfidence produces underachievement: Inaccurate self evaluations undermine students’ learning and retention. Learning and Instruction, 22(4), 271–280.  https://doi.org/10.1016/j.learninstruc.2011.08.003.CrossRefGoogle Scholar
  34. Efklides, A., Samara, A., & Petropoulou, M. (1999). Feeling of difficulty: An aspect of monitoring that influences control. European Journal of Psychology of Education, 14(4), 461–476.  https://doi.org/10.1007/BF03172973.CrossRefGoogle Scholar
  35. Engelmann, T., Dehler, J., Bodemer, D., & Buder, J. (2009). Knowledge awareness in CSCL: A psychological perspective. Computers in Human Behavior, 25(4), 949–960.  https://doi.org/10.1016/j.chb.2009.04.004.CrossRefGoogle Scholar
  36. Engelmann, T., & Hesse, F. W. (2010). How digital concept maps about the collaborators’ knowledge and information influence computer-supported collaborative problem solving. International Journal of Computer-Supported Collaborative Learning, 5(3), 299–319.  https://doi.org/10.1007/s11412-010-9089-1.CrossRefGoogle Scholar
  37. Engelmann, T., & Hesse, F. W. (2011). Fostering sharing of unshared knowledge by having access to the collaborators’ meta-knowledge structures. Computers in Human Behavior, 27, 2078–2087.  https://doi.org/10.1016/j.chb.2011.06.002.CrossRefGoogle Scholar
  38. Erkens, G., Jaspers, J., Prangsma, M., & Kanselaar, G. (2005). Coordination processes in computer supported collaborative writing. Computers in Human Behavior, 21(3), 463–486.  https://doi.org/10.1016/j.chb.2004.10.038.CrossRefGoogle Scholar
  39. Erkens, M., Bodemer, D., & Hoppe, H. U. (2016). Improving collaborative learning in the classroom: Design and evaluation of a text mining based grouping and representing. International Journal of Computer-Supported Collaborative Learning, 11(4), 387–415.  https://doi.org/10.1007/s11412-016-9243-5.CrossRefGoogle Scholar
  40. Fazio, L. K., & Marsh, E. J. (2009). Surprising feedback improves later memory. Psychonomic Bulletin & Review, 16(1), 88–92.  https://doi.org/10.3758/PBR.16.1.88.CrossRefGoogle Scholar
  41. Field, A. P., & Wilcox, R. R. (2017). Robust statistical methods: A primer for clinical psychology and experimental psychopathology researchers. Behaviour Research and Therapy, 98, 19–38.  https://doi.org/10.1016/j.brat.2017.05.013.CrossRefGoogle Scholar
  42. 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.  https://doi.org/10.1080/00461520.2012.748005.CrossRefGoogle Scholar
  43. Flavell, J. H. (1979). Metacognition and cognitive monitoring: A new area of cognitive-developmental inquiry. American Psychologist, 34(10), 906–911.  https://doi.org/10.1037/0003-066X.34.10.906.CrossRefGoogle Scholar
  44. Fransen, J., Kirschner, P. A., & Erkens, G. (2011). Mediating team effectiveness in the context of collaborative learning: The importance of team and task awareness. Computers in Human Behavior, 27(3), 1103–1113.  https://doi.org/10.1016/j.chb.2010.05.017.CrossRefGoogle Scholar
  45. Fraundorf, S. H., & Benjamin, A. S. (2016). Conflict and metacognitive control: The mismatch-monitoring hypothesis of how others’ knowledge states affect recall. Memory, 24(8), 1108–1122.  https://doi.org/10.1080/09658211.2015.1069853.CrossRefGoogle Scholar
  46. Friedrich, S., Konietschke, F., Pauly, M. (2017). MANOVA.RM: Analysis of multivariate data and repeated measures designs (Version 0.2.1).Google Scholar
  47. Fritz, M. S., Taylor, A. B., & MacKinnon, D. P. (2012). Explanation of two anomalous results in statistical mediation analysis. Multivariate Behavioral Research, 47(1), 61–87.  https://doi.org/10.1080/00273171.2012.640596.CrossRefGoogle Scholar
  48. Gijlers, H. (2005). Confrontation and co-construction: Exploring and supporting collaborative scientific discovery learning with computer simulations (Doctoral dissertation). University of Twente, Enschede. Retrieved from http://doc.utwente.nl/50896/.
  49. Gijlers, H., Saab, N., van Joolingen, W. R., de Jong, T., & van Hout-Wolters, B. H. A. M. (2009). Interaction between tool and talk: How instruction and tools support consensus building in collaborative inquiry-learning environments. Journal of Computer Assisted Learning, 25(3), 252–267.  https://doi.org/10.1111/j.1365-2729.2008.00302.x.CrossRefGoogle Scholar
  50. Greller, W., & Drachsler, H. (2012). Translating learning into numbers: A generic framework for learning analytics. Journal of Educational Technology & Society, 15(3), 42–57.Google Scholar
  51. Hacker, D. J., Dunlosky, J., & Graesser, A. C. (2009). A growing sense of agency. In D. J. Hacker, J. Dunlosky, & A. C. Graesser (Eds.), Handbook of metacognition in education (pp. 1–4). New York: Routledge.CrossRefGoogle Scholar
  52. Hancock, T. E., Stock, W. A., & Kulhavy, R. W. (1992). Predicting feedback effects from response-certitude estimates. Bulletin of the Psychonomic Society, 30(2), 173–176.  https://doi.org/10.3758/BF03330431.CrossRefGoogle Scholar
  53. Hayes, A. F. (2013). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. New York: The Guilford Press.Google Scholar
  54. Hayes, A. F., & Cai, L. (2007). Using heteroskedasticity-consistent standard error estimators in OLS regression: An introduction and software implementation. Behavior Research Methods, 39(4), 709–722.  https://doi.org/10.3758/BF03192961.CrossRefGoogle Scholar
  55. Hayes, A. F., & Preacher, K. J. (2014). Statistical mediation analysis with a multicategorical independent variable. British Journal of Mathematical and Statistical Psychology, 67(3), 451–470.  https://doi.org/10.1111/bmsp.12028.CrossRefGoogle Scholar
  56. Heimbuch, S., & Bodemer, D. (2018). Interaction of guidance types and the need for cognitive closure in wiki-based learning. PeerJ, 6, e5541.  https://doi.org/10.7717/peerj.5541.CrossRefGoogle Scholar
  57. Hesse, F. (2007). Being told to do something or just being aware of something? An alternative approach to scripting in CSCL. In F. Fischer, I. Kollar, H. Mandl, & J. M. Haake (Eds.), Scripting computer-supported communication of knowledge – Cognitive, computational and educational perspectives (pp. 91–98). New York: Springer.Google Scholar
  58. Higgins, S., Mercier, E., Burd, E., & Hatch, A. (2011). Multi-touch tables and the relationship with collaborative classroom pedagogies: A synthetic review. International Journal of Computer-Supported Collaborative Learning, 6(4), 515–538.  https://doi.org/10.1007/s11412-011-9131-y.CrossRefGoogle Scholar
  59. Hines, J. C., Touron, D. R., & Hertzog, C. (2009). Metacognitive influences on study time allocation in an associative recognition task: An analysis of adult age differences. Psychology and Aging, 24(2), 462–475.  https://doi.org/10.1037/a0014417.CrossRefGoogle Scholar
  60. Hunt, D. P. (2003). The concept of knowledge and how to measure it. Journal of Intellectual Capital, 4(1), 100–113.  https://doi.org/10.1108/14691930310455414.CrossRefGoogle Scholar
  61. Hurme, T.-R., Palonen, T., & Järvelä, S. (2006). Metacognition in joint discussions: An analysis of the patterns of interaction and the metacognitive content of the networked discussions in mathematics. Metacognition and Learning, 1(2), 181–200.  https://doi.org/10.1007/s11409-006-9792-5.CrossRefGoogle Scholar
  62. 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(3), 379–393.  https://doi.org/10.1016/j.learninstruc.2010.05.002.CrossRefGoogle Scholar
  63. Janssen, J., & Bodemer, D. (2013). Coordinated computer-supported collaborative learning: Awareness and awareness tools. Educational Psychologist, 48(1), 40–55.  https://doi.org/10.1080/00461520.2012.749153.CrossRefGoogle Scholar
  64. Janssen, J., Erkens, G., & Kanselaar, G. (2007). Visualization of agreement and discussion processes during computer-supported collaborative learning. Computers in Human Behavior, 23(3), 1105–1125.  https://doi.org/10.1016/j.chb.2006.10.005.CrossRefGoogle Scholar
