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Learner Attitudes Towards Humanoid Robot Tutoring Systems: Measuring of Cognitive and Social Motivation Influences

Maya Dimitrova, Hiroaki Wagatsuma, Gyanendra Nath Tripathi, Guangyi Ai

Source Title: Cyber-Physical Systems for Social Applications

ISBN13: 9781522578796|ISBN10: 152257879X|ISBN13 Softcover: 9781522593454|EISBN13: 9781522578802

DOI: 10.4018/978-1-5225-7879-6.ch004

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MLA

Dimitrova, Maya, et al. "Learner Attitudes Towards Humanoid Robot Tutoring Systems: Measuring of Cognitive and Social Motivation Influences." Cyber-Physical Systems for Social Applications, edited by Maya Dimitrova and Hiroaki Wagatsuma, IGI Global, 2019, pp. 1-24. https://doi.org/10.4018/978-1-5225-7879-6.ch004

APA

Dimitrova, M., Wagatsuma, H., Tripathi, G. N., & Ai, G. (2019). Learner Attitudes Towards Humanoid Robot Tutoring Systems: Measuring of Cognitive and Social Motivation Influences. In M. Dimitrova & H. Wagatsuma (Eds.), Cyber-Physical Systems for Social Applications (pp. 1-24). IGI Global. https://doi.org/10.4018/978-1-5225-7879-6.ch004

Chicago

Dimitrova, Maya, et al. "Learner Attitudes Towards Humanoid Robot Tutoring Systems: Measuring of Cognitive and Social Motivation Influences." In Cyber-Physical Systems for Social Applications, edited by Maya Dimitrova and Hiroaki Wagatsuma, 1-24. Hershey, PA: IGI Global, 2019. https://doi.org/10.4018/978-1-5225-7879-6.ch004

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Abstract

A novel framework for investigation of the learner attitude towards a humanoid robot tutoring system is proposed in the chapter. The theoretical approach attempts to understand both the cognitive motivation as well as the social motivation of the participants in a teaching session, held by a robotic tutor. For this aim, a questionnaire is delivered after the eye tracking experiment in order to record the type and amount of the learned material as well as the social motivation of the participants. The results of the experiments show significant effects of both cognitive and social motivation influences. It has been shown that cognitive motivation can be observed and analyzed on a very individual level. This is an important biometric feature and can be used to recognize individuals from patterns of viewing behaviors in a lesson. Guidelines, drawn from first-person accounts of learner participation in the study, are also formulated for achieving more intuitive interactions with humanoid robots intended to perform social jobs like being teachers or advisors.