Fostering Analogical Reasoning Through Creating Robotic Models of Biological Systems
This article considers student analogical reasoning associated with learning practice in creating bio-inspired robots. The study was in the framework of an outreach course for middle school students. Fifty eighth and ninth graders performed inquiries into behavior and locomotion of snakes and designed robotic models using the BIOLOID robot construction kit. We analyzed the interdomain analogies between biological and robotic systems elaborated by the students and evaluated the contribution of the analogies to the integrated learning of biology and robotics. The analogies expressed by the students at different stages of the course were collected and categorized, and their use in knowledge construction was traced. The study indicated that students’ reasoning evolved with learning, towards an increased share of deeper analogies at the end of the course. We found that analogical reasoning helped students to construct knowledge and guided their inquiry and design activities. In the proposed framework, the students learn to inquire into biological systems, generate analogies, and use them for developing and improving robotic systems.
KeywordsModel-based learning Design and inquiry Bio-inspired robotics Analogical reasoning Interdomain analogies Bioloid
This study was supported by the PTC Inc. grant.
Compliance with Ethical Standards
Conflict of Interest
The authors declare that they have no conflict of interest.
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
Informed consent was obtained from all individual participants included in the study.
- Beer, C. G. (1980). Perspectives on animal behavior comparisons. In M. H. Bornstein (Ed.), Comparative methods in psychology (pp. 17–64). NJ: Erlbaum Hillsdale.Google Scholar
- Bers, M. (2010). When robots tell a story about culture...and children tell a story about learning. In Y. Nicola (Ed.), Contemporary perspective on early childhood education (pp. 227–247). Maidenhead, UK: Open University Press.Google Scholar
- Cuperman, D., & Verner, I. M. (2013). Learning bio-inspired inquiry and design of robotic models. Proceedings of the Israeli Conference on Robotics (ICR 2013). Tel Aviv, Israel.Google Scholar
- Daugherty, J., & Mentzer, N. (2008). Analogical reasoning in the engineering design process and technology education applications. Journal of Technology Education, 19(2), 7–21.Google Scholar
- Dunbar, K. (2001). The analogical paradox: Why analogy is so easy in naturalistic settings yet so difficult in the psychological laboratory. In D. Gentner, K. J. Holyoak, & B. N. Kokinov (Eds.), The analogical mind: Perspectives from cognitive science (pp. 313–334). Cambridge, MA, US: The MIT Press.Google Scholar
- Gentner, D. (2002). Mental models, psychology of. In N. J. Smelser & P. B. Bates (Eds.), International encyclopedia of the social and behavioral sciences (pp. 9683–9687). Amsterdam: Elsevier Science.Google Scholar
- Gentner, D., & Smith, L. A. (2013). Analogical learning and reasoning. In D. Reisberg (Ed.), The Oxford handbook of cognitive psychology (pp. 668–681). New York, NY: Oxford University Press.Google Scholar
- Giere, R. G. (1999). Science without laws. Chicago: University of Chicago Press.Google Scholar
- Gilbert, J. K., & Justi, R. (2016). Modeling-based teaching in science education. Cham, Switzerland: Springer International Publishing.Google Scholar
- Gilbert, J. K., Boulter, C. J., & Elmer, R. (2000). Positioning models in science education and in design and technology education. In J. K. Gilbert & C. J. Boulter (Eds.), Developing models in science education (pp. 3–17). Dordrecht, the Netherlands: Kluwer Academic Publishers.CrossRefGoogle Scholar
- Glynn, S. M. (2008). Making science concepts meaningful to students: Teaching with analogies. In S. Mikelskis-Seifert & U. Ringelband (Eds.), Four decades of research in science education: From curriculum development to quality improvement (pp. 113–125). Münster, Germany: Waxmann.Google Scholar
- Griffiths, D. (2016). The age of analogy: Science and literature between the Darwins. Baltimore: JHU Press.Google Scholar
