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EduRobot Taxonomy

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Smart Learning with Educational Robotics

Abstract

For many years you could count the number of companies selling education robots on two hands. Since 2010, we’ve seen this number grow at an ever-increasing rate. What do these robots have in common and how do they differ? Reading the literature focused on robots in education, we see authors using different words for the same idea. We need a standard grammar. Education robots involve multiple strands of intellectual effort, for example, from psychology to teaching practice and classroom assessment to high-stakes testing. Trying to fit all these ideas into one theory isn’t the way to solve the problem. We need to isolate and explore different features. EduRobot is one strand: it’s concerned with the nature of robots – not the way we use them or their benefits – other papers do that. This chapter briefly explains EduRobot, its ideas and arguments before using it to classify some of the robots cited in this book.

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Notes

  1. 1.

    We’re actively seeking contributions from other parts of the world; contact info@robots-for-education.com if you’re interested.

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Correspondence to Dave Catlin .

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Catlin, D., Kandlhofer, M., Cabibihan, JJ., Angel-Fernandez, J., Holmquist, S., Csizmadia, A.P. (2019). EduRobot Taxonomy. In: Daniela, L. (eds) Smart Learning with Educational Robotics. Springer, Cham. https://doi.org/10.1007/978-3-030-19913-5_15

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  • DOI: https://doi.org/10.1007/978-3-030-19913-5_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-19912-8

  • Online ISBN: 978-3-030-19913-5

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