Skip to main content
Log in

Making Sense by Building Sense: Kindergarten Children’s Construction and Understanding of Adaptive Robot Behaviors

  • Published:
International Journal of Computers for Mathematical Learning Aims and scope Submit manuscript

Abstract

This study explores young children’s ability to construct and explain adaptive behaviors of a behaving artifact, an autonomous mobile robot with sensors. A central component of the behavior construction environment is the RoboGan software that supports children’s construction of spatiotemporal events with an a-temporal rule structure. Six kindergarten children participated in the study, three girls and three boys. Activities and interviews were conducted individually along five sessions that included increasingly complex construction tasks. It was found that all of the children succeeded in constructing most such behaviors, debugging their constructions in a relatively small number of cycles. An adult’s assistance in noticing relevant features of the problem was necessary for the more complex tasks that involved four complementary rules. The spatial scaffolding afforded by the RoboGan interface was well used by the children, as they consistently used partial backtracking strategies to improve their constructions, and employed modular construction strategies in the more complex tasks. The children’s explanations following their construction usually capped at one rule, or two condition-action couples, one rule short of their final constructions. With respect to tasks that involved describing a demonstrated robot’s behavior, in describing their constructions, explanations tended to be more rule-based, complex and mechanistic. These results are discussed with respect to the importance of making such physical/computational environments available to young children, and support of young children’s learning about such intelligent systems and reasoning in developmentally-advanced forms.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  • Ackermann, E. (1991). The agency model of transactions: Towards an understanding of children’s theory of control. In J. Montangero & A. Tryphon (Eds.), Psychologie genetique et sciences cognitives. Geneva: Fondation Archives Jean Piaget.

    Google Scholar 

  • Ackermann, E. (1996). Perspective-taking and object construction. In Y. Kafai & M. Resnick (Eds.), Constuctionism in practice: Designing, thinking, and learning in a digital world (pp. 25–37). Mahwah, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Bar-Yam, Y. (1997). Dynamics of complex systems, The Advanced Book Program. Reading, MA: Addison-Wesley.

    Google Scholar 

  • Begel, A. (1996). LogoBlocks: A graphical programming language for interacting with the world. Cambridge, MA: Electrical Engineering and Computer Science Department. MIT.

    Google Scholar 

  • Bernestein, D., & Crowley, K. (2008). Searching for signs if intelligent life: An investigation of young children’s’ beliefs about robot intelligence. Journal of the Learning Sciences, 17, 225–257.

    Article  Google Scholar 

  • Bers, M. U., & Portsmore, M. (2005). Teaching partnerships: Early childhood and engineering student teaching math and science through robotics. Journal of Science Education and Technology, 14(1), 59–73.

    Article  Google Scholar 

  • Bilotta, E., & Pantano, P. (2000). Some problems of programming in robotics. In A. Blackwell & E. Bilotta (Eds.), Proceedings of PPIG 12. Italy: Cozenza.

    Google Scholar 

  • Bloom, P. (1996). Intention, history, and artifact concepts. Cognition, 60, 1–29.

    Article  Google Scholar 

  • Chi, M. T. H. (2005). Commonsense conceptions of emergent processes: Why some misconceptions are robust. Journal of the Learning Sciences, 14(2), 161–199.

    Article  Google Scholar 

  • Clements, D. (1990). Metacomponential development in a logo programming environment. Journal of Educational Psychology, 82(1), 141–149.

    Article  Google Scholar 

  • Clements, D. (1999). The future of educational computing research: The case of computer programming. Information Technology in Childhood Education, 1, 147–179.

    Google Scholar 

  • Cuban, L. (1993). Computers meet classroom: Classroom wins. The Teachers College Record, 95(2), 185–210.

    Google Scholar 

  • Defeyter, G. (2003). Acquiring an understanding of design: Evidence from children’s insight problem solving. Cognition, 89, 133–155.

    Article  Google Scholar 

  • Diesendruck, G., Hammer, R., & Catz, O. (2003). Mapping the similarity space of children’s and adults’ artifact categories. Cognitive Development, 18, 217–231.

    Google Scholar 

  • Fagin, B., & Merkle, L. (2002). Quantitative analysis of the effects of robots on introductory computer science education. ACM Journal of Educational Resources in Computing, 2(4), 1–18.

