Advertisement

Instructional Science

, Volume 36, Issue 1, pp 27–52 | Cite as

Is underestimation less detrimental than overestimation? The impact of experts’ beliefs about a layperson’s knowledge on learning and question asking

  • Jörg WittwerEmail author
  • Matthias Nückles
  • Alexander Renkl
Article

Abstract

Although prior research has shown that experts tend to overestimate or underestimate what laypersons actually know, little is known about the specific consequences of biased estimations for communication. To investigate the impact of biased estimations of a layperson’s knowledge on the effectiveness of experts’ explanations, we conducted a web-based dialog experiment with 45 pairs of experts and laypersons. We manipulated the experts’ mental model of the layperson by presenting them either valid information about the layperson’s knowledge or information that was biased towards overestimation or underestimation. Results showed that the experts adopted the biased estimations and adapted their explanations accordingly. Consequently, the laypersons’ learning from the experts’ explanations was impaired when the experts overestimated or underestimated the layperson’s knowledge. In addition, laypersons whose knowledge was overestimated more often generated questions that reflected comprehension problems. Laypersons whose knowledge was underestimated asked mainly for additional information previously not addressed in the explanations. The results suggest that underestimating a learner during the instructional dialog is as detrimental to learning as is the overestimation of a learner’s knowledge. Thus, the provision of effective explanations presupposes an accurate mental model of the learner’s knowledge prerequisites.

Keywords

Adaptive instruction Advice giving Computer-mediated communication Expert-layperson communication Informal learning Learning from instructional texts Question asking 

Notes

Acknowledgments

This research was supported by the Deutsche Forschungsgemeinschaft (DFG; [German Research Foundation]) with a project grant awarded to Matthias Nückles and Alexander Renkl (contract NU 129/1-1). We thank Tarik Gasmi for the programming of the assessment tool database system. Many thanks also go to our student research assistants, Christine Otieno, Isabel Braun, Eva März, and Sandra Hübner, for their help with many practical aspects of the project such as data collection and scoring. We also wish to thank Marcia Neff and Susan Bell for their proofreading.

References

  1. Alexander, P. A., Kulikowich, J. M., & Schulze, S. K. (1994). How subject-matter knowledge affects recall and interest. American Educational Research Journal, 31, 313–337.CrossRefGoogle Scholar
  2. Alty, J. L., & Coombs, M. J. (1981). Communicating with university computer users: A case study. In M. J. Coombs & J. L. Alty (Eds.), Computing skills and the user interface (pp. 7–71). London: Academic Press.Google Scholar
  3. Anderson, K. C., & Leinhardt, G. (2002). Maps as representations: Expert novice comparison of projection understanding. Cognition and Instruction, 20, 283–321.CrossRefGoogle Scholar
  4. Beck, I. L., McKeown, M. G., Sinatra, G. M., & Loxterman, J. A. (1991). Revising social studies text from a textprocessing perspective: Evidence of improved comprehensibility. Reading Research Quarterly, 26, 251–276.CrossRefGoogle Scholar
  5. Borko, H., & Putnam, R. (1996). Learning to teach. In D. Berliner & R. Calfee (Eds.), Handbook of educational psychology (pp. 673–708). New York: Macmillan.Google Scholar
  6. Brennan, S. E., & Clark, H. H. (1996). Conceptual pacts and lexical choice in conversation. Journal of Experimental Psychology: Learning, Memory, and Cognition, 22, 1482–1493.CrossRefGoogle Scholar
  7. Britton, B. K., & Gülgöz, S. (1991). Using Kintsch’s computational model to improve instructional text: Effects of repairing inference calls on recall and cognitive structures. Journal of Educational Psychology, 83, 329–345.CrossRefGoogle Scholar
  8. Bromme, R., Jucks, R., & Runde, A. (2005a). Barriers and biases in computer-mediated expert-layperson-communication. In R. Bromme, F. W. Hesse, & H. Spada (Eds.), Barriers and biases in computer-mediated knowledge communication – and how they may be overcome (pp. 89–118). New York: Springer.Google Scholar
  9. Bromme, R., Jucks, R., & Wagner, T. (2005b). How to refer to “diabetes”? Language in online health advice. Applied Cognitive Psychology, 19, 569–586.CrossRefGoogle Scholar
  10. Bromme, R., Nückles, M., & Rambow, R. (1999). Adaptivity and anticipation in expert-laypeople communication. In S. E. Brennan, A. Giboin, & D. Traum (Eds.), Psychological models of communication in collaborative systems. AAAI Fall Symposion Series (pp. 17–24). Menlo Park, CA: AAAI.Google Scholar
  11. Bromme, R., Rambow, R., & Nückles, M. (2001). Expertise and estimating what other people know: The influence of professional experience and type of knowledge. Journal of Experimental Psychology: Applied, 7, 317–330.Google Scholar
  12. Brown, P. M., & Dell, G. S. (1987). Adapting production to comprehension: The explicit mention of instruments. Cognitive Psychology, 19, 441–472.CrossRefGoogle Scholar
  13. Candlin, C. N., & Candlin, S. (2002). Discourse, expertise, and the management of risk in health care settings. Research on Language and Social Interaction, 35, 115–137.CrossRefGoogle Scholar
  14. Chi, M. T. H., Feltovich, P., & Glaser, R. (1981). Categorization and representation of physics problems by experts and novices. Cognitive Science, 5, 121–152.CrossRefGoogle Scholar
  15. Chi, M. T. H., Glaser, R., & Farr, M. (Eds.). (1988). The nature of expertise. Hillsdale, NJ: Lawrence Erlbaum.Google Scholar
  16. Chi, M. T. H., Siler, S., & Jeong, H. (2004). Can tutors monitor students’ understanding accurately? Cognition and Instruction, 22, 363–387.CrossRefGoogle Scholar
  17. Chi, M. T. H., Siler, S., Jeong, H., Yamauchi, T., & Hausmann, R. G. (2001). Learning from human tutoring. Cognitive Science, 25, 471–533.CrossRefGoogle Scholar
  18. Clark, H. H. (1992). Arenas of language use. Chicago: University of Chicago Press.Google Scholar
  19. Clark, H. H. (1996). Using language. Cambridge: University Press.Google Scholar
  20. 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
  21. Clark, H. H., & Murphy, G. L. (1982). Audience design in meaning and reference. In J. F. LeNy & W. Kintsch (Eds.), Language and comprehension (pp. 287–299). Amsterdam: North-Holland Publishing Company.Google Scholar
  22. Cohen, P. A., Kulik, J. A., & Kulik, C. (1982). Educational outcomes of tutoring: A meta-analysis of findings. American Educational Research Journal, 19, 237–248.CrossRefGoogle Scholar
  23. Cramton, C. D. (2001). The mutual knowledge problem and its consequences for dispersed collaboration. Organization Science, 12, 346–371.CrossRefGoogle Scholar
  24. Erickson, F., & Schultz, J. (1982). The counselor as gatekeeper. Social interaction in interviews. New York: Academic Press.Google Scholar
  25. Fleiss, J. L. (1981). Statistical methods for rates and proportions. New York, NY: Wiley.Google Scholar