  65. Janssen, J., Erkens, G., & Kirschner, P. A. (2011a). Group awareness tools: It’s what you do with it that matters., 27(3). doi:  https://doi.org/10.1016/j.chb.2010.06.002
  66. Janssen, J., Erkens, G., Kirschner, P. A., & Kanselaar, G. (2011b). Multilevel analysis in CSCL research. In S. Puntambekar, G. Erkens, & C. Hmelo-Silver (Eds.), Analyzing interactions in CSCL (pp. 187–205). Boston: Springer US.  https://doi.org/10.1007/978-1-4419-7710-6_9.CrossRefGoogle Scholar
  67. Järvelä, S., & Hadwin, A. F. (2013). New frontiers: Regulating learning in CSCL. Educational Psychologist, 48(1), 25–39.  https://doi.org/10.1080/00461520.2012.748006.CrossRefGoogle Scholar
  68. Järvelä, S., Kirschner, P. A., Hadwin, A., Järvenoja, H., Malmberg, J., Miller, M., & Laru, J. (2016). Socially shared regulation of learning in CSCL: Understanding and prompting individual- and group-level shared regulatory activities. International Journal of Computer-Supported Collaborative Learning, 11(3), 263–280.  https://doi.org/10.1007/s11412-016-9238-2.CrossRefGoogle Scholar
  69. Järvelä, S., Kirschner, P. A., Panadero, E., Malmberg, J., Phielix, C., Jaspers, J., et al. (2015). Enhancing socially shared regulation in collaborative learning groups: Designing for CSCL regulation tools. Educational Technology Research and Development, 63(1), 125–142.  https://doi.org/10.1007/s11423-014-9358-1.CrossRefGoogle Scholar
  70. Jarvis, B. G. (2012). MediaLab (Version 2012) [computer software]. New York: Empirisoft Corporation.Google Scholar
  71. Jermann, P., & Dillenbourg, P. (2003). Elaborating new arguments through a CSCL script. In J. Andriessen, M. Baker, & D. Suthers (Eds.), Arguing to learn: Confronting cognitions in computer-supported collaborative learning environments (pp. 205–226). Dordrecht: Springer Netherlands.  https://doi.org/10.1007/978-94-017-0781-7_8.CrossRefGoogle Scholar
  72. Johnson, D. W., & Johnson, R. T. (2009a). An educational psychology success story: Social interdependence theory and cooperative learning. Educational Researcher, 38(5), 365–379.  https://doi.org/10.3102/0013189X09339057.CrossRefGoogle Scholar
  73. Johnson, D. W., & Johnson, R. T. (2009b). Energizing learning: The instructional power of conflict. Educational Researcher, 38(1), 37–51.  https://doi.org/10.3102/0013189X08330540.CrossRefGoogle Scholar
  74. Johnson, D. W., Johnson, R. T., & Tjosvold, D. (2000). Constructive controversy: The value of intellectual opposition. In M. Deutsch & P. T. Coleman (Eds.), The handbook of conflict resolution: Theory and practice (pp. 65–85). San Francisco: Jossey-Bass.Google Scholar
  75. Johnson, R., Brooker, C., Stutzman, J., Hultman, D., & Johnson, D. W. (1985). The effects of controversy, concurrence seeking, and individualistic learning on achievement and attitude change. Journal of Research in Science Teaching, 22(3), 197–205.  https://doi.org/10.1002/tea.3660220302.CrossRefGoogle Scholar
  76. Kalyuga, S. (2013). Effects of learner prior knowledge and working memory limitations on multimedia learning. Procedia - Social and Behavioral Sciences, 83, 25–29.  https://doi.org/10.1016/j.sbspro.2013.06.005.CrossRefGoogle Scholar
  77. Kelley, K. (2017). MBESS (Version 4.0.0).Google Scholar
  78. King, A. (1992). Facilitating elaborative learning through guided student-generated questioning. Educational Psychologist, 27(1), 111–126.  https://doi.org/10.1207/s15326985ep2701_8.CrossRefGoogle Scholar
  79. King, A. (2007). Scripting collaborative learning processes: A cognitive perspective. In F. Fischer, I. Kollar, H. Mandl, & J. M. Haake (Eds.), Scripting computer-supported collaboratorive learning: Cognitive, computational and educational perspectives (pp. 13–37). New York: Springer.CrossRefGoogle Scholar
  80. Kirschner, P. A., Sweller, J., Kirschner, F., & Zambrano R., J. (2018). From cognitive load theory to collaborative cognitive load theory. International Journal of Computer-Supported Collaborative Learning, 13(2), 213–233. doi:  https://doi.org/10.1007/s11412-018-9277-y.
