Top

Journal of Science Education and Technology

Published in:

15-09-2018

Fostering Analogical Reasoning Through Creating Robotic Models of Biological Systems

Authors: Dan Cuperman, Igor M. Verner

Published in: Journal of Science Education and Technology | Issue 2/2019

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

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.

previous article When Makerspaces Meet School: Negotiating Tensions Between Instruction and Construction

next article Implementing Engineering in Diverse Upper Elementary and Middle School Science Classrooms: Student Learning and Attitudes

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Abrantes, P. (1999). Analogical reasoning and modeling in the sciences. Foundations of Science, 4(3), 237–270.CrossRef

Beer, C. G. (1980). Perspectives on animal behavior comparisons. In M. H. Bornstein (Ed.), Comparative methods in psychology (pp. 17–64). NJ: Erlbaum Hillsdale.

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.

Bonnardel, N. (2000). Towards understanding and supporting creativity in design: Analogies in a constrained cognitive environment. Knowledge-Based Systems, 13(7–8), 505–513.CrossRef

Bonnardel, N., & Marmèche, E. (2004). Evocation processes by novice and expert designers: Towards stimulating analogical thinking. Creativity and Innovation Management, 13(3), 176–186.CrossRef

Collins, A., & Gentner, D. (1987). How people construct mental models. In D. Holland & N. Quinn (Eds.), Cultural models in language and thought (pp. 243–265). New York: Cambridge University Press.CrossRef

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.

Daugherty, J., & Mentzer, N. (2008). Analogical reasoning in the engineering design process and technology education applications. Journal of Technology Education, 19(2), 7–21.

Dean, J. (1998). Animats and what they can tell us. Trends in cognitive sciences, 2(2), 60–67.CrossRef

Duit, R. (1991). On the role of analogies and metaphors in learning science. Science Education, 75(6), 649–672.CrossRef

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.

Fu, K., Moreno, D., Yang, M., & Wood, K. L. (2014). Bio-inspired design: An overview investigating open questions from the broader field of design-by-analogy. Journal of Mechanical Design, 136(11), 111102.CrossRef

Gardner, R. D. (2016). Teaching Biology with Extended Analogies. The American Biology Teacher, 78(6), 512–514.

Gentner, D. (1983). Structure-mapping: A theoretical framework for analogy. Cognitive Science, 7(2), 155–170.CrossRef

Gentner, D. (1988). Metaphor as structure mapping: The relational shift. Child development 59, 47–59.

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.

Gentner, D., & Markman, A. B. (1997). Structure mapping in analogy and similarity. American Psychologist, 52(1), 45–56.CrossRef

Gentner, D., & Smith, L. (2012). Analogical reasoning. In V. S. Ramachandran (Ed.), Encyclopedia of human behavior (2nd ed., pp. 130–136). Oxford, UK: Elsevier.CrossRef

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.

Giere, R. G. (1999). Science without laws. Chicago: University of Chicago Press.

Gilbert, J. K. (2004). Models and modeling: Routes to more authentic science education. International Journal of Science and Mathematics Education, 2(2), 115–130.CrossRef

Gilbert, J. K., & Justi, R. (2016). Modeling-based teaching in science education. Cham, Switzerland: Springer International Publishing.

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.CrossRef

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.

Gobert, J. D., & Buckley, B. C. (2000). Introduction to model-based teaching and learning in science education. International Journal of Science Education, 22(9), 891–894.CrossRef

Goswami, U. (2013). Analogical reasoning in children. London: Psychology Press.CrossRef

Griffiths, D. (2016). The age of analogy: Science and literature between the Darwins. Baltimore: JHU Press.

Gupta, S. K., Bejgerowski, W., Gerdes, J., Hopkins, J., Lee, L., Narayanan, M. S., Mendel, F., & Krovi, V. (2014). An engineering approach to utilizing bio-inspiration in robotics applications. In Biologically inspired design (pp. 245–267). London: Springer.CrossRef

Harrison, A. G., & Treagust, D. F. (1998). Modeling in science lessons: Are there better ways to learn with models? School Science and Mathematics, 98(8), 420–429.CrossRef

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.

Hernández, M. I., Couso, D., & Pintó, R. (2015). Analyzing students’ learning progressions out a teaching sequence on acoustic properties of materials with a model-based inquiry approach. Journal of Science Education and Technology, 24(2–3), 356–377.CrossRef

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.

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.

Hirose, S., & Yamada, H. (2009). Snake-like robots [tutorial]. IEEE Robotics & Automation Magazine, 16(1), 88–98.CrossRef

Hofstein, A., & Lunetta, V. N. (2004). The laboratory in science education: Foundations for the twenty-first century. Science Education, 88(1), 28–54.CrossRef

Houkes, W. (2009). The nature of technological knowledge. In A. Meijers (Ed.), Philosophy of technology and engineering sciences (pp. 309–350). Burlington, MA: North Holland.CrossRef

International Technology Education Association (ITEA). (2007). Standards for technological literacy: Content for the study of technology. Reston, VA: Author.

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.

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.