  26. Fussell, S. R., & Krauss, R. M. (1992). Coordination of knowledge in communication: Effects of speakers’ assumptions about what others know. Journal of Personality and Social Psychology, 62, 378–391.CrossRefGoogle Scholar
  27. Glass, M., Kim, J. H., Evens, M. E., Michael, J. A., & Rovick, A. A. (1999). Novice vs. expert tutors: A comparison of style. In Tenth midwest artificial intelligence and cognitive science conference (pp. 43–49). Bloomington, IN: AAAI Press.Google Scholar
  28. Graesser, A. C., Léon, J. A., & Otero, J. C. (2002). Introduction to the psychology of science text comprehension. In J. Otero, J. A. Léon, & A. C. Graesser (Eds.), The psychology of science text comprehension (pp. 1–15). Mahwah, NJ: Lawrence Erlbaum.Google Scholar
  29. Graesser, A. C., & McMahen, C. L. (1993). Anomalous information triggers questions when adults solve quantitative problems and comprehend stories. Journal of Educational Psychology, 85, 136–151.CrossRefGoogle Scholar
  30. Graesser, A. C., Person, N. K., & Huber, J. (1992). Mechanisms that generate questions. In T. Lauer, E. Peacock, & A. Graesser (Eds.), Questions and information systems (pp. 167–187). Hillsdale, NJ: Lawrence Erlbaum.Google Scholar
  31. Hinds, P. J. (1999). The curse of expertise: The effects of expertise and debiasing methods on predictions of novice performance. Journal of Experimental Psychology: Applied, 5, 205–221.CrossRefGoogle Scholar
  32. Hinds, P. J., Patterson, M., & Pfeffer, J. (2001). Bothered by abstraction: The effect of expertise on knowledge transfer and subsequent knowledge performance. Journal of Applied Psychology, 86, 1232–1243.CrossRefGoogle Scholar
  33. Hinds, P. J., & Pfeffer, J. (2003). Why organizations don’t “know what they know”: Cognitive and motivational factors affecting the transfer of expertise. In M. Ackerman, V. Pipek, & V. Wulf (Eds.), Beyond knowledge management: Sharing expertise (pp. 3–26). Cambridge, MA: MIT Press.Google Scholar
  34. Horton, W. S., & Gerrig, R. J. (2002). Speakers’ experiences and audience design: Knowing when and knowing how to adjust utterances to addressees. Journal of Memory and Language, 47, 589–606.CrossRefGoogle Scholar
  35. Horton, W. S., & Keysar, B. (1996). When do speakers take into account common ground? Cognition, 59, 91–117.CrossRefGoogle Scholar
  36. Isaacs, E. A., & Clark, H. H. (1987). References in conversation between experts and novices. Journal of Experimental Psychology: General, 116, 26–37.CrossRefGoogle Scholar
  37. Kintsch, W. (1994). Text comprehension, memory, and learning. American Psychologist, 49, 294–303.CrossRefGoogle Scholar
  38. Krauss, R. M., & Fussell, S. R. (1996). Social psychological models of interpersonal communication. In E. T. Higgins & A. Kruglanski (Eds.), Social psychology: A handbook of basic principles (pp. 655–701). New York: Guilford.Google Scholar
  39. Lau, I. Y.-M., Chiu, C.-Y., & Hong, Y.-Y. (2001). I know what you know: Assumptions about others’ knowledge and their effects on message construction. Social Cognition, 19, 587–600.CrossRefGoogle Scholar
  40. Lebie, L., Rhoades, J. A., & McGrath, J. E. (1996). Interaction process in computer-mediated and face-to-face groups. Computer Supported Cooperative Work, 4, 127–152.CrossRefGoogle Scholar
  41. Macintosh, G., & Gentry, J. W. (1999). Decision making in personal selling: Testing the ‘K.I.S.S. principle’. Psychology & Marketing, 16, 393–408.CrossRefGoogle Scholar
  42. McNamara, D. S., Kintsch, E., Songer, N. B., & Kintsch, W. (1996). Are good texts always better? Interactions of text coherence, background knowledge, and levels of understanding in learning from text. Cognition and Instruction, 14, 1–43.CrossRefGoogle Scholar
  43. Nathan, M. J., & Koedinger, K. R. (2000). An investigation of teachers’ beliefs of students’ algebra development. Cognition and Instruction, 18, 209–237.CrossRefGoogle Scholar
  44. Nathan, M. J., & Petrosino, A. J. (2003). Expert blind spot among preservice teachers. American Educational Research Journal, 40, 905–928.CrossRefGoogle Scholar
  45. Nickerson, R. S. (1999). How we know – and sometimes misjudge – what others know: Imputing one’s own knowledge to others. Psychological Bulletin, 125, 737–759.CrossRefGoogle Scholar
  46. Nückles, M., & Bromme, R. (2002). Internet experts’ planning of explanations for laypersons: A web experimental approach in the Internet domain. Experimental Psychology, 49, 292–304.Google Scholar
  47. Nückles, M., & Stürz, A. (2006). The assessment tool. A method to support asynchronous communication between computer experts and laypersons. Computers in Human Behavior, 22, 917–940.CrossRefGoogle Scholar
