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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
The student thinks that he has to change his bad attitude of avoiding difficult exercises.
- 2.
e.g., “After all, science learning is not that important. Many other tasks could be done.”.
- 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.
Forewarning often produces resistance to persuasion.
- 5.
- 6.
No direct robot’s request could be inferred directly based on it speech.
- 7.
We considered students that have a minimum of cognition need.
- 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.
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.
This is to measure level of cognitive dissonance according to the student’s subjective evaluation.
- 11.
- 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.
Whether the student thinks that performing the attitude is good or bad.
- 14.
It refers to the student’s beliefs about how significant others view the relevant behavior.
- 15.
It refers to the notion that behavioral prediction is affected by whether people believe that they can perform the relevant behavior.
- 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.
The student has already exerted cognitive effort just before to overcome the first cognitive dissonance.
References
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)
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)
Billard, A.: Robota: clever toy and educational tool. Robot. Auton. Syst. 42, 259–269 (2003)
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)
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)
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)
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)
Abramason, L.Y., Seligman, M.E., Teasdale, J.D.: Learned Helplessness in humans: critique and reformulation. J. Abnormal Psychol., 49–74 (1978)
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)
Cacioppo, J.T., Petty, R.E., Kao, C.F.: The efficient assessment of need for cognition. J. Pers. Assess. 48(3), 306–307 (1984)
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)
Pantos, A.J.: Measuring implicit and explicit attitudes toward foreign-accented speech. J. Lang. Soc. Psychol. 32(1), 3–20 (2013)
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)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights 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)