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Technology, Knowledge and Learning

, Volume 22, Issue 3, pp 443–463 | Cite as

Introducing Computational Thinking to Young Learners: Practicing Computational Perspectives Through Embodiment in Mathematics Education

  • Woonhee Sung
  • Junghyun Ahn
  • John B. Black
Original research

Abstract

A science, technology, engineering, and mathematics-influenced classroom requires learning activities that provide hands-on experiences with technological tools to encourage problem-solving skills (Brophy et al. in J Eng Educ 97(3):369–387, 2008; Matarić et al. in AAAI spring symposium on robots and robot venues: resources for AI education, pp 99–102, 2007). The study aimed to bring computational thinking, an applicable skill set in computer science, into existing mathematics and programming education in elementary classrooms. An essential component of computational thinking is the ability to think like a computer scientist when confronted with a problem (Grover and Pea in Educ Res 42(1):38–43. doi: 10.3102/0013189X12463051, 2013). Computational perspectives (Berland and Wilensky in J Sci Educ Technol 24(5):628–647. doi: 10.1007/s10956-015-9552-x, 2015) refer to the frame of reference programmers or computer scientists adopt when approaching a problem. The study examined the effects of taking computational perspectives through various degrees of embodied activities (i.e., full vs. low) on students’ achievement in mathematics and programming. The study employed a 2 (full vs. low embodiment) × 2 (with vs. without computational perspective taking) factorial condition to evaluate four learning conditions from a combination of embodiment and computational perspective-taking practice. The results from this experimental study (N = 66 kindergarten and first graders) suggest that full-embody activities combined with the practice of computational perspective-taking in solving mathematics problem improved mathematics understanding and programming skills as demonstrated in Scrath Jr. among novice young learners. Moreover, the practice of using a computational perspective significantly improved students’ understanding of core programming concepts regardless of the level of embodiment. The article includes recommendations for how to make the computational thinking process more concrete and relevant within the context of a standard curriculum, particularly mathematics.

Keywords

Computational thinking Embodied cognition Elementary education Programming Mathematics Computational perspectives STEM 