    Article  Google Scholar 

  • Flavell, J. H., Miller, P. H., & Miller, S. A. (1993). Cognitive Development (Third ed.). New Jersey: Prentice Hall.

    Google Scholar 

  • Frye, D., Zelazo, P. D., Brooks, P. J., & Samuels, M. C. (1996). Inference and action in early causal reasoning. Developmental Psychology, 32(1), 120–131.

    Article  Google Scholar 

  • Fujita, M., Kitano, H., & Doi, T. T. (2000). Robot entertainment. In A. Druin & J. Hendler (Eds.), Robots for kids: Exploring new technologies for learning (pp. 37–72). San-Francisco, California: Morgan Kaufmann Publishers.

    Google Scholar 

  • Gelman, S. A., & Opfer, J. E. (2004). Development of the Animate-Inanimate Distinction. In U. Gowwami (Ed.), Blackwell handbook of childhood cognitive development. London: Wiley-Blackwell.

    Google Scholar 

  • Granott, N. (1991). Puzzled minds and weird creatures: Phases in the spontaneous process of knowledge construction. In I. Harel & S. Papert (Eds.), Constructionism (pp. 295–310). Norwood, NJ: Ablex Publishing Corporation.

    Google Scholar 

  • Horn, M., Solovey, E., Crouser, R., & Jacob, R. (2009). Comparing the use of tangible and graphical programming languages for informal science education. Proceedings of CHI 2009. Boston, MA: ACM.

    Google Scholar 

  • Jacobson, M. J. (2001). Problem solving, cognition, and complex systems: Differences between experts and novices. Complexity, 6(3), 41–49.

    Article  Google Scholar 

  • Jipson, J. L., & Gelman, S. A. (2007). Robots and rodents: Children’s inferences about living and nonliving kinds. Child Development, 78(6), 1675–1688.

    Article  Google Scholar 

  • Jonassen, D. H. (Ed.). (2004). Handbook of research on educational communications and technology (2nd ed.). Mahwah, NJ: Lawrence Erlbaum Associates.

  • Kahn, K. (2004). ToonTalk—steps towards ideal computer-based learning environments. In M. Tokoro & L. Steels (Eds.), A learning zone of one’s own: Sharing representations and flow in collaborative learning environments (pp. 259–270). Amsterdam, The Netherlands: IOS Press Inc.

    Google Scholar 

  • Kelleher, C., & Pausch, R. (2005). Lowering the barriers to programming. ACM Computing Surveys, 37(2), 83–137.

    Article  Google Scholar 

  • Kemler Nelson, D. G., & 11 Swarthmore College Students. (1995). Principle-based inferences in young children’s categorization: Revisiting the impact of function on the naming of artifacts. Cognitive Development, 10, 347–380.

    Article  Google Scholar 

  • Klahr, D. (1985). Solving problems with ambiguous subgoal ordering: Preschoolers’ performance. Child Development, 56, 940–952.

    Article  Google Scholar 

  • Klahr, D., Fay, A. L., & Dunbar, K. (1993). Heuristics for scientific experimentation: A developmental study. Cognitive Psychology, 25, 111–146.

    Article  Google Scholar 

  • Klopfer, E. (2003). Technologies to support the creation of complex systems models—using StarLogo software with students. Biosystems, 71(1–2), 111–122.

    Article  Google Scholar 

  • Klopfer, E., Yoon, S., & Perry, J. (2005). Using palm technology in participatory simulations of complex systems: A new take on ubiquitous and accessible mobile computing. Journal of Science Education and Technology, 14(3), 285–297.

    Article  Google Scholar 

  • Kuhn, D. (1989). Children and adults as intuitive scientists. Psychological Review, 96, 674–689.

    Article  Google Scholar 

  • Lehrer, R., & Schauble, L. (1998). Reasoning about structure and function: Children’s conceptions of gears. Journal of Research in Science Teaching, 35(1), 3–25.

    Article  Google Scholar 

  • Levy, S., & Mioduser, D. (2008). Does it “want” or “was it programmed to…”? Kindergarten children’s explanations of an autonomous robot’s adaptive functioning. International Journal of Technology and Design Education, 18(3), 337–359.

    Article  Google Scholar 

  • Levy, S., & Mioduser, D. (2010). Approaching complexity through planful play: Kindergarten children’s strategies in constructing an autonomous robot’s behavior. International Journal of Computers in Mathematical Learning, 15(1), 21–43.

