The effects of collaborative learning and informing students about the dangers of overconfidence on metacognitive judgments and conceptual learning were examined in two classroom studies. In the first study, the conceptual knowledge of operant conditioning and the confidence judgments of 287 graduate students enrolled in a teacher education programme were assessed at the beginning of the educational psychology course and following instruction that included student work on examples of operant conditioning concepts, either individually or in small groups. Students’ recognition of the concepts in examples and explanations of their answers were collected during learning along with ratings of their confidence in their answers. Students in the collaborative learning condition showed higher confidence in their answers on both tasks, but they also showed higher bias in their judgments on the explanation task. They also displayed better recognition of the concepts and discrimination between accurate and inaccurate recognition. The second study aimed to examine the effect of a more structured collaborative learning condition and the effect of informing students about the dangers of overconfident judgments on students’ confidence in the accuracy of their answers on the same tasks as in the first study and on their performance. The participants were 223 students enrolled in the teacher education programme. A strong positive effect of collaboration on discrimination and performance on both tasks was obtained. Furthermore, the students in the collaborative learning condition showed lower bias in the explanation task. Informing students about the dangers of overconfidence did not have beneficial effects.
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Arntzen, E., Lokke, J., Lokke, G., & Eilertsen, D. E. (2010). On misconceptions about behavior analysis among university students and teachers. The Psychological Record, 60(2), 325–336. https://doi.org/10.1007/BF03395710
Belge, C. H., & Boz, Y. (2016). Structuring cooperative learning for motivation and conceptual change in the concepts of mixtures. International Journal of Science and Mathematics Education, 14, 635–657. https://doi.org/10.1007/s10763-014-9602-5
Bensley, D. A., & Lilienfeld, S. O. (2017). Psychological misconceptions: Recent scientific advances and unresolved issues. Current Directions in Psychological Science, 26(4), 377–382. https://doi.org/10.1177/0963721417699026
Berland, L. K., & Lee, V. R. (2012). In pursuit of consensus: Disagreement and legitimization during small-group argumentation. International Journal of Science Education, 34(12), 1857–1882. https://doi.org/10.1080/09500693.2011.645086
Berthold, K., & Renkl, A. (2010). How to foster active processing of explanations in instructional communication. Educational Psychology Review, 22(1), 25–40. https://doi.org/10.1007/s10648-010-9124-9
Bol, L., Hacker, D. J., Walck, C. C., & Nunnery, J. A. (2012). The effects of individual or group guidelines on the calibration accuracy and achievement of high school biology students. Contemporary Educational Psychology, 37(4), 280–287. https://doi.org/10.1016/j.cedpsych.2012.02.004
Callender, A. A., Franco-Watkins, A. M., & Roberts, A. S. (2016). Improving metacognition in the classroom through instruction, training, and feedback. Metacognition and Learning, 11, 215–235. https://doi.org/10.1007/s11409-015-9142-6
Caravita, S., & Halldén, O. (1994). Re-framing the problem of conceptual change. Learning and Instruction, 4(1), 89–111. https://doi.org/10.1016/0959-4752(94)90020-5
Carpenter, S., Endres, T., & Hui, L. (2020). Students’ use of retrieval in self-regulated learning: Implications for monitoring and regulating effortful learning experiences. Educational Psychology Review, 32, 1029–1054. https://doi.org/10.1007/s10648-020-09562-w
Chiu, J. L., & Chi, M. T. (2014). Supporting self-explanation in the classroom. In V. A. Benassi, C. E. Overson & C. M. Hakala (Eds.), Applying science of learning in education: Infusing psychological science into the curriculum (pp. 91–103). Society for the Teaching of Psychology. Retrieved from http://teachpsych.org/ebooks/asle2014/index.php
Cohen, J. (2009). Statistical power analysis for the behavioral sciences (2nd ed.). Taylor and Francis.
De Backer, L., Van Keer, H., & Valcke, M. (2020). Variations in socially shared metacognitive regulation and their relation with university students’ performance. Metacognition and Learning, 15, 233–259. https://doi.org/10.1007/s11409-020-09229-5
de Bruin, A. B. H., & van Gog, T. (2012). Improving self-monitoring and self-regulation: From cognitive psychology to the classroom. Learning and Instruction, 22, 245–252. https://doi.org/10.1016/j.learninstruc.2012.01.003
de Bruin, A. B., Roelle, J., Carpenter, S. K., Baars, M., & EFG-MRE, . (2020). Synthesizing cognitive load and self-regulation theory: A theoretical framework and research agenda. Educational Psychology Review, 32, 1–13. https://doi.org/10.1007/s10648-020-09576-4
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
diSessa, A. A. (2006). A history of conceptual change research: Threads and fault lines. In R. K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (pp. 265– 281). Cambridge University Press. https://doi.org/10.1017/CBO9780511816833.017
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
Dunlosky, J., Rawson, K. A., & Middleton, E. L. (2005). What constrains the accuracy of metacomprehension judgments? Testing the transfer-appropriate-monitoring and accessibility hypotheses. Journal of Memory and Language, 52(4), 551–565. https://doi.org/10.1016/j.jml.2005.01.011
Durning, S. J., Dong, T., Artino, A. R., van der Vleuten, C., Holmboe, E., & Schuwirth, L. (2015). Dual processing theory and expertsʼ reasoning: Exploring thinking on national multiple-choice questions. Perspectives on Medical Education, 4(4), 168–175. https://doi.org/10.1007/s40037-015-0196-6
Erkens, G., Kanselaar, G., Prangsma, M., & Jaspers, J. (2003). Computer support for collaborative and argumentative writing. In de E. Corte, L. Verschaffel, N. Entwistle, & J. Van Merriënboer (Eds.). Powerful learning environments: Unravelling basic components and dimension (pp. 159–177). Pergamon/Elsevier Science Ltd.
Faul, F., Erdfelder, E., Lang, A. G., & Buchner, A. (2007). G* Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39(2), 175–191. https://doi.org/10.3758/BF03193146
Foster, N. L., Was, C. A., Dunlosky, J., & Isaacson, R. M. (2017). Even after thirteen class exams, students are still overconfident: The role of memory for past exam performance in student predictions. Metacognition & Learning, 12, 1–19. https://doi.org/10.1007/s11409-016-9158-6
García-Rodicio, H., & Sánchez, E. (2014). Does the detection of misunderstanding lead to its revision? Metacognition & Learning, 9, 265–286. https://doi.org/10.1007/s11409-014-9116-0
Hacker, D. J., Bol, L., Horgan, D. D., & Rakow, E. A. (2000). Test prediction and performance in a classroom context. Journal of Educational Psychology, 92(1), 160–170. https://doi.org/10.1037/0022-06126.96.36.199
Hadwin, A. F., Oshige, M., Gress, C. L. Z., & Winne, P. H. (2010). Innovative ways for using gStudy to orchestrate and research social aspects of self-regulated learning. Computers in Human Behavior, 26, 794–805. https://doi.org/10.1016/j.chb.2007.06.007
Hammer, D. (1996). Misconceptions or p-prims: How may alternative perspectives of cognitive structure influence instructional perceptions and intentions. The Journal of the Learning Sciences, 5(2), 97–127. https://doi.org/10.1207/s15327809jls0502_1
Händel, M., Harder, B., & Dresel, M. (2020). Enhanced monitoring accuracy and test performance: Incremental effects of judgment training over and above repeated testing. Learning and Instruction, 65, 101–245. https://doi.org/10.1016/j.learninstruc.2019.101245
Hatano, G., & Inagaki, K. (2003). When is conceptual change intended? A cognitive- sociocultural view. In G. M. Sinatra & P. R. Pintrich (Eds.), Intentional conceptual change (pp. 407–427). Lawrence Erlbaum Associates.
Hattie, J. (2013). Calibration and confidence: Where to next? Learning and Instruction, 24, 62–66. https://doi.org/10.1016/j.learninstruc.2012.05.009
Hausmann, R. G. M., & VanLehn, K. (2007). Explaining self-explaining: A contrast between content and generation. In R. Luckin, K. R. Koedinger & J. Greer (Eds.), Artificial intelligence in education: Building technology rich learning contexts that work (Vol. 158, pp. 417–424). IOS Press.
Järvenoja, H., Näykki, P., & Törmänen, T. (2019). Emotional regulation in collaborative learning: When do higher education students activate group level regulation in the face of challenges? Studies in Higher Education, 44(10), 1747–1757. https://doi.org/10.1080/03075079.2019.1665318
Kahneman, D., & Frederick, S. (2005). A model of heuristic judgment. In K. Holyoak, & R. G. Morrison (Eds.), The Cambridge handbook of thinking and reasoning pp. 267–294. Cambridge University Press.
Kalyuga, S. (2011). Cognitive load theory: How many types of load does it really need? Educational Psychology Review, 23(1), 1–19. https://doi.org/10.1007/s10648-010-9150-7
Keren, G. (1991). Calibration and probability judgments: Conceptual and methodological issues. Acta Psychologica, 77(3), 217–273. https://doi.org/10.1016/0001-6918(91)90036-Y
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(3), 287–307. https://doi.org/10.1007/s11409-014-9117-z
Kruger, J., & Dunning, D. (1999). Unskilled and unaware of it: How difficulties in recognizing one’s own incompetence lead to inflated self-assessments. Journal of Personality and Social Psychology, 77(6), 1121–1134. https://doi.org/10.1037/0022-35188.8.131.521
Lamal, P. A. (1995). College students’ misconceptions about behavior analysis. Teaching of Psychology, 22(3), 177–180. https://doi.org/10.1207/s15328023top2203_3
Leach, J. T., & Scott, P. H. (2009). Teaching for conceptual understanding: An approach drawing on individual and sociocultural perspectives. In S. Vosniadou (Ed.) International handbook of research on conceptual change (pp. 675–703). Routledge.
Miyake, N. (2013). Conceptual change through collaboration. In S. Vosniadou (Ed.), International handbook of research on conceptual change (pp. 453–478). Routledge. https://doi.org/10.4324/9780203154472-34
Murphy, E. S., & Lupfer, G. J. (2014). Basic principles of operant conditioning. In F. K. McSweeney & E. S. Murphy (Eds.), The Wiley Blackwell handbook of operant and classical conditioning (pp. 167–194). Wiley-Blackwell. https://doi.org/10.1002/9781118468135.ch8
Nelson, T. O. (1999). Cognition versus metacognition. In R. J. Sternberg (Ed.), The nature of cognition (pp. 625–641). MIT Press.
Nelson, T. O., & Narens, L. (1990). Metamemory: A theoretical framework and new findings. Psychology of Learning and Motivation, 26, 125–141. https://doi.org/10.1016/S0079-7421(08)60053-5
Nelson, T. O., & Dunlosky, J. (1991). When people’s judgments of learning (JOLs) are extremely accurate at predicting subsequent recall: The “delayed-JOL effect.” Psychological Science, 2, 267–270. https://doi.org/10.1111/j.1467-9280.1991.tb00147.x
Nokes, T. J., Hausmann, R. G. M., VanLehn, K., & Gershman, S. (2011). Testing the instructional fit hypothesis: The case of self-explanation prompts. Instructional Science, 39(5), 645–666. https://doi.org/10.1007/s11251-010-9151-4
Osborne, J. (2010). Arguing to learn in science: The role of collaborative, critical discourse. Science, 328(5977), 463–466. https://doi.org/10.1126/science.1183944
Osterhage, J. L., Usher, E. L., Douin, T. A., & Bailey, W. M. (2019). Opportunities for self-evaluation increase student calibration in an introductory biology course. CBE—Life Sciences Education, 18(2), ar16, 1-ar210. https://doi.org/10.1187/cbe.18-10-0202
Paulewicz, B., Siedlecka, M., & Koculak, M. (2020). Confounding in studies on metacognition: A preliminary causal analysis framework. Frontiers in Psychology, 11, 1933. https://doi.org/10.3389/fpsyg.2020.01933
Pena-Shaff, J. B., & Nicholls, C. (2004). Analyzing student interactions and meaning construction in computer bulletin board discussions. Computers & Education, 42(3), 243–265. https://doi.org/10.1016/j.compedu.2003.08.003
Perry, N. E., & Rahim, A. (2011). Studying self-regulated learning in classrooms. In B. J. Zimmerman & D. H. Schunk (Eds.), Handbook of Self-regulation of Learning and Performance (pp. 122–136). Routledge.
