Skip to main content

Advertisement

Springer Nature Link
Log in

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

  • Original research
  • Published:
Technology, Knowledge and Learning Aims and scope Submit manuscript

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.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Explore related subjects

Discover the latest articles and news from researchers in related subjects, suggested using machine learning.

Notes

  1. https://www.engageny.org/resource/new-york-state-p-12-common-core-learning-standards.

References

  • 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 

  • 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.

    Chapter  Google Scholar 

  • Abrahamson, D., & Trninic, D. (2015). Bringing forth mathematical concepts: Signifying sensorimotor enactment in fields of promoted action. ZDM Mathematics Education, 47(2), 295–306.

    Article  Google Scholar 

  • Alimisis, D. (2013). Educational robotics: Open questions and new challenges. Themes in Science and Technology Education, 6(1), 63–71.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Barsalou, L. W. (2008). Grounded cognition. Annual Review of Psychology, 59(1), 617–645. doi:10.1146/annurev.psych.59.103006.093639.

    Article  Google Scholar 

  • Barsalou, L. W., Niedenthal, P. M., Barbey, A. K., & Ruppert, J. A. (2003). Social embodiment. Psychology of Learning and Motivation, 43, 43–92.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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 

  • 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.

  • 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 

  • Booth, J. L., & Siegler, R. S. (2008). Numerical magnitude representations influence arithmetic learning. Child Development, 79(4), 1016–1031.

    Article  Google Scholar 

  • Brophy, S., Klein, S., Portsmore, M., & Rogers, C. (2008). Advancing engineering education in P-12 classrooms. Journal of Engineering Education, 97(3), 369–387.

    Article  Google Scholar 

  • 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 

  • 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.

  • 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.

    Article  Google Scholar 

  • Clements, D. H., & Gullo, D. F. (1984). Effects of computer programming on young children’s cognition. Journal of Educational Psychology, 76(6), 1051–1058.

    Article  Google Scholar 

  • Clements, D. H., & Sarama, J. (2002). The role of technology in early childhood learning. Teaching Children Mathematics, 8(6), 340–343.

    Google Scholar 

  • 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.

  • Ericsson, K. A., & Simon, H. A. (1980). Verbal reports as data. Psychological Review, 87(3), 215–251.

    Article  Google Scholar 

  • Fadjo, C. L. (2012). Developing Computational Thinking through Grounded Embodied Cognition (Unpublished doctoral dissertation). Columbia University, NY.

  • 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.

  • 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.

  • 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.

    Article  Google Scholar 

  • Fryer, W. A. (2014). Hopscotch challenges: Learn to code on an iPad!. Retrieved from http://publications.wesfryer.com/index.php/archive/article/view/53.

  • 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.

    Chapter  Google Scholar 

  • Glenberg, A. M. (2010). Embodiment as a unifying perspective for psychology. Wiley Interdisciplinary Reviews: Cognitive Science, 1(4), 586–596.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

  • 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.

  • 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 

  • International Society for Technology in Education. (2011). Operational definition of computational thinking for K–12 education. Available at http://www.iste.org/docs/ct-documents/computational-thinking-operational-definition-flyer.pdf?sfvrsn=2.

  • 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.

    Article  Google Scholar 

  • 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 

  • Kurland, D. M., & Pea, R. D. (1985). Children’s mental models of recursive logo programs. Journal of Educational Computing Research, 1(2), 235–243.

    Article  Google Scholar 

  • Lakoff, G., & Johnson, M. (1999). Philosophy in the flesh: The embodied mind and its challenge to western thought. New York: Basic Books.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • 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.

  • 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 

  • Papert, S. (1972). Teaching children thinking. Programmed Learning and Educational Technology, 9(5), 245–255.

    Google Scholar 

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

    Google Scholar 

  • Pea, R. D., & Kurland, D. M. (1984). On the cognitive effects of learning computer programming. New Ideas in Psychology, 2(2), 137–168.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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 

  • Schwartz, D. L., & Black, J. B. (1996). Shuttling between depictive models and abstract rules: Induction and fallback. Cognitive Science, 20(4), 457–497.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Wilson, M. (2002). Six views of embodied cognition. Psychonomic Bulletin & Review, 9(4), 625–636. doi:10.3758/BF03196322.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33–35. doi:10.1145/1118178.1118215.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mathematics, Science and Technology, Teachers College, Columbia University, 525 W. 120th Street, New York, NY, 10027, USA

    Woonhee Sung & Junghyun Ahn

  2. Department of Human Development, Teachers College, Columbia University, 525 W. 120th Street, New York, NY, 10027, USA

    John B. Black

Authors
  1. Woonhee Sung
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Junghyun Ahn
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. John B. Black
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Woonhee Sung.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sung, W., Ahn, J. & Black, J.B. Introducing Computational Thinking to Young Learners: Practicing Computational Perspectives Through Embodiment in Mathematics Education. Tech Know Learn 22, 443–463 (2017). https://doi.org/10.1007/s10758-017-9328-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10758-017-9328-x

Keywords

Profiles

  1. Woonhee Sung View author profile