Instructional Science

, Volume 46, Issue 3, pp 337–355 | Cite as

Feedback that corrects and contrasts students’ erroneous solutions with expert ones improves expository instruction for conceptual change

  • Christa S. C. Asterhan
  • Aviv Dotan


In the present study, we examined the effects of feedback that corrects and contrasts a student’s own erroneous solutions with the canonical, correct one (CEC&C feedback) on learning in a conceptual change task. Sixty undergraduate students received expository instruction about natural selection, which presented the canonical, scientifically accepted account in detail. Two-third of these received CEC&C feedback on their self-generated solutions to open-ended test items. Students either received this feedback on their pretest solutions (prior to instruction), or on their immediate posttest solutions (following instruction). Students in the control condition only received the correct canonical answers to the immediate post-test items and compared these with their own solutions autonomously. Conceptual understanding on transfer items was assessed after 1 week. Results showed that students in the CEC&C feedback conditions outperformed control students. Timing of feedback did not affect learning, however. These findings add to accumulating evidence from different lines of research on the importance of instructional support that explicitly compares and contrasts between erroneous student models and canonical models in conceptual change tasks.


Conceptual change Feedback Comparing and contrasting Erroneous solutions 



This research was funded by Israeli Science Foundation Award 1044/13. We thank Maya Resnick, Roni Segal, Noa Ettinger and Morag Pitaro for their assistance in data collection and coding efforts.


  1. Ames, G. J., & Murray, F. B. (1982). When two wrongs make a right: Promoting cognitive development through cognitive conflict. Developmental Psychology, 18(6), 894–897. Scholar
  2. Asterhan, C. S. C., & Schwarz, B. B. (2007). The effects of monological and dialogical argumentation on concept learning in evolutionary theory. Journal of Educational Psychology, 99, 626–639.CrossRefGoogle Scholar
  3. Asterhan, C. S. C., & Schwarz, B. B. (2009). The role of argumentation and explanation in conceptual change: Indications from protocol analyses of peer-to-peer dialogue. Cognitive Science, 33, 373–399.CrossRefGoogle Scholar
  4. Asterhan, C. S. C., & Schwarz, B. B. (2016). Argumentation for learning: Well-trodden paths and unexplored territories. Educational Psychologist, 51(2), 164–187.CrossRefGoogle Scholar
  5. Asterhan, C. S. C., Schwarz, B. B., & Cohen-Eliyahu, N. (2014). Outcome feedback during collaborative learning: Contingencies between feedback and dyad composition. Learning and Instruction, 34(4), 1–10.CrossRefGoogle Scholar
  6. Babai, R., Sekal, R., & Stavy, R. (2010). Persistence of the intuitive conception of living things in adolescence. Journal of Science Education and Technology, 19(1), 20–26. Scholar
  7. Bransford, J. D., & Schwartz, D. L. (1999). Rethinking transfer: A simple proposal with multiple implications. Review of research in education, 24(1), 61–100.CrossRefGoogle Scholar
  8. Butler, A. C., Karpicke, J. D., & Roediger, H. L., III. (2007). The effect of type and timing of feedback on learning from multiple-choice tests. Journal of Experimental Psychology: Applied, 13(4), 273–281. Scholar
  9. Chan, C., Burtis, J., & Bereiter, C. (1997). Knowledge building as a mediator of conflict in conceptual change. Cognition and Instruction, 15(1), 1–40. Scholar
  10. Chi, M. T. H. (2008). Three types of conceptual change: Belief revision, mental model transformation, and categorical shift. In S. Vosniadou (Eds.), Handbook of research on conceptual change (pp. 61–82). Hillsdale, NJ: Lawrence Erlbaum Associates, Inc.
  11. Chi, M. T. H., Roscoe, R., Slotta, J., Roy, M., & Chase, M. (2012). Misconceived causal explanations for “emergent” processes. Cognitive Science, 36, 1–61. Scholar
  12. Chinn, C. A., & Brewer, W. F. (1993). The role of anomalous data in knowledge acquisition: A theoretical framework and implications for science instruction. Review of Educational Research, 63(1), 1–49. Scholar
  13. Chinn, C. A., & Brewer, W. F. (1998). An empirical test of a taxonomy of responses to anomalous data in science. Journal of Research in Science Teaching35(6), 623–654.
  14. Diakidoy, I. A. N., Kendeou, P., & Ioannides, C. (2003). Reading about energy: the effects of text structure in science learning and conceptual change. Contemporary Educational Psychology, 28, 335–356. Scholar
  15. Diakidoy, I. A. N., Mouskounti, T., Fella, A., & Ioannides, C. (2016). Comprehension processes and outcomes with refutation and expository texts and their contribution to learning. Learning and Instruction, 41, 60–69. Scholar
  16. Doise, W., & Mugny, G. (1978). Individual and collective conflicts of centration in cognitive development. European Journal of Social Psychology, 9(1), 245–247. Scholar
  17. Dunbar, K., Fugelsang, J., & Stein, C. (2007). Do naïve theories ever go away? Using brain and behavior to understand changes in concepts. Thinking with data. Scholar
  18. Durkin, K., & Rittle-Johnson, B. (2012). The effectiveness of using incorrect examples to support learning about decimal magnitude. Learning and Instruction, 22(3), 206–214. Scholar
  19. Fugelsang, J., & Dunbar, K. (2005). Brain-based mechanisms underlying complex causal thinking. Neuropsychologia, 48, 1204–1213.CrossRefGoogle Scholar
  20. Gadgil, S., Nokes-Malach, T. J., & Chi, M. T. (2012). Effectiveness of holistic mental model confrontation in driving conceptual change. Learning and Instruction, 22(1), 47–61. Scholar
  21. Große, C. S., & Renkl, A. (2007). Finding and fixing errors in worked examples: Can this foster learning outcomes? Learning and Instruction, 17, 612–634. Scholar
  22. Hattie, J., & Timperley, H. (2007). The power of feedback. Review of educational research, 77(1), 81–112. Scholar
  23. Hill, H., Rowan, B., & Ball, D. L. (2005). Effects of teachers’ mathematical knowledge for teaching on student achievement. American Education Research Journal, 42(2), 371–406. Scholar
  24. Howe, C., Tolmie, A., Duchak-Tanner, V., & Rattay, C. (2000). Hypothesis-testing in science: Group consensus and the acquisition of conceptual and procedural knowledge. Learning and Instruction, 10(4), 361–391. Scholar
  25. Jensen, M. S., & Finley, F. N. (1996). Changes in students’ understanding of evolution resulting from different curricular and instructional strategies. Journal of Research in Science Teaching, 33(8), 879–900.<879:aid-tea4>;2-t.CrossRefGoogle Scholar
  26. Jiménez-Aleixandre, M. P. (1992). Thinking about theories or thinking with theories?: A classroom study with natural selection. International Journal of Science Education, 14(1), 51–61. Scholar
  27. Kapur, M., & Bielaczyc, K. (2011). Classroom-based experiments in productive failure. In L. Carlson, C. Holscher, & T. Shipley (Eds.), Proceedings of the 33rd Annual Conference of the Cognitive Science Society (pp. 2812–2817). Austin, TX: Cognitive Science Society.