Posner, G. J., Strike, K. A., Hewson, P. W., & Gertzog, W. A. (1982). Accommodation of a scientific conception: Toward a theory of conceptual change. Science Education, 66(2), 211–227. https://doi.org/10.1002/sce.3730660207
Prinz, A., Golke, S., & Wittwer, J. (2020). To what extent do situation-model-approach interventions improve relative metacomprehension accuracy? Meta-analytic insights. Educational Psychology Review, 32, 917–949. https://doi.org/10.1007/s10648-020-09558-6
Rawson, K. A., Thomas, R. C., & Jacoby, L. L. (2015). The power of examples: Illustrative examples enhance conceptual learning of declarative concepts. Educational Psychology Review, 27(3), 483–504. https://doi.org/10.1007/s10648-014-9273-3
Reimann, P. (1997). Lernprozesse beim Wissenserwerb aus Beispielen [Learning processes of knowledge acquisitions from examples]. Huber.
Renkl, A. (2014). Toward an instructionally oriented theory of example-based learning. Cognitive Science, 38, 1–37. https://doi.org/10.1111/cogs.12086
Roelle, J., Lehmkuhl, N., Beyer, M.-U., & Berthold, K. (2015). The role of specificity, targeted learning activities, and prior knowledge for the effects of relevance instructions. Journal of Educational Psychology, 107(3), 705–723. https://doi.org/10.1037/edu0000010
Roelle, J., Schmidt, E. M., Buchau, A., & Berthold, K. (2017). Effects of informing learners about the dangers of making overconfident judgments of learning. Journal of Educational Psychology, 109(1), 99–117. https://doi.org/10.1037/edu0000132
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(4), 375–415. https://doi.org/10.1080/07370008.2011.607930
Roth, W.-M. (1995). Authentic school science. Knowing and learning in open-inquiry science laboratories. Kluwer Academic.