Laut, J., Bartolini, T., & Porfiri, M. (2015). Bioinspiring an interest in STEM. IEEE Transactions on Education, 58(1), 48–55.CrossRef

Lawson, A. E. (2005). What is the role of induction and deduction in reasoning and scientific inquiry? Journal of Research in Science Teaching, 42(6), 716–740.CrossRef

LEGO Education (2018). Retrieved April, 2018 from https://education.lego.com/en-us.

Lewis, T. (2006). Design and inquiry: Bases for an accommodation between science and technology education in the curriculum? Journal of Research in Science Teaching, 43(3), 255–281.CrossRef

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.

Miller, G. A. (2003). The cognitive revolution: A historical perspective. Trends in Cognitive Sciences, 7(3), 141–144.CrossRef

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.

Nagel, J. K., Nagel, R. L., Stone, R. B., & McAdams, D. A. (2010). Function-based, biologically inspired concept generation. Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 24(4), 521–535.CrossRef

National Research Council (NRC). (1996). National science education standards. Washington, DC: National Academy Press.

Nersessian, N. J. (2002). The cognitive basis of model-based reasoning in science. In P. Carruthers, S. Stich, & M. Siegal (Eds.), The cognitive basis of science (pp. 133–153). Cambridge, UK: Cambridge University Press.CrossRef

Nersessian, N. J. (2008). Creating scientific concepts (pp. 9–12). Cambridge, MA: MIT press.CrossRef

NGSS Lead States. (2013). Next generation science standards: For states, by states. Washington, DC: The National Academies Press.

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.

Papert, S. (1980). Mindstorms: Children, computers, and powerful ideas. New York: Basic Books.

Penner, D., Giles, N., Lehrer, R., & Schauble, L. (1997). Building functional models: Designing an elbow. Journal of Research in Science Teaching, 34(2), 125–147.CrossRef

Rattermann, M. J., & Gentner, D. (1998). More evidence for a relational shift in the development of analogy: Children's performance on a causal-mapping task. Cognitive Development, 13(4), 453–478.

Resnick, M. (1999). Decentralized modeling and decentralized thinking. In W. Feurzeig & N. Roberts (Eds.), Modeling and simulation in science and mathematics education (pp. 114–137). New York, NY: Springer-Verlag.CrossRef

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.

Richland, L. E., Morrison, R. G., & Holyoak, K. J. (2006). Children’s development of analogical reasoning: Insights from scene analogy problems. Journal of Experimental Child Psychology, 94(3), 249–273.CrossRef

ROBOTIS (2018). Retrieved April, 2018 from http://en.robotis.com/.

Ropohl, G. (1997). Knowledge types in technology. International Journal of Technology and Design Education, 7(1–2), 65–72.CrossRef

Rusk, N., Resnick, M., Berg, R., & Pezalla-Granlund, M. (2008). New pathways into robotics: Strategies for broadening participation. Journal of Science Education and Technology, 17(1), 59–69.CrossRef

Saito, M., Fukaya, M., & Iwasaki, T. (2002). Modeling, analysis, and synthesis of serpentine locomotion with a multilink robotic snake. IEEE Control Systems Magazine, 22(1), 64–81.CrossRef

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.

Sibley, D. F. (2009). A cognitive framework for reasoning with scientific models. Journal of Geoscience Education, 57(4), 255–263.CrossRef

Steadman, P. (2008). The evolution of designs: Biological analogy in architecture and the applied arts. Abingdon: Routledge.CrossRef

Treagust, D. F. (1997). Analogies in biology education: A contentious issue. The American Biology Teacher, 59(5), 282–287.

Vendetti, M. S., Matlen, B. J., Richland, L. E., & Bunge, S. A. (2015). Analogical reasoning in the classroom: Insights from cognitive science. Mind Brain Educ, 9(2), 100–106.CrossRef

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.

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.

Webb, B. (2001). Can robots make good models of biological behaviour? Behavioural and Brain Sciences, 24, 1033–1050.

Wilbers, J., & Duit, R. (2006). Post-festum and heuristic analogies. In Metaphor and analogy in science education (pp. 37–49). Netherlands: Springer.CrossRef

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.

Title: Fostering Analogical Reasoning Through Creating Robotic Models of Biological Systems
Authors: Dan Cuperman
Igor M. Verner
Publication date: 15-09-2018
Publisher: Springer Netherlands
Published in: Journal of Science Education and Technology / Issue 2/2019
Print ISSN: 1059-0145
Electronic ISSN: 1573-1839
DOI: https://doi.org/10.1007/s10956-018-9750-4

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Other articles of this Issue 2/2019

Implementation of Game-transformed Inquiry-based Learning to Promote the Understanding of and Motivation to Learn Chemistry

Squishing Circuits: Circuitry Learning with Electronics and Playdough in Early Childhood

Waves: Scaffolding Self-regulated Learning to Teach Science in a Whole-Body Educational Game

Implementing Engineering in Diverse Upper Elementary and Middle School Science Classrooms: Student Learning and Attitudes

When Makerspaces Meet School: Negotiating Tensions Between Instruction and Construction

The Impact of Student-Constructed Animation on Middle School Students’ Learning about Plate Tectonics

Premium Partners