  28. Kluger, A. N., & DeNisi, A. (1996). The effects of feedback interventions on performance: A historical review, a meta-analysis, and a preliminary feedback intervention theory. Psychological Bulletin, 119(2), 254–284. Scholar
  29. Kulik, J. A., & Kulik, C. L. C. (1988). Timing of feedback and verbal learning. Review of Educational Research, 58(1), 79–97. Scholar
  30. Light, P., & Glachan, M. (1985). Facilitation of individual problem solving through peer interaction. Educational Psychology, 5, 217–225. Scholar
  31. Limon, M. (2001). On the cognitive conflict as an instructional strategy for conceptual change: A critical appraisal. Learning and Instruction, 11, 357–380. Scholar
  32. Loibl, K., & Rummel, N. (2014). Knowing what you don’t know makes failure productive. Learning and Instruction, 34, 74–85. Scholar
  33. Masson, S., Potvin, P., Riopel, M., & Foisy, L. M. B. (2014). Differences in brain activation between novices and experts in science during a task involving a common misconception in electricity. Mind, Brain, and Education, 8(1), 44–55. Scholar
  34. Ohlsson, S. (2002). Generating and understanding qualitative explanations. In J. Otero, J. A. Leon, & A. C. Graesser (Eds.), The psychology of science text comprehension (pp. 91–128). Mahwah, NJ: Erlbaum.Google Scholar
  35. Opfer, J. E., & Siegler, R. S. (2004). Revisiting preschoolers’ living things concept: A microgenetic analysis of conceptual change in basic biology. Cognitive Psychology, 49, 301–332. Scholar
  36. Özdemir, G., & Clark, D. (2007). An overview of conceptual change theories. Eurasia Journal of Mathematics, Science & Technology Education, 3, 351–361.CrossRefGoogle Scholar
  37. Potvin, P., Masson, S., Lafortune, S., & Cyr, G. (2015a). Persistence of the intuitive conception that heavier objects sink more: A reaction time study with different levels of interference. International Journal of Science and Mathematics Education, 13(1), 21–43. Scholar
  38. Potvin, P., Sauriol, É., & Riopel, M. (2015b). Experimental evidence of the superiority of the prevalence model of conceptual change over the classical models and traditional teaching. Journal of Research in Science Teaching, 52(8), 1082–1108. Scholar
  39. Ramsburg, J. T., & Ohlsson, S. (2016). Category change in the absence of cognitive conflict. Journal of Educational Psychology, 108(1), 98. Scholar
  40. Richland, L. E., Kornell, N., & Kao, L. S. (2009). The pretesting effect: Do unsuccessful retrieval attempts enhance learning? Journal of Experimental Psychology: Applied, 15(3), 243. Scholar
  41. Sadler, P. M., Sonnert, G., Coyle, H. P., Cook-Smith, N., & Miller, J. L. (2013). The influence of teachers’ knowledge on student learning in middle school physical science classrooms. American Educational Research Journal, 50(5), 1020–1049. Scholar
  42. Schnotz, W., & Preuss, A. (1999). Task-dependent construction of mental models as a basis for conceptual change. In W. Schnotz, S. Vosniadou, & M. Carretero (Eds.), New perspectives on conceptual change (pp. 193–222). Amsterdam: Pergamon Press.Google Scholar
  43. Schwarz, B. B., & Linchevski, L. (2007). The role of task design and argumentation in cognitive development during peer interaction: The case of proportional reasoning. Learning and Instruction, 17(5), 510–531. Scholar
  44. Schwarz, B. B., Neuman, Y., & Biezuner, S. (2000). Two wrongs may make a right… If they argue together! Cognition and Instruction, 18(4), 461–494. Scholar
  45. Shtulman, A. (2006). Qualitative differences between naïve and scientific theories of evolution. Cognitive Psychology, 52, 170–194. Scholar
  46. Shtulman, A., & Valcarcel, J. (2012). Scientific knowledge suppresses but does not supplant earlier intuitions. Cognition, 124(2), 209–215. Scholar
  47. Sinatra, G. M., & Broughton, S. H. (2011). Bridging reading comprehension and conceptual change in science education: The promise of refutation text. Reading Research Quarterly, 46(4), 374–393.CrossRefGoogle Scholar
  48. Tippett, C. D. (2010). Refutation text in science education: A review of two decades of research. International Journal of Science and Mathematics Education, 8, 951–970.CrossRefGoogle Scholar
  49. Van Loon, M. H., Dunlosky, J., Van Gog, T., Van Merriënboer, J. J., & De Bruin, A. B. (2015). Refutations in science texts lead to hypercorrection of misconceptions held with high confidence. Contemporary Educational Psychology, 42, 39–48. Scholar
  50. Vosniadou, S. (Ed.). (2009). International handbook of research on conceptual change. Hillsdale, NJ: Lawrence Erlbaum Associates, Inc.
  51. Vosniadou, S., & Brewer, W. F. (1994). Mental models of the day/nightcycle. Cognitive Science, 18, 123–183.CrossRefGoogle Scholar
  52. Vosniadou, S., & Mason, L. (2013). Conceptual change induced by instruction: A complex interplay of multiple factors. In S. Graham, J. Royer, & M. Zeidner (Eds.), Individual differences and cultural and contextual factors, Vol 2 of the APA Educational Psychology Handbook Series (pp. 221–246). APA Publications.

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© Springer Science+Business Media B.V., part of Springer Nature 2017

Authors and Affiliations

  1. 1.School of EducationHebrew University of JerusalemJerusalemIsrael

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