Saenz, G. D., Geraci, L., & Tirso, R. (2019). Improving metacognition: A comparison of interventions. Applied Cognitive Psychology, 33(5), 918–929. https://doi.org/10.1002/acp.3556
Sánchez, E., & García-Rodicio, H. (2013). Using online measures to determine how learners process instructional explanations. Learning and Instruction, 26, 1–11. https://doi.org/10.1016/j.learninstruc.2012.12.003
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
Schworm, S., & Renkl, A. (2006). Computer-supported example-based learning: When instructional explanations reduce self-explanations. Computers & Education, 46(4), 426–445. https://doi.org/10.1016/j.compedu.2004.08.011
Serra, M. J., & DeMarree, K. G. (2016). Unskilled and unaware in the classroom: College students’ desired grades predict their biased grade predictions. Memory & Cognition, 44(7), 1127–1137. https://doi.org/10.3758/s13421-016-0624-9
Seufert, T. (2020). Building bridges between self-regulation and cognitive load—An invitation for a broad and differentiated attempt. Educational Psychology Review, 32(4), 1151–1162. https://doi.org/10.1007/s10648-020-09574-6
Sfard, A. (1998). On two metaphors for learning and the dangers of choosing just one. Educational Researcher, 27(2), 4–13. https://doi.org/10.3102/0013189X027002004
Sheldon, J. P. (2002). Operant conditioning concepts in introductory psychology textbooks and their companion web sites. Teaching of Psychology, 29(4), 281–285. https://doi.org/10.1207/S15328023TOP2904_04
Supanc, M., Völlinger, V. A., & Brunstein, J. C. (2017). High-structure versus low-structure cooperative learning in introductory psychology classes for student teachers: Effects on conceptual knowledge, self-perceived competence, and subjective task values. Learning and Instruction, 50, 75–84. https://doi.org/10.1016/j.learninstruc.2017.03.006
Talsma, K., Schüz, B., & Norris, K. (2019). Miscalibration of self-efficacy and academic performance: Self-efficacy ≠ self-fulfilling prophecy. Learning and Individual Differences, 69, 182–195. https://doi.org/10.1016/j.lindif.2018.11.002
Taylor, A. K., & Kowalski, P. (2014). Student misconceptions: Where do they come from and what can we do? In V. A. Benassi, C. E. Overson & C. M. Hakala (Eds.), Applying science of learning in education: Infusing psychological science into the curriculum (pp. 259–273). Retrieved from the Society for the Teaching of Psychology web site: http://teachpsych.org/ebooks/asle2014/index.php. Accessed 02 Sept 2020.
Thompson, V. A., Turner, J. A. P., & Pennycook, G. (2011). Intuition, reason, and metacognition. Cognitive Psychology, 63(3), 107–140. https://doi.org/10.1016/j.cogpsych.2011.06.001
Volet, S., Summers, M., & Thurman, J. (2009a). High-level co-regulation in collaborative learning: How does it emerge and how is it sustained? Learning and Instruction, 19(2), 128–143. https://doi.org/10.1016/j.learninstruc.2008.03.001
Volet, S., Vauras, M., & Salonen, P. (2009b). Self-and social regulation in learning contexts: An integrative perspective. Educational Psychologist, 44(4), 215–226. https://doi.org/10.1080/00461520903213584
Vosniadou, S. (2012). Reframing the classical approach to conceptual change: Preconceptions, misconceptions and synthetic models. In B. J. Fraser, K. Tobin & C. J. McRobbie (Eds.) Second international handbook of science education (pp. 119–130). Springer. https://doi.org/10.1007/978-1-4020-9041-7_10
Webb, N. M., & Farivar, S. (1999). Developing productive group interaction in middle school. In A. M. O'Donnell & A. King (Eds.), Cognitive perspectives on peer learning (pp. 117–149). Lawrence Erlbaum Associates Publishers.
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. 279–306). Erlbaum.
Winne, P. H., Hadwin, A. F., & Perry, N. E. (2013). Metacognition and computer-supported collaborative learning. In C. E. Hmelo-Silver, C. A. Chinn, C. K. K. Chan & A. O'Donnell (Eds.), Educational psychology handbook series. The international handbook of collaborative learning (pp. 462–479). Routledge/Taylor & Francis Group.
The authors acknowledge University of Rijeka, Croatia for funding this research within the project Personal and contextual determinants of learning and instruction different age groups (Grant 006.01.0059).
University of Rijeka, Croatia, within the project Personal and contextual determinants of learning and instruction different age groups (Grant 006.01.0059).
The study was approved by Ethical Committee for Scientific Research of the Faculty of Humanities and Social Sciences, University of Rijeka.
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Kolić-Vehovec, S., Pahljina-Reinić, R. & Rončević Zubković, B. Effects of collaboration and informing students about overconfidence on metacognitive judgment in conceptual learning. Metacognition Learning (2021). https://doi.org/10.1007/s11409-021-09275-7
- Metacognitive judgment
- Collaborative learning
- Conceptual learning