Advertisement

Measuring Computational Thinking Development with the FUN! Tool

  • Sarah BrasielEmail author
  • Kevin Close
  • Soojeong Jeong
  • Kevin Lawanto
  • Phil Janisiewicz
  • Taylor Martin
Chapter
Part of the Educational Communications and Technology: Issues and Innovations book series (ECTII)

Abstract

Computational thinking (CT) has been given recent attention suggesting that it be developed in children of all ages. With the creation of K-12 computer science standards by the Computer Science Teacher Association, states such as Massachusetts and Washington are leading the nation in adopting these standards into their school systems. This seems somewhat premature, when there are so few measures of computational thinking or computer programming skills that can be applied easily in a K-12 setting to assess outcomes of such state-wide initiatives. Through funding from the National Science Foundation, we developed an analysis tool to efficiently capture student learning progressions and problem-solving activities while coding in Scratch, a popular visual programming language developed by MIT Media Lab. Our analysis tool, the Functional Understanding Navigator! or FUN! tool, addresses the need to automate processes to help researchers efficiently clean, analyze, and present data. We share our experiences using the tool with Scratch data collected from three different week-long summer Scratch Camps with students in grades 5 to 8. Based on our preliminary analyses, we share important considerations for researchers interested in educational data mining and learning analytics in the area of assessing computational thinking. We also provide links to the publically available FUN! tool and encourage others to participate in a community developing new measures of computational thinking and computer programming.

Keywords

Computational thinking Educational data mining Learning analytics 

Notes

Acknowledgment

This work is supported by the National Science Foundation Grant IIS-1319938. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

References

  1. Baker, R. S. J. d. (2011). Data mining for education. In B. McGaw, P. Peterson, & E. Baker (Eds.), International encyclopedia of education (3rd ed.). Oxford: Elsevier.Google Scholar
  2. Bienkowski, M., Feng, M., & Means, B. (2012). Enhancing teaching and learning through educational data mining and learning analytics: An issue brief. Washington, DC: Office of Educational Technology, U.S. Department of Education.Google Scholar
  3. Boe, B., Hill, C., Len, M., Dreschler, G., Conrad, P., & Franklin, D. (2013). Hairball: Lint-inspired static analysis of scratch projects. In Proceeding of the 44th ACM technical symposium on computer science education (pp. 215–220). Denver, CO: ACM.Google Scholar
  4. Brennan, K., & Resnick, M. (2012). New frameworks for studying and assessing the development of computational thinking. In Proceedings of the 2012 annual meeting of the American Educational Research Association. Vancouver, Canada.Google Scholar
  5. Close, K., Janisiewicz, P., Brasiel, S., & Martin, T. (2015). What do I do with all this data? How to use the FUN! tool to automatically clean, analyze, and visualize your digital data. In Proceedings from games and learning society 11 conference. Madison, WI, USA. Retrieved from http://press.etc.cmu.edu/files/GLS11-Proceedings-2015-web.pdf
  6. Fields, D. A., Quirk, L., Horton, T., Velasquez, X., Amely, J. & Pantic, K. (2016a). Working toward equity in a constructionist Scratch camp: Lessons learned in applying a studio design model. In Proceedings of constructionism. Bangkