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Impact of Agent Role on Confusion Induction and Learning

  • Blair Lehman
  • Arthur Graesser
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8474)

Abstract

The presentation of contradictory information to trigger deeper processing and increase learning has been investigated in a variety of ways (e.g., conversational agents, worked examples). However, the impact of information source (e.g., expertise, gender) and the relationship between the contradicting sources (e.g., status level) has not been investigated to the same degree. We previously reported that confusion can successfully be induced and learning increased when contradictory information was presented by two conversational agents (tutor, peer student). In the present experiment we investigated contradictions posed by two peer student agents. Self-reports of confusion and learner responses to embedded forced-choice questions revealed that the contradictions still successfully induced confusion. There were, however, differences in the nature of confusion induction based on the inter-agent relationship (i.e., student-student vs. tutor-student). Learners performed better on transfer tasks when presented with contradictions compared to a no-contradiction control, but only when they were successfully confused.

Keywords

confusion contradiction affect tutoring animated pedagogical agents intelligent tutoring systems learning 

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References

  1. 1.
    D’Mello, S., Lehman, B., Pekrun, R., Graesser, A.: Confusion can be beneficial for learning. Learning and Instruction 29, 153–170 (2014)CrossRefGoogle Scholar
  2. 2.
    Grosse, C., Renkl, A.: Finding and fixing errors in worked examples: Can this foster learning outcomes? Learning and Instruction 17, 612–634 (2007)CrossRefGoogle Scholar
  3. 3.
    Lehman, B., D’Mello, S., Strain, A., Mills, C., Gross, M., Dobbins, A., et al.: Inducting and tracking confusion with contradictions during complex learning. International Journal of Artificial Intelligence in Education 22, 71–93 (2013)Google Scholar
  4. 4.
    McLaren, B.M., et al.: To err is human, to explain and correct is divine: A study of interactive erroneous examples with middle school math students. In: Ravenscroft, A., Lindstaedt, S., Kloos, C.D., Hernández-Leo, D. (eds.) EC-TEL 2012. LNCS, vol. 7563, pp. 222–235. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  5. 5.
    Tsovaltzi, D., Melis, E., McLaren, B.: Erroneous examples: Effects on learning fractions in a web-based setting. International Journal of Technology Enhanced Learning 4, 191–230 (2012)CrossRefGoogle Scholar
  6. 6.
    Braasch, J., Rouet, J.-F., Vibert, N., Britt, M.: Readers’ use of source information in text comprehension. Memory & Cognition 40, 450–465 (2012)CrossRefGoogle Scholar
  7. 7.
    Limón, M.: On the cognitive conflict as an instructional strategy for conceptual change: A critical appraisal. Learning and Instruction 11, 357–380 (2001)CrossRefGoogle Scholar
  8. 8.
    Festinger, L.: A theory of cognitive dissonance. Row Peterson, Evanston (1957)Google Scholar
  9. 9.
    Graesser, A., Lu, S., Olde, B., Cooper-Pye, E., Whitten, S.: Question asking and eye tracking during cognitive disequilibrium: Comprehending illustrated texts on devices when devices breakdown. Memory & Cognition 33, 1235–1247 (2005)CrossRefGoogle Scholar
  10. 10.
    Piaget, J.: The origins of intelligence. International University Press, New York (1952)Google Scholar
  11. 11.
    Chinn, C., Brewer, W.: An empirical test of a taxonomy of responses to anomalous data in science. Journal of Research in Science Teaching 35, 623–654 (1998)CrossRefGoogle Scholar
  12. 12.
    Bråten, I., Strømsø, H., Britt, M.: Trust matters: Examining the role of source evaluation in students’ construction of meaning within and across multiple texts. Reading Research Quarterly 44, 6–28 (2009)CrossRefGoogle Scholar
  13. 13.
    Goldman, S., Braasch, J., Wiley, J., Graesser, A., Brodowinska, K.: Comprehending and learning from internet sources: Processing patterns of better and poorer learners. Reading Research Quarterly 47, 356–381 (2012)Google Scholar
  14. 14.
    Strømsø, H., Bråten, I., Britt, M.: Reading multiple texts about climate change: The relationship between memory for sources and text comprehension. Learning and Instruction 20, 192–204 (2010)CrossRefGoogle Scholar
  15. 15.
    Wiley, J., Goldman, S., Graesser, A., Sanchez, C., Ash, I., Hemmerich, J.: Source evaluation, comprehension, and learning in internet science inquiry tasks. American Educational Research Journal 46, 1060–1106 (2009)CrossRefGoogle Scholar
  16. 16.
    Baylor, A., Kim, Y.: Simulating instructional roles through pedagogical agents. International Journal of Artificial Intelligence in Education 15, 95–115 (2005)Google Scholar
  17. 17.
    Halpern, D., Millis, K., Graesser, A., Butler, H., Forsyth, C., Cai, Z.: Operation ARA: A computerized learning game that teaches critical thinking and scientific reasoning. Thinking Skills and Creativity 7, 93–100 (2012)CrossRefGoogle Scholar
  18. 18.
    Graesser, A., D’Mello, S.: Emotions during the learning of difficult material. In: Ross, B. (ed.) The Psychology of Learning and Motivation, vol. 57, pp. 183–225. Elsevier (2012)Google Scholar
  19. 19.
    D’Mello, S., Graesser, A.: Dynamics of affective states during complex learning. Learning and Instruction 22, 145–157 (2012)CrossRefGoogle Scholar
  20. 20.
    VanLehn, K., Siler, S., Murray, C., Yamauchi, T., Baggett, W.: Why do only some events cause learning during human tutoring? Cognition & Instruction 21, 209–249 (2003)CrossRefGoogle Scholar
  21. 21.
    Chan, C., Burtis, J., Bereiter, C.: Knowledge building as a mediator of conflict in conceptual change. Cognition and Instruction 15, 1–40 (1997)CrossRefGoogle Scholar
  22. 22.
    Baylor, A., Kim, Y.: The role of gender and ethnicity in pedagogical agent perception. In: Richards, G. (ed.) Proceedings of the World Conference on E-learning in Corporate, Government, Healthcare, and Higher Education, pp. 1503–1506. AACE, Chesapeake (2003)Google Scholar
  23. 23.
    Moreno, R., Flowerday, T.: Students’ choice of animated pedagogical agents in science learning: A test of the similarity-attraction hypothesis on gender and ethnicity. Contemporary Educational Psychology 31, 186–207 (2005)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Blair Lehman
    • 1
  • Arthur Graesser
    • 1
  1. 1.University of MemphisMemphisUSA

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