  81. Kirschner, P. A., & van Merriënboer, J. J. G. (2013). Do learners really know best? Urban legends in education. Educational Psychologist, 48(3), 169–183.  https://doi.org/10.1080/00461520.2013.804395.CrossRefGoogle Scholar
  82. Kollar, I., Wecker, C., & Fischer, F. (2018). Scaffolding and scripting (computer-supported) collaborative learning. In F. Fischer, C. E. Hmelo-Silver, S. R. Goldman, & P. Reimann (Eds.), International handbook of the learning sciences (pp. 340–350). New York: Routledge/Taylor & Francis.Google Scholar
  83. Koriat, A. (2012). The relationships between monitoring, regulation and performance. Learning and Instruction, 22(4), 296–298.  https://doi.org/10.1016/j.learninstruc.2012.01.002.CrossRefGoogle Scholar
  84. Koriat, A., Adiv, S., & Schwarz, N. (2015). Views that are shared with others are expressed with greater confidence and greater fluency independent of any social influence. Personality and Social Psychology Review, 20(2), 176–193.  https://doi.org/10.1177/1088868315585269.CrossRefGoogle Scholar
  85. Koriat, A., & Levy-Sadot, R. (2001). The combined contributions of the cue-familiarity and accessibility heuristics to feelings of knowing. Journal of Experimental Psychology. Learning, Memory, and Cognition, 27(1), 34–53.  https://doi.org/10.1037//0278-7393.27.1.34.CrossRefGoogle Scholar
  86. Koriat, A., Ma’ayan, H., & Nussinson, R. (2006). The intricate relationships between monitoring and control in metacognition: Lessons for the cause-and-effect relation between subjective experience and behavior. Journal of Experimental Psychology: General, 135(1), 36–69.  https://doi.org/10.1037/0096-3445.135.1.36.CrossRefGoogle Scholar
  87. Kornell, N., & Metcalfe, J. (2006). Study efficacy and the region of proximal learning framework. Journal of Experimental Psychology: Learning, Memory, and Cognition, 32(3), 609–622.  https://doi.org/10.1037/0278-7393.32.3.609.CrossRefGoogle Scholar
  88. Kulhavy, R. W., & Stock, W. A. (1989). Feedback in written instruction: The place of response certitude. Educational Psychology Review, 1(4), 279–308.  https://doi.org/10.1007/BF01320096.CrossRefGoogle Scholar
  89. Kulhavy, R. W., Stock, W. A., Hancock, T. E., Swindell, L. K., & Hammrich, P. L. (1990). Written feedback: Response certitude and durability. Contemporary Educational Psychology, 15(4), 319–332.  https://doi.org/10.1016/0361-476X(90)90028-Y.CrossRefGoogle Scholar
  90. Leclercq, D. (1983). Confidence marking: Its use in testing. Evaluation in Education, 6(2), 161–287.  https://doi.org/10.1016/0191-765X(82)90011-8.CrossRefGoogle Scholar
  91. Leclercq, D. (1993). Validity, reliability, and acuity of self-assessment in educational testing. In D. Leclercq & J. E. Bruno (Eds.), Item banking: Interactive testing and self-assessment (pp. 114–131). Berlin, Germany: Springer. Retrieved from http://link.springer.com/chapter/10.1007/978-3-642-58033-8_11
  92. Leclercq, D., & Poumay, M. (2004). Objective assessment of subjectivity: Degrees of certainty and partial knowledge. In Presented at the 2nd biennial meeting of the EARLI special interest group 16 metacognition. Amsterdam: NL.Google Scholar
  93. Lee, G., & Kwon, J. (2001). What do we know about students‘ cognitive conflict in science classroom: A theoretical model of cognitive conflict process. In P. A. Rubba, J. A. Rye, W. J. Di Biase, & B. A. Crawford (Eds.), Proceedings of the 2001 annual meeting of the Association for the Education of teachers in science (pp. 309–325). Costa Mesa: Association for the Education of Teachers in Science.Google Scholar
