Skip to main content

Investigating the Differences in Effects of the Persuasive Message’s Timing During Science Learning to Overcome the Cognitive Dissonance

  • Conference paper
  • First Online:
Social Robotics (ICSR 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9979))

Included in the following conference series:

Abstract

Based on conceptual change theory, cognitive dissonance is known as an important factor in conceptual change. Thus, those who design and build educational robots will need to understand how best to provide ways for robots to implicitly persuade students to change their bad attitudes when encountering a cognitively dissonant situation. Building on diverse literature, we examine how to make students change their bad attitudes of avoiding difficult science exercises. More precisely, we intend to make students overcome cognitive dissonance by choosing to redo a difficult science exercise that they had previously answered incorrectly rather than jumping to another exercise. First, we introduce the concept of gamma window. Then we investigate how different timings of the persuasive strategy affect how students overcome the cognitive dissonance and avoid learned helplessness.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    The student thinks that he has to change his bad attitude of avoiding difficult exercises.

  2. 2.

    e.g., “After all, science learning is not that important. Many other tasks could be done.”.

  3. 3.

    The student thinks that the answer afforded by the book is incorrect and that there was a mistake in the correction because the student thinks that he has mastered the subject (very high self-esteem).

  4. 4.

    Forewarning often produces resistance to persuasion.

  5. 5.

    goo.gl/CBc26W.

  6. 6.

    No direct robot’s request could be inferred directly based on it speech.

  7. 7.

    We considered students that have a minimum of cognition need.

  8. 8.

    This is by debriefing the students. In fact, psychologists usually think of explicit measures as those that require respondents’ conscious attention to the construct being measured by using Likert scale and semantic differential scale (we need to measure the planned behavior in our case).

  9. 9.

    This is important to verify whether the student is convinced that he needs to strive for science learning by redoing difficult exercises rather than adopting a negative implicit attitude that supports learned helplessness. Implicit measures are those that do not require this conscious attention (spontaneous behavior). Some methods could help to measure the implicit attitude such as evaluative priming and the implicit association test.

  10. 10.

    This is to measure level of cognitive dissonance according to the student’s subjective evaluation.

  11. 11.

    www.allaboutux.org/self-assessment-scale-sam.

  12. 12.

    Wear-out could occur when the student is inattentionally blind to the message’s irritation and immediately feels that he hates the message.

  13. 13.

    Whether the student thinks that performing the attitude is good or bad.

  14. 14.

    It refers to the student’s beliefs about how significant others view the relevant behavior.

  15. 15.

    It refers to the notion that behavioral prediction is affected by whether people believe that they can perform the relevant behavior.

  16. 16.

    Intrusive thoughts about the current exercise that was once pursued and left incomplete. In this case, students might experience it because they stopped at least a few moments to listen to the robot’s message.

  17. 17.

    The student has already exerted cognitive effort just before to overcome the first cognitive dissonance.

References

  1. Szafir, D., Mutlu, B.: Pay Attention!: designing adaptive agents that monitor and improve user engagement. In: Human Factors in Computing Systems, pp. 11–20 (2012)

    Google Scholar 

  2. Han, J., Kim, D.: r-Learning services for elementary school students with a teaching assistant robot. In: Conference on Human Robot Interaction, pp. 255–256 (2009)

    Google Scholar 

  3. Billard, A.: Robota: clever toy and educational tool. Robot. Auton. Syst. 42, 259–269 (2003)

    Article  MATH  Google Scholar 

  4. Kanda, T., Sato, R., Ishiguro, H.: A two-month field trial in an elementary school for long-term human-robot interaction. IEEE Trans. Robot. 23(5), 962–971 (2007)

    Article  Google Scholar 

  5. Zhen, Y., Chi, S., Chih, C., Gwo-Dong, C.: A robot as a teaching assistant in an English class. In: Conference on Advanced Learning Technologies, pp. 87–91 (2006)

    Google Scholar 

  6. Siegel, M., Breazeal, C., Norton, M.I.: Persuasive Robotics: The influence of robot gender on human behavior. In: International Conference on Intelligent Robots and Systems, pp. 2563–2568 (2009)

    Google Scholar 

  7. Ham, J., Midden, C.J.H.: A persuasive robot to stimulate energy conservation: the influence of positive and negative social feedback and task similarity on energy-consumption behavior. Int. J. Soc. Robot. 6(2), 163–171 (2014)

    Article  Google Scholar 

  8. Abramason, L.Y., Seligman, M.E., Teasdale, J.D.: Learned Helplessness in humans: critique and reformulation. J. Abnormal Psychol., 49–74 (1978)

    Google Scholar 

  9. Douglas, H., Jullian, S., Geoffrey, S., Lester, J.: After I had made the decision. toward a scale to measure cognitive dissonance. J. Consum. Satisfaction Dissatisfaction Complaining Behavior (1998)

    Google Scholar 

  10. Cacioppo, J.T., Petty, R.E., Kao, C.F.: The efficient assessment of need for cognition. J. Pers. Assess. 48(3), 306–307 (1984)

    Article  Google Scholar 

  11. Roets, A., Van Hiel, A.: Item selection and validation of a brief, 15-item version of the need for closure scale. Personality Individ. Differ. 50(1), 90–94 (2011)

    Article  Google Scholar 

  12. Pantos, A.J.: Measuring implicit and explicit attitudes toward foreign-accented speech. J. Lang. Soc. Psychol. 32(1), 3–20 (2013)

    Article  Google Scholar 

  13. Levin, D., Harriott, C., Natalie, A.P., Tao, Z., Julie, A.A.: Cognitive dissonance as a measure of reactions to human-robot interaction. J. Hum. Robot Interact. 2(3), 3–17 (2013)

    Article  Google Scholar 

  14. Fazio, R.H.: Multiple processes by which attitudes guide behavior: the MODE model as an integrative frame work. Adv. Exp. Soc. Psychol. 23, 75–109 (1990)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Khaoula Youssef .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing AG

About this paper

Cite this paper

Youssef, K., Ham, J., Okada, M. (2016). Investigating the Differences in Effects of the Persuasive Message’s Timing During Science Learning to Overcome the Cognitive Dissonance. In: Agah, A., Cabibihan, JJ., Howard, A., Salichs, M., He, H. (eds) Social Robotics. ICSR 2016. Lecture Notes in Computer Science(), vol 9979. Springer, Cham. https://doi.org/10.1007/978-3-319-47437-3_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-47437-3_11

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47436-6

  • Online ISBN: 978-3-319-47437-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics