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Investigating the Effects of the Persuasive Source’s Social Agency Level and the Student’s Profile to Overcome the Cognitive Dissonance

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Book cover Social Robotics (ICSR 2016)

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

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Abstract

Educational robots are regarded as beneficial tools in education due to their capabilities of improving learning motivation. Using cognitive dissonance as a teaching tool has been popular in science education too. A considerable number of researchers have argued that cognitive dissonance has an important role in the student’s attitudes change. This paper presents a design for a cutting-edge experiment where we describe a procedure that induces cognitive dissonance. We propose to use an educational robot that helps the student overcome the cognitive dissonance during science learning. We make the difference between students that base their decisions on thinking (though-minded) and those that mostly base their decisions on feeling (relational). The main mission of the study was to implicitly lead students to evolve a positive implicit attitude supporting redoing difficult scientific exercises to understand one’s errors and to avoid learned helplessness. Based on the assumption that relational students are emotional (easily alienated), we investigate whether they are easy to be persuaded in comparison to though-minded students. Also, we verify whether it is possible to consider an educational robot for such a mission. We compare different persuasive sources (tablet showing a persuasive text, an animated robot and a human) encouraging the student to strive for cognitive closure, to verify which of these sources leads to better implicit attitude supporting defeating one’s self to assimilate difficult scientific exercises. Finally, we explore which of the persuasive sources better fits each of both student’s profiles.

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Notes

  1. 1.

    www.ald.softbankrobotics.com/en/solutions/education-research.

  2. 2.

    www.vgocom.com.

  3. 3.

    The student will avoid science learning.

  4. 4.

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

  5. 5.

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

  6. 6.

    The student thinks that the answer afforded by the book is incorrect.

  7. 7.

    By measuring the implicit and explicit attitudes, we can verify whether it was established for a long term basis.

  8. 8.

    Typical errors in human social judgment that are caused by systemic use of cognitive strategies.

  9. 9.

    In decision-making, the weight given to possible losses is greater than possible gains.

  10. 10.

    goo.gl/forms/fzpCl4onDRG2s9zE2.

  11. 11.

    This is to measure the student’s explicit attitude. We just ask respondents to think about and report their attitudes.

  12. 12.

    Forewarning often produces resistance to persuasion.

  13. 13.

    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 (it is the planned behavior in our case).

  14. 14.

    This is important to verify whether the student is convinced about the fact 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.

  15. 15.

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

  16. 16.

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

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Correspondence to Khaoula Youssef .

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Youssef, K., Ham, J., Okada, M. (2016). Investigating the Effects of the Persuasive Source’s Social Agency Level and the Student’s Profile 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_12

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  • DOI: https://doi.org/10.1007/978-3-319-47437-3_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47436-6

  • Online ISBN: 978-3-319-47437-3

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