  94. Lee, G., Kwon, J., Park, S.-S., Kim, J.-W., Kwon, H.-G., & Park, H.-K. (2003). Development of an instrument for measuring cognitive conflict in secondary-level science classes. Journal of Research in Science Teaching, 40(6), 585–603.  https://doi.org/10.1002/tea.10099.CrossRefGoogle Scholar
  95. Levine, J. M., Resnick, L. B., & Higgins, E. T. (1993). Social foundations of cognition. Annual Review of Psychology, 44(1), 585–612.  https://doi.org/10.1146/annurev.ps.44.020193.003101.CrossRefGoogle Scholar
  96. Lowry, N., & Johnson, D. W. (1981). Effects of controversy on epistemic curiosity, achievement, and attitudes. The Journal of Social Psychology, 115(1), 31–43.  https://doi.org/10.1080/00224545.1981.9711985.CrossRefGoogle Scholar
  97. Maki, R. H. (1998a). Predicting performance on text: Delayed versus immediate predictions and tests. Memory & Cognition, 26(5), 959–964.  https://doi.org/10.3758/BF03201176.CrossRefGoogle Scholar
  98. Maki, R. H. (1998b). Test predictions over text material. In D. J. Hacker, J. Dunlosky, & A. C. Graesser (Eds.), Metacognition in educational theory and practice (pp. 117–144). Mahwah: Lawrence Erlbaum Associates Publishers.Google Scholar
  99. McNeish, D. (2017). Thanks coefficient alpha, we’ll take it from here. Psychological Methods, 32(3).  https://doi.org/10.1037/met0000144.
  100. Metcalfe, J., & Finn, B. (2008). Evidence that judgments of learning are causally related to study choice. Psychonomic Bulletin & Review, 15(1), 174–179.  https://doi.org/10.3758/PBR.15.1.174.CrossRefGoogle Scholar
  101. Metcalfe, J., & Finn, B. (2011). People’s hypercorrection of high-confidence errors: Did they know it all along? Journal of Experimental Psychology: Learning, Memory & Cognition, 37(2), 437–448.  https://doi.org/10.1037/a0021962.CrossRefGoogle Scholar
  102. Metcalfe, J., & Kornell, N. (2005). A region of proximal learning model of study time allocation. Journal of Memory and Language, 52(4), 463–477.  https://doi.org/10.1016/j.jml.2004.12.001.CrossRefGoogle Scholar
  103. Miller, M., & Hadwin, A. (2015). Scripting and awareness tools for regulating collaborative learning: Changing the landscape of support in CSCL. Computers in Human Behavior, 52, 573–588.  https://doi.org/10.1016/j.chb.2015.01.050.CrossRefGoogle Scholar
  104. Mitchum, A. L., Kelley, C. M., & Fox, M. C. (2016). When asking the question changes the ultimate answer: Metamemory judgments change memory. Journal of Experimental Psychology: General, 145(2), 200–219.  https://doi.org/10.1037/a0039923.CrossRefGoogle Scholar
  105. Mugny, G., Butera, F., Sanchez-Mazas, M., & Perez, J. A. (1995). Judgments in conflict: The conflict elaboration theory of social influence. In B. Boothe, R. Hirsig, A. Helminger, & R. Volkart (Eds.), Perception, evaluation, interpretation. Seattle: Hogrefe & Huber.Google Scholar
  106. Mugny, G., & Doise, W. (1978). Socio-cognitive conflict and structure of individual and collective performances. European Journal of Social Psychology, 8(2), 181–192.  https://doi.org/10.1002/ejsp.2420080204.CrossRefGoogle Scholar
  107. Nelson, T. O., Dunlosky, J., Graf, A., & Narens, L. (1994). Utilization of metacognitive judgments in the allocation of study during multitrial learning. Psychological Science, 5(4), 207–213.  https://doi.org/10.1111/j.1467-9280.1994.tb00502.x.CrossRefGoogle Scholar
  108. Nelson, T. O., & Leonesio, J. R. (1988). Allocation of self-paced study time and the “labor-in-vain effect.”. Journal of Experimental Psychology: Learning, Memory, and Cognition, 14(4), 676–686.  https://doi.org/10.1037/0278-7393.14.4.676.CrossRefGoogle Scholar
  109. Nelson, T. O., & Narens, L. (1990). Metamemory: A theoretical framework and new findings. In G. H. Bower (Ed.), Psychology of learning & motivation (Vol. 26, pp. 125–173). New York: Academic Press.Google Scholar
  110. Nickerson, R. S. (1999). How we know—And sometimes misjudge—What others know: Imputing one’s own knowledge to others. Psychological Bulletin, 125(6), 737–759.  https://doi.org/10.1037/0033-2909.125.6.737.CrossRefGoogle Scholar
  111. Nisbett, R. E., & Wilson, T. D. (1977). Telling more than we can know: Verbal reports on mental processes. Psychological Review, 84(3), 231–259.  https://doi.org/10.1037/0033-295X.84.3.231.CrossRefGoogle Scholar
  112. Nova, N., Wehrle, T., Goslin, J., Bourquin, Y., & Dillenbourg, P. (2007). Collaboration in a multi-user game: Impacts of an awareness tool on mutual modeling. Multimedia Tools and Applications, 32(2), 161–183.  https://doi.org/10.1007/s11042-006-0065-8.CrossRefGoogle Scholar
  113. Panadero, E., & Järvelä, S. (2015). Socially shared regulation of learning: A review. European Psychologist, 20(3), 190–203.  https://doi.org/10.1027/1016-9040/a000226.CrossRefGoogle Scholar
  114. Price, P. C., & Stone, E. R. (2004). Intuitive evaluation of likelihood judgment producers: Evidence for a confidence heuristic. Journal of Behavioral Decision Making, 17(1), 39–57.  https://doi.org/10.1002/bdm.460.CrossRefGoogle Scholar
  115. Rosenshine, B., Meister, C., & Chapman, S. (1996). Teaching students to generate questions: A review of the intervention studies. Review of Educational Research, 66(2), 181–221.  https://doi.org/10.3102/00346543066002181.CrossRefGoogle Scholar
  116. Sangin, M., Molinari, G., Nüssli, M.-A., & Dillenbourg, P. (2008). Knowing what the peer knows: The differential effect of knowledge awareness on collaborative learning performance of asymmetric pairs. In P. Dillenbourg & M. Specht (Eds.), Times of convergence. Technologies across learning contexts (pp. 384–394). Berlin, Germany: Springer Berlin Heidelberg. Retrieved from http://link.springer.com/chapter/10.1007/978-3-540-87605-2_43
  117. Sangin, M., Molinari, G., Nüssli, M.-A., & Dillenbourg, P. (2011). Facilitating peer knowledge modeling: Effects of a knowledge awareness tool on collaborative learning outcomes and processes. Computers in Human Behavior, 27(3), 1059–1067.  https://doi.org/10.1016/j.chb.2010.05.032.CrossRefGoogle Scholar
  118. Schnaubert, L., & Bodemer, D. (2016). How socio-cognitive information affects individual study decisions. In C.-K. Looi, J. Polman, U. Cress, & P. Reimann (Eds.), Transforming learning, empowering learners: The international conference of the learning sciences (ICLS) 2016 (pp. 274–281). Singapore: International Society of the Learning Sciences.Google Scholar
  119. Schnaubert, L., & Bodemer, D. (2017). Prompting and visualising monitoring outcomes: Guiding self-regulatory processes with confidence judgments. Learning and Instruction, 49, 251–262.  https://doi.org/10.1016/j.learninstruc.2017.03.004.CrossRefGoogle Scholar
  120. Schnaubert, L., & Bodemer, D. (2018). What interdependence can tell us about collaborative learning: A statistical and psychological perspective. Research and Practice in Technology Enhanced Learning, 13(1), 1–18.  https://doi.org/10.1186/s41039-018-0084-x.CrossRefGoogle Scholar
  121. Scholvien, A., Bodemer, D. (2013). Information cueing in collaborative multimedia learning. In N. Rummel, M. Kapur, M. Nathan, & S. Puntambekar (Eds.), To See the World and a Grain of Sand: Learning across Levels of Space, Time, and Scale: CSCL 2013 Conference Proceedings Volume 2 — Short Papers, Panels, Posters, Demos & Community Events (Vol. 2, pp. 149–152). Madison, WI.Google Scholar
  122. Schraw, G. (2009). A conceptual analysis of five measures of metacognitive monitoring. Metacognition and Learning, 4(1), 33–45.  https://doi.org/10.1007/s11409-008-9031-3.CrossRefGoogle Scholar
  123. Schraw, G., Kuch, F., & Gutierrez, A. P. (2013). Measure for measure: Calibrating ten commonly used calibration scores. Learning and Instruction, 24, 48–57.  https://doi.org/10.1016/j.learninstruc.2012.08.007.CrossRefGoogle Scholar
  124. Shrout, P. E., & Fleiss, J. L. (1979). Intraclass correlations: Uses in assessing rater reliability. Psychological Bulletin, 86(2), 420–428.  https://doi.org/10.1037/0033-2909.86.2.420.CrossRefGoogle Scholar
  125. Soderstrom, N. C., Clark, C. T., Halamish, V., & Bjork, E. L. (2015). Judgments of learning as memory modifiers. Journal of Experimental Psychology: Learning, Memory, and Cognition, 41(2), 553–558.  https://doi.org/10.1037/a0038388.CrossRefGoogle Scholar
  126. Soller, A., Martínez, A., Jermann, P., & Muehlenbrock, M. (2005). From mirroring to guiding: A review of state of the art technology for supporting collaborative learning. Int. J. Artif. Intell. Ed., 15(4), 261–290.Google Scholar
  127. Son, L. K., & Metcalfe, J. (2000). Metacognitive and control strategies in study-time allocation. Journal of Experimental Psychology. Learning, Memory, and Cognition, 26(1), 204–221.  https://doi.org/10.1037/0278-7393.1.204.CrossRefGoogle Scholar
  128. Stegmann, K., Wecker, C., Weinberger, A., & Fischer, F. (2012). Collaborative argumentation and cognitive elaboration in a computer-supported collaborative learning environment. Instructional Science, 40(2), 297–323.  https://doi.org/10.1007/s11251-011-9174-5.CrossRefGoogle Scholar
  129. Stegmann, K., Weinberger, A., & Fischer, F. (2007). Facilitating argumentative knowledge construction with computer-supported collaboration scripts. International Journal of Computer-Supported Collaborative Learning, 2(4), 421–447.  https://doi.org/10.1007/s11412-007-9028-y.CrossRefGoogle Scholar
  130. Suthers, D. D. (2001). Towards a systematic study of representational guidance for collaborative learning discourse. Journal of Universal Computer Science, 7, 254–277.Google Scholar
  131. Suthers, D. D., & Hundhausen, C. D. (2003). An experimental study of the effects of representational guidance on collaborative learning processes. Journal of the Learning Sciences, 12(2), 183–218.  https://doi.org/10.1207/S15327809JLS1202_2.CrossRefGoogle Scholar
  132. Sweller, J. (1994). Cognitive load theory, learning difficulty, and instructional design. Learning and Instruction, 4(4), 295–312.  https://doi.org/10.1016/0959-4752(94)90003-5.CrossRefGoogle Scholar