References

  1. Abrahamson, D., & Howison, M. (2010). Embodied artifacts: coordinated action as an object-to-think with. Denver, CO: In annual meeting of the American Educational Research Association.Google Scholar
  2. Abrahamson, D., & Lindgren, R. (2014). Embodiment and embodied design. In R. K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (2nd ed., pp. 358–376). Cambridge: Cambridge University Press. doi: 10.1017/CBO9781139519526.022.CrossRefGoogle Scholar
  3. Abrahamson, D., & Trninic, D. (2015). Bringing forth mathematical concepts: Signifying sensorimotor enactment in fields of promoted action. ZDM Mathematics Education, 47(2), 295–306.CrossRefGoogle Scholar
  4. Alimisis, D. (2013). Educational robotics: Open questions and new challenges. Themes in Science and Technology Education, 6(1), 63–71.Google Scholar
  5. Bamberger, J., & DiSessa, A. (2003). Music as embodied mathematics: A study of a mutually informing affinity. International Journal of Computers for Mathematical Learning, 8(8), 123–160. doi: 10.1023/B:IJCO.0000003872.84260.96.CrossRefGoogle Scholar
  6. Barsalou, L. W. (2008). Grounded cognition. Annual Review of Psychology, 59(1), 617–645. doi: 10.1146/annurev.psych.59.103006.093639.CrossRefGoogle Scholar
  7. Barsalou, L. W., Niedenthal, P. M., Barbey, A. K., & Ruppert, J. A. (2003). Social embodiment. Psychology of Learning and Motivation, 43, 43–92.CrossRefGoogle Scholar
  8. Berland, M., & Wilensky, U. (2015). Comparing virtual and physical robotics environments for supporting complex systems and computational thinking. Journal of Science Education and Technology, 24(5), 628–647. doi: 10.1007/s10956-015-9552-x.CrossRefGoogle Scholar
  9. Bers, M. U. (2008). Using robotic manipulatives to develop technological fluency in early childhood. In O. N. Saracho & B. Spodek (Eds.), Contemporary perspectives on science and technology in early childhood education, LAP 105–225. Greenwich, CT: Information Age Publishing Inc.Google Scholar
  10. Bers, M. U. (2010). The TangibleK robotics program: Applied computational thinking for young children. Early Childgood Research & Practice, 12(2), 1–20. Retrieved from http://ecrp.uiuc.edu/v12n2/bers.html.
  11. Black, J. B., Segal, A., Vitale, J., & Fadjo, C. (2012). Embodied cognition and learning environment design. Theoretical Foundations of Learning Environments, 2, 198–223.Google Scholar
  12. Booth, J. L., & Siegler, R. S. (2008). Numerical magnitude representations influence arithmetic learning. Child Development, 79(4), 1016–1031.CrossRefGoogle Scholar
  13. Brophy, S., Klein, S., Portsmore, M., & Rogers, C. (2008). Advancing engineering education in P-12 classrooms. Journal of Engineering Education, 97(3), 369–387.CrossRefGoogle Scholar
  14. Burke, Q. (2012). The markings of a new pencil: Introducing programming-as-writing in the middle school classroom. Journal of Media Literacy Education, 4(2), 121–135.Google Scholar
  15. Chan, M. S., & Black, J. B. (2006). Direct-manipulation animation: Incorporating the haptic channel in the learning process to support middle school students in science learning and mental model acquisition. In Proceedings of the 7th International Conference on Learning Sciences (pp. 64–70). Bloomington, IN.Google Scholar
  16. Clements, D. H., & Battista, M. T. (1989). Learning of geometric concepts in a Logo environment. Journal for Research in Mathematics Education, 20(5), 450–467.CrossRefGoogle Scholar
  17. Clements, D. H., & Gullo, D. F. (1984). Effects of computer programming on young children’s cognition. Journal of Educational Psychology, 76(6), 1051–1058.CrossRefGoogle Scholar
  18. Clements, D. H., & Sarama, J. (2002). The role of technology in early childhood learning. Teaching Children Mathematics, 8(6), 340–343.Google Scholar
  19. Einhorn, S. (2011). Micro-worlds, computational thinking, and 21st century learning.[White paper] Retrieved from http://el.media.mit.edu/logofoundation/resources/papers/pdf/computational_thinking.pdf.
  20. Ericsson, K. A., & Simon, H. A. (1980). Verbal reports as data. Psychological Review, 87(3), 215–251.CrossRefGoogle Scholar
  21. Fadjo, C. L. (2012). Developing Computational Thinking through Grounded Embodied Cognition (Unpublished doctoral dissertation). Columbia University, NY.Google Scholar
  22. Fadjo, C. L., Hallman Jr., G., Harris, R., & Black, J. B. (2009). Surrogate embodiment, mathematics instruction and video game programming. Paper presented at the World Conference on Educational Media and Technology, Honolulu, HI. http://www.editlib.org/p/31876.
  23. Fadjo, C., Lu, M., & Black, J. B. (2009). Instructional embodiment and video game programming in an after school program. Paper presented at the World Conference on Educational Media and Technology, Honolulu, HI. http://www.editlib.org/p/32064.
  24. Feurzeig, W., Papert, S., & Lawler, B. (2011). Programming-languages as a conceptual framework for teaching mathematics. Interactive Learning Environments, 19(5), 487–501. doi: 10.1080/10494820903520040.CrossRefGoogle Scholar
  25. Fryer, W. A. (2014). Hopscotch challenges: Learn to code on an iPad!. Retrieved from http://publications.wesfryer.com/index.php/archive/article/view/53.
  26. Glenberg, A. M. (2008). Toward the integration of bodily states, language, and action. In G. R. Semin & E. R. Smith (Eds.), Embodied grounding: Social, cognitive, affective, and neuroscientific approaches (pp. 43–70). New York: Cambridge University Press.CrossRefGoogle Scholar
