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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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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APA

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 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9979 LNAI, pp. 115–125). Springer Verlag. https://doi.org/10.1007/978-3-319-47437-3_12

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