    Article  Google Scholar 

  • Levy, S. T., & Wilensky, U. (2009). Students’ learning with the connected chemistry (CC1) curriculum: Navigating the complexities of the particulate world. Journal of Science Education and Technology, 18(3), 243–254.

    Article  Google Scholar 

  • Macchiusi, L. (1997). Children, robotics and problem solving: Technology in the early childhood classroom. Australian Educational Computing, 12(2), 24–31.

    Google Scholar 

  • Maddocks, R. (2000). Bolts from the blue: How large dreams can become real products. In A. Druin & J. Hendler (Eds.), Robots for kids: Exploring new technologies for learning (pp. 111–156). San-Francisco, California: Morgan Kaufmann Publishers.

    Google Scholar 

  • Matan, A., & Carey, S. (2001). Developmental changes within the core of artifact concept. Cognition, 78, 1–26.

    Article  Google Scholar 

  • Metz, K. (1991). Development of explanation: Incremental and fundamental change in children’s physical knowledge. Journal of Research in Science Teaching, 28(9), 785–797.

    Article  Google Scholar 

  • Mioduser, D., Levy, S. T., & Talis, V. (2009). Episodes to scripts to rules: Concrete-abstractions in kindergarten children’s explanations of a robot’s behaviors. Journal of Technology and Design Education, 19(1), 15–36.

    Article  Google Scholar 

  • Monroy-Hernandez, A., & Resnick, M. (2008). Empowering kids to create and share programmable media. Interactions, 15(2), 50–53.

    Article  Google Scholar 

  • Montemayor, J., Druin, A., & Hendler, J. (2000). PETS: A personal electronic teller of stories. In A. Druin & J. Hendler (Eds.), Robots for kids: Exploring new technologies for learning (pp. 73–110). San-Francisco, California: Morgan Kaufmann Publishers.

    Google Scholar 

  • Morgado, L., Cruz, M., & Kahn, K. (2006). Radia Perlman—A pioneer of young children computer programming. In A. Mendez-Vilas, S. Martin, J. Mesa-Gonzalez, & J. A. Mesa-Gonzalez (Eds.), Current developments in technology-assisted education. Proceedings of m-ICTE 2006 (pp. 1903–1908). Badajoz, Spain: Formatex.

  • Okita, S. Y., & Schwartz, D. L. (2006). Young children’s understanding of animacy and entertainment robots. International Journal of Humanoid Robotics, 3(3), 393–412.

    Article  Google Scholar 

  • Papert, S. (1980/1993). Mindstorms: Children, computers, and powerful ideas (1st and 2nd ed.). Cambridge, MA: Basic Books.

    Google Scholar 

  • Papert, S. (1987). Computer criticism vs. technocentric thinking. Educational Researcher, 16(1), 22–30.

    Google Scholar 

  • Papert, S., & Solomon, C. (1971). Twenty things to do with a computer. Artificial Intelligence Memo No. 248, Logo Memo No. 3. Cambridge, MA: MIT.

    Google Scholar 

  • Pea, R. D., Kurland, D. M., & Hawkins, J. (1985). Logo and the development of thinking skills. In M. Chen & W. Paisley (Eds.), Children and microcomputers: Formative studies (pp. 193–212). Beverly Hills, CA: Sage.

    Google Scholar 

  • Perlman, R. (1974). TORTIS—toddlers own recursive turtle interpreter system. A.I. Memo 311 - Logo Memo 9. Cambridge, MA: MIT A.I. Lab.

    Google Scholar 

  • Piaget, J. (1956). The child’s conception of physical causality. New Jersey: Littlefield Adams and Co.

    Google Scholar 

  • Piaget, J., & Inhelder, B. (1972). Explanations of Machines. The Child’s conception of physical causality (pp. 195–236). New-Jersey: Littlefield Adams and Co.

    Google Scholar 

  • Resnick, M. (1998). Technologies for lifelong kindergarten. Educational Technology Research and Development, 46(4), 43–55.

    Article  Google Scholar 

  • Resnick, M. & Wilensky, U. (1993). Beyond the deterministic, centralized mindsets: New thinking for new sciences. Paper presented at the annual meeting of the American Educational Research Association, Atlanta, GA.