  27. Glenberg, A. M. (2010). Embodiment as a unifying perspective for psychology. Wiley Interdisciplinary Reviews: Cognitive Science, 1(4), 586–596.Google Scholar
  28. Grover, S., & Pea, R. (2013). Computational thinking in K–12: A review of the state of the field. Educational Researcher, 42(1), 38–43. doi: 10.3102/0013189X12463051.CrossRefGoogle Scholar
  29. Hallman, G., Paley, I., Han, I., & Black, J. (2009). Possibilities of haptic feedback simulation for physics learning. In Proceedings of world conference on educational multimedia, hypermedia and telecommunications (pp. 3597–3602). Honolulu, HI.Google Scholar
  30. Huang, S. C., Vea, T., & Black, J. (2011). Learning classic mechanics with embodied cognition. In Proceedings of world conference on e-learning in corporate, government, healthcare, and higher education (pp. 209–215). Chesapeake, VA.Google Scholar
  31. Hughes, M., & Macleod, H. (1986). Using logo with very young children. In R. Lawler, B. du Boulay, M. Hughes, & H. Macleod (Eds.), Cognition and computers: Studies in learning (pp. 179–219). Chichester: Ellis Horwood.Google Scholar
  32. International Society for Technology in Education. (2011). Operational definition of computational thinking for K12 education. Available at http://www.iste.org/docs/ct-documents/computational-thinking-operational-definition-flyer.pdf?sfvrsn=2.
  33. Johnson-Glenberg, M. C., Birchfield, D. A., Tolentino, L., & Koziupa, T. (2014). Collaborative embodied learning in mixed reality motion-capture environments: Two science studies. Journal of Educational Psychology, 106(1), 86–104.CrossRefGoogle Scholar
  34. Kazakoff, E., & Bers, M. (2012). Programming in a robotics context in the kindergarten classroom: The impact on sequencing skills. Journal of Educational Multimedia and Hypermedia, 21(4), 371–391.Google Scholar
  35. Kurland, D. M., & Pea, R. D. (1985). Children’s mental models of recursive logo programs. Journal of Educational Computing Research, 1(2), 235–243.CrossRefGoogle Scholar
  36. Lakoff, G., & Johnson, M. (1999). Philosophy in the flesh: The embodied mind and its challenge to western thought. New York: Basic Books.Google Scholar
  37. Lee, I., Martin, F., Denner, J., Coulter, B., Allan, W., Erickson, J., et al. (2011). Computational thinking for youth in practice. ACM Inroads, 2(1), 32–37. doi: 10.1145/1929887.1929902.CrossRefGoogle Scholar
  38. Lindgren, R. (2014). Getting into the cue: Embracing technology-facilitated body movements as a starting point for learning. In V. R. Lee (Ed.), Learning technologies and the body: Integration and implementation in formal and informal environment (pp. 39–54). New York, NY: Routledge.Google Scholar
  39. Lye, S. Y., & Koh, J. H. L. (2014). Review on teaching and learning of computational thinking through programming: What is next for K-12? Computers in Human Behavior, 41, 51–61. doi: 10.1016/j.chb.2014.09.012.CrossRefGoogle Scholar
  40. Matarić, M. J., Koenig, N., & Feil-Seifer, D. (2007). Materials for enabling hands-on robotics and STEM education. In AAAI spring symposium on robots and robot venues: Resources for AI education (pp. 99–102). Stanford, CA.Google Scholar
  41. National Research Council. (2012). A framework for K-12 science education: Practices, crosscutting concepts, and core ideas. Washington, DC: The National Academies Press.Google Scholar
  42. Papert, S. (1972). Teaching children thinking. Programmed Learning and Educational Technology, 9(5), 245–255.Google Scholar
  43. Papert, S. (1980). Mindstorms: Children, computers, and powerful ideas. New York, NY: Basic Books Inc.Google Scholar
  44. Pea, R. D., & Kurland, D. M. (1984). On the cognitive effects of learning computer programming. New Ideas in Psychology, 2(2), 137–168.CrossRefGoogle Scholar
  45. Resnick, M., Maloney, J., Monroy-Hernandez, A., Rusk, N., Eastmond, E., Brennan, K., et al. (2009). Scratch: Programming for all. Communications of the ACM, 52(11), 60–67.CrossRefGoogle Scholar
  46. Robinson, M. A., & Uhlig, G. E. (1988). The effects of guided discovery Logo instruction on mathematical readiness and visual motor development in first grade students. Journal of Human Behavior and Learning, 5, 1–13.Google Scholar
  47. Schwartz, D. L., & Black, J. B. (1996). Shuttling between depictive models and abstract rules: Induction and fallback. Cognitive Science, 20(4), 457–497.CrossRefGoogle Scholar
  48. Siegler, R. S., & Opfer, J. E. (2003). The development of numerical estimation evidence for multiple representations of numerical quantity. Psychological Science, 14(3), 237–250.CrossRefGoogle Scholar
  49. Wilson, M. (2002). Six views of embodied cognition. Psychonomic Bulletin & Review, 9(4), 625–636. doi: 10.3758/BF03196322.CrossRefGoogle Scholar
  50. Wilson, A. D., & Golonka, S. (2013). Embodied cognition is not what you think it is. Frontiers in Psychology, 4, 1–13. doi: 10.3389/fpsyg.2013.00058.CrossRefGoogle Scholar
  51. Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33–35. doi: 10.1145/1118178.1118215.CrossRefGoogle Scholar
  52. Wing, J. M. (2008). Computational thinking and thinking about computing. Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, 366(1881), 3717–3725.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2017

Authors and Affiliations

  1. 1.Department of Mathematics, Science and Technology, Teachers CollegeColumbia UniversityNew YorkUSA
  2. 2.Department of Human Development, Teachers CollegeColumbia UniversityNew YorkUSA

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