Skip to main content

Providing different types of group awareness information to guide collaborative learning

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.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

References

  • Apache Software Foundation. (2013). Apache CouchDB [computer program]. Wakefield: Apache Software Foundation.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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 

  • 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 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • Butler, D. L., & Winne, P. H. (1995). Feedback and self-regulated learning: A theoretical synthesis. Review of Educational Research, 65(3), 245–281.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

  • Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297–334. https://doi.org/10.1007/BF02310555.

    Google Scholar 

  • 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 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • Dunlosky, J., & Metcalfe, J. (2009). Metacognition. Los Angeles: Sage Publications.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    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. https://doi.org/10.1080/00461520.2012.748005.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • Friedrich, S., Konietschke, F., Pauly, M. (2017). MANOVA.RM: Analysis of multivariate data and repeated measures designs (Version 0.2.1).

  • 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.

    Google Scholar 

  • 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/.

  • 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.

    Google Scholar 

  • 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 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • Hayes, A. F. (2013). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. New York: The Guilford Press.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • Jarvis, B. G. (2012). MediaLab (Version 2012) [computer software]. New York: Empirisoft Corporation.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • Kelley, K. (2017). MBESS (Version 4.0.0).

  • King, A. (1992). Facilitating elaborative learning through guided student-generated questioning. Educational Psychologist, 27(1), 111–126. https://doi.org/10.1207/s15326985ep2701_8.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

  • 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.

    Google Scholar 

  • 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 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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

  • 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 

  • 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 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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 

  • McNeish, D. (2017). Thanks coefficient alpha, we’ll take it from here. Psychological Methods, 32(3). https://doi.org/10.1037/met0000144.

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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

  • 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.

    Google Scholar 

  • 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 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

Download references

Acknowledgements

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

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Lenka Schnaubert.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendices

Appendices

Appendix 1

figure afigure afigure a

Appendix 2

figure bfigure bfigure b

Appendix 3

factors (type) F(2, 125) p ηp2
main effects
time (within) 179.70 < .001 .74
metacognitive GA information (between) 1.57 .211 .03
cognitive GA information (between) 0.03 .974 < .01
first order interactions
metacognitive * cognitive GA information 0.36 .700 < .01
time * metacognitive GA information 1.75 .177 .03
time * cognitive GA information 1.15 .320 .02
second order interaction
time * metacognitive * cognitive GA information 1.95 .147 .03

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Schnaubert, L., Bodemer, D. Providing different types of group awareness information to guide collaborative learning. Intern. J. Comput.-Support. Collab. Learn 14, 7–51 (2019). https://doi.org/10.1007/s11412-018-9293-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11412-018-9293-y

Keywords

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