- Harrison, A. G., & Treagust, D. F. (2006). Teaching and learning with analogies: Friend or foe?. In P. J. Aubusson, A. G. Harrison, & S. M. Ritchie (Eds.), Metaphor and analogy in science education (pp. 11–24). Dordrecht, the Netherlands: Springer.Google Scholar
- Hestenes, D. (1996). Modeling software for learning and doing physics. In C. Bernardini, C. Tarsitani, & M. Vincentini (Eds.), Thinking physics for teaching (pp. 25–66). New York, NY: Plenum.Google Scholar
- Hestenes, D. (2006). Notes for a modeling theory of science, cognition and physics education. In E. van den Berg, A. Ellermeijer, & O. Slooten (Eds.), Modelling in physics and physics education. The Netherlands: University of Amsterdam.Google Scholar
- International Technology Education Association (ITEA). (2007). Standards for technological literacy: Content for the study of technology. Reston, VA: Author.Google Scholar
- Klahr, D., Matlen, B., & Jirout, J. (2012). Children as scientific thinkers. In G. J. Feist & M. E. Gorman (Eds.), Handbook of the psychology of science. Berlin: Springer.Google Scholar
- Kolodner, J. L. (2009). Learning by design’s framework for promoting learning of 21st century skills. Presentation to the National Research Council Workshop on exploring the intersection of science education and the development of 21st century skills. Retrieved December, 2016 from: http://sites.nationalacademies.org/DBASSE/BOSE/DBASSE_080127.
- LEGO Education (2018). Retrieved April, 2018 from https://education.lego.com/en-us.
- Markman, A. B., & Wisniewski, E. J. (1997). Similar and different: The differentiation of basic-level categories. Journal of Experimental Psychology: Learning, Memory, and Cognition, 23(1), 54–70.Google Scholar
- Milrad, M. (2004). Learning with models and learning by modeling: Exploring the role of multiple representations using computational media, Proceedings of the Fourth IEEE International Conference on Advanced Learning Technologies (ICALT’04), (pp. 1092–1093), Joensuu, Finland.Google Scholar
- National Research Council (NRC). (1996). National science education standards. Washington, DC: National Academy Press.Google Scholar
- Nersessian, N. J. (2008). Creating scientific concepts (pp. 9–12). Cambridge, MA: MIT press.Google Scholar
- NGSS Lead States. (2013). Next generation science standards: For states, by states. Washington, DC: The National Academies Press.Google Scholar
- Norman, D. A. (2014). Some observations on mental models. In D. Gentner & A. L. Stevens (Eds.), Mental Models (pp. 7–14). New York: Psychology Press.Google Scholar
- Papert, S. (1980). Mindstorms: Children, computers, and powerful ideas. New York: Basic Books.Google Scholar
- Resnick, M., Martin, F., Berg, R., Borovoy, R. Colella, V., Kramer, K., & Silverman, B. (1998). Digital manipulatives: New toys to think with. In Proceedings of CHI ‘98, conference on human factors in computing systems (pp. 281–287). Los Angeles California.Google Scholar
- ROBOTIS (2018). Retrieved April, 2018 from http://en.robotis.com/.
- Schank, J. C., & Koehnle, T. J. (2007). Modeling complex biobehavioral systems. In M. D. Laubichler & G. B. Muller (Eds.), Modeling biology: Structures, behaviors, evolution (pp. 219–244). Cambridge, MA: MIT Press.Google Scholar
- Verner, I. M., & Cuperman, D. (2010). Learning by inquiry into natural phenomena and construction of their robotic representations. In H. Middleton (Ed.), Knowledge in technology education (pp. 171–177). Australia: Griffith Institute for Educational research, Griffith University, Brisbane.Google Scholar
- Verner, I., & Korchnoy, E. (2006). Experiential learning through designing robots and motion behaviors: A tiered approach. International Journal of Engineering Education, 22(4), 758–765.Google Scholar
- Webb, B. (2001). Can robots make good models of biological behaviour? Behavioural and Brain Sciences, 24, 1033–1050.Google Scholar
- Yuen, T., Stone, J., Davis, D., Gomez, A., Guillen, A., Tiger, E. P., & Boecking, M. (2016). A model of how children construct knowledge and understanding of engineering design within robotics focused contexts. International Journal of Research Studies in Educational Technology, 5(1), 3–15.Google Scholar