  133. Sweller, J., van Merrienboer, J. J. G., & Paas, F. G. W. C. (1998). Cognitive architecture and instructional design. Educational Psychology Review, 10(3), 251–296.  https://doi.org/10.1023/A:1022193728205.CrossRefGoogle Scholar
  134. Tenney, E. R., Small, J. E., Kondrad, R. L., Jaswal, V. K., & Spellman, B. A. (2011). Accuracy, confidence, and calibration: How young children and adults assess credibility. Developmental Psychology, 47(4), 1065–1077.  https://doi.org/10.1037/a0023273.CrossRefGoogle Scholar
  135. Thiede, K. W. (1999). The importance of monitoring and self-regulation during multitrial learning. Psychonomic Bulletin & Review, 6(4), 662–667.  https://doi.org/10.3758/BF03212976.CrossRefGoogle Scholar
  136. Thiede, K. W., Anderson, M. C. M., & Therriault, D. (2003). Accuracy of metacognitive monitoring affects learning of texts. Journal of Educational Psychology, 95(1), 66–73.  https://doi.org/10.1037/0022-0663.95.1.66.CrossRefGoogle Scholar
  137. Thiede, K. W., & Dunlosky, J. (1999). Toward a general model of self-regulated study: An analysis of selection of items for study and self-paced study time. Journal of Experimental Psychology: Learning, Memory, and Cognition, 25(4), 1024–1037.  https://doi.org/10.1037/0278-7393.25.4.1024.CrossRefGoogle Scholar
  138. Valcke, M. (2002). Cognitive load: Updating the theory? Learning and Instruction, 12(1), 147–154.  https://doi.org/10.1016/S0959-4752(01)00022-6.CrossRefGoogle Scholar
  139. Vernon, D., & Usher, M. (2003). Dynamics of metacognitive judgements: Pre- and post retrieval mechanisms. Journal of Experimental Psychology, Learning, Memory and Cognition, 29(3), 339–346.  https://doi.org/10.1037/0278-7393.29.3.339.CrossRefGoogle Scholar
  140. Weinberger, A., Ertl, B., Fischer, F., & Mandl, H. (2005). Epistemic and social scripts in computer-supported collaborative learning. Instructional Science, 33(1), 1–30.  https://doi.org/10.1007/s11251-004-2322-4.CrossRefGoogle Scholar
  141. Weinberger, A., & Fischer, F. (2006). A framework to analyze argumentative knowledge construction in computer-supported collaborative learning. Computers & Education, 46(1), 71–95.  https://doi.org/10.1016/j.compedu.2005.04.003.CrossRefGoogle Scholar
  142. Wilcox, R. (2012). Introduction to robust estimation and hypothesis testing (3rd ed.). Boston: Academic Press.  https://doi.org/10.1016/B978-0-12-386983-8.00020-2.CrossRefGoogle Scholar
  143. Winne, P. H., & Hadwin, A. F. (1998). Studying as self-regulated learning. In D. J. Hacker, J. Dunlosky, & A. C. Graesser (Eds.), Metacognition in educational theory and practice (pp. 277–304). Mahwah: Lawrence Erlbaum.Google Scholar
  144. Wise, A. F., & Schwarz, B. B. (2017). Visions of CSCL: Eight provocations for the future of the field. International Journal of Computer-Supported Collaborative Learning, 12(4), 423–467.  https://doi.org/10.1007/s11412-017-9267-5.CrossRefGoogle Scholar
  145. Yates, J. F., Price, P. C., Lee, J.-W., & Ramirez, J. (1996). Good probabilistic forecasters: The ‘consumer’s’ perspective. International Journal of Forecasting, 12(1), 41–56.  https://doi.org/10.1016/0169-2070(95)00636-2.CrossRefGoogle Scholar

Copyright information

© International Society of the Learning Sciences, Inc. 2019

Authors and Affiliations

  1. 1.University of Duisburg-EssenDuisburgGermany

Personalised recommendations