  48. Nückles, M., Winter, A., Wittwer, J., Herbert, M., & Hübner, S. (2006). How do experts adapt their explanations to a layperson’s knowledge in asynchronous communication? An experimental study. User Modeling and User-Adapted Interaction, 16, 87–127.CrossRefGoogle Scholar
  49. Nückles, M., Wittwer, J., & Renkl, A. (2005). Information about a layperson’s knowledge supports experts in giving effective and efficient online advice to laypersons. Journal of Experimental Psychology: Applied, 11, 219–236.CrossRefGoogle Scholar
  50. Otero, J., & Graesser, A. C. (2001). PREG: Elements of a model of question asking. Cognition and Instruction, 19, 143–175.CrossRefGoogle Scholar
  51. Person, N. K., Graesser, A. C., Magliano, J. P., & Kreuz, R. J. (1994). Inferring what the student knows in one-to-one tutoring: The role of student questions and answers. Learning and Individual Differences, 6, 205–229.CrossRefGoogle Scholar
  52. Pickering, J. M., & Garrod, S. (2004). Toward a mechanistic psychology of dialogue. Behavioral and Brain Science, 27, 169–226.Google Scholar
  53. Reid, F. J. M., Malinek, V., Stott, C. J. T., & Evans, J. St. B. T. (1996). The messaging threshold in computer-mediated communication. Ergonomics, 39, 1017–1037.CrossRefGoogle Scholar
  54. Richter, T., Naumann, J., & Groeben, N. (2000). Attitudes toward the computer: Construct validation of an instrument with scales differentiated by content. Computers in Human Behavior, 16, 473–491.CrossRefGoogle Scholar
  55. Rikers, R. M. J. P., Schmidt, H. G., & Boshuizen, H. P. A. (2002). On the constraints of encapsulated knowledge: Clinical case representations by medical experts and subexperts. Cognition and Instruction, 20, 27–45.CrossRefGoogle Scholar
  56. Ross, L., Greene, D., & House, P. (1977). The ‘false consensus’ effect: An egocentric bias in social perception and attribution processes. Journal of Experimental Social Psychology, 13, 279–301.CrossRefGoogle Scholar
  57. Schober, M. F. (1998). Different kinds of conversational perspective-taking. In S. R. Fussell & R. J. Kreuz (Eds.), Social and cognitive psychological approaches to interpersonal communication (pp. 145–174). Mahwah, NJ: Lawrence Erlbaum.Google Scholar
  58. Schober, M. F., & Brennan, S. E. (2003). Processes of interactive spoken discourse: The role of the partner. In A. C. Graesser, M. A. Gernsbacher, & S. R. Goldman (Eds.), The handbook of discourse processes (pp. 123–164). Mahwah, NJ: Lawrence Erlbaum.Google Scholar
  59. Schober, M. F., & Clark, H. H. (1989). Understanding by addressees and overhearers. Cognitive Psychology, 21, 211–232.CrossRefGoogle Scholar
  60. Schulze, H. H. (2003). Computerlexikon [computer glossary]. Reinbek, Germany: Rowohlt.Google Scholar
  61. Siler, S. A., & VanLehn, K. (2003). Accuracy of tutors’ assessments of their students by tutoring context. In R. Alterman & D. Kirsch (Eds.), Proceedings of the 25th annual conference of the cognitive science society. Mahwah, NJ: Erlbaum.Google Scholar
  62. Simon, H. A., & Chase, W. G. (1973). Skill in chess. American Scientist, 61, 394–403.Google Scholar
  63. Stehr, N., & Ericson, R. V. (1992). The culture and power of knowledge – Inquiries into cotemporary societies. Berlin: de Gruyter.Google Scholar
  64. Tversky, A., & Kahneman, D. (1974). Judgement under uncertainty: Heuristics and biases. Science, 185, 1124–1131.CrossRefGoogle Scholar
  65. VanLehn, K., Siler, S., Murray, C., Yamauchi, T., & Baggett, W. B. (2003). Why do only some events cause learning during human tutoring? Cognition and Instruction, 21, 209–249.CrossRefGoogle Scholar
  66. Vidal-Abarca, E., & Sanjose, V. (1998). Levels of comprehension of scientific prose: The role of text variables. Learning and Instruction, 8, 215–233.CrossRefGoogle Scholar
  67. Voss, J. F., & Silfies, L. N. (1996). Learning from history text: The interaction of knowledge comprehension skill with text structure. Cognition and Instruction, 14, 45–68.CrossRefGoogle Scholar
  68. Wolfe, M. B., Schreiner, M. E., Rehder, B., Laham, D., Foltz, P. W., Kintsch, W., & Landauer, T. K. (1998). Learning from text: Matching readers and text by latent semantic analysis. Discourse Processes, 25, 309–336.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, Inc. 2007

Authors and Affiliations

  • Jörg Wittwer
    • 1
    • 2
    Email author
  • Matthias Nückles
    • 1
  • Alexander Renkl
    • 1
  1. 1.Department of PsychologyUniversity of FreiburgFreiburgGermany
  2. 2.Leibniz Institute for Science Education at the University of KielKielGermany

Personalised recommendations