  • Resnick, M., & Wilensky, U. (1998). Diving into complexity: Developing probabilistic decentralized thinking through role-playing activities. The Journal of the Learning Sciences, 7, 153–172.

    Article  Google Scholar 

  • Scaife, M., & van Duuren, M. (1995). Do computers have brains? What children believe about intelligent artefacts. British Journal of Developmental Psychology, 13, 367–377.

    Google Scholar 

  • Schauble, L. (1990). Belief revision in children: The role of prior knowledge and strategies for generating evidence. Journal of Experimental Child Psychology, 49, 31–57.

    Article  Google Scholar 

  • Schweikardt, E., & Gross, M. (2007). A brief survey of distributed computational toys. Proceedings of the first IEEE international workshop on digital game and intelligent toy enhanced learning (DIGITEL’07), pp. 57–64.

  • Siegler, R. S. (1986). Children’s thinking. Englewood-Cliffs, NJ: Prentice-Hall International.

    Google Scholar 

  • Siegler, R. S., & Chen, Z. (1998). Developmental differences in rule learning: A microgenetic analysis. Cognitive Psychology, 36, 273–310.

    Article  Google Scholar 

  • Slotta, J. D., & Chi, M. T. H. (2006). Helping students understand challenging topics in science through ontology training. Cognition and Instruction, 24(2), 261–289.

    Article  Google Scholar 

  • Sobel, D. M., Tenenbaum, J. B., & Gopnik, A. (2004). Children’s causal inferences from indirect evidence: Backwards blocking and Bayesian reasoning in preschoolers. Cognitive Science, 28, 303–333.

    Google Scholar 

  • Soloway, E., Norris, C., Blumenfeld, P., Fishman, B., Kracjik, J., & Marx, R. (2001). Log on education handheld devices are ready-at-hand. Communications of the ACM, 44, 6.

    Article  Google Scholar 

  • Turkle, S. (1984). The second self: Computers and the human spirit. New York: Simon and Schuster.

    Google Scholar 

  • van Duuren, M. A., & Scaife, M. (1995). How do children represent intelligent technology? European Journal of Psychology of Education, 10, 289–301.

    Article  Google Scholar 

  • van Duuren, M., & Scaife, M. (1996). Because a robot’s brain hasn’t got a brain, it just controls itself: Children’s attribution of brain related behavior to intelligent artifacts. European Journal of Psychology of Education, 11(4), 365–376.

    Article  Google Scholar 

  • Wilensky, U., & Reisman, K. (2006). Thinking like a wolf, a sheep or a firefly: Learning biology through constructing and testing computational theories—an embodied modeling approach. Cognition & Instruction, 24(2), 171–209.

    Article  Google Scholar 

  • Wilensky, U., & Resnick, M. (1999). Thinking in levels: A dynamic systems perspective to making sense of the world. Journal of Science Education and Technology, 8(1), 3–19.

    Article  Google Scholar 

  • Wyeth, P., & Purchase, H. (2000). Programming without a computer: A new interface for children under eight. Proceedings of the first australasian user interface conference, p. 141, Australia. Retrieved at: http://www.cl.cam.ac.uk/users/afb21/CognitiveDimensions/workshop2005/Wright_draft_paper.pdf.

  • Yelland, N. (1995). Mindstorms or a storm in a teacup? A review of research with Logo. International Journal of Mathematics Education Science and Technology, 26(6), 853–869.

    Article  Google Scholar 

  • Zuckerman, O., Arida, S., & Resnick, M. (2005). Extending tangible interfaces for education: Digital Montessori-inspired manipulatives. Proceedings of CHI 2005. ACM Press, pp. 859–868.

Download references

Acknowledgments

We gratefully thank Dr. Vadim Talis, who collaborated with us in designing the RoboGan environment and in conducting the research with the children.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Sharona T. Levy.

Appendix

Appendix

See Table 6.

Table 6 Description and construction tasks

Rights and permissions

Reprints and permissions

About this article

Cite this article

Mioduser, D., Levy, S.T. Making Sense by Building Sense: Kindergarten Children’s Construction and Understanding of Adaptive Robot Behaviors. Int J Comput Math Learning 15, 99–127 (2010). https://doi.org/10.1007/s10758-010-9163-9

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10758-010-9163-9

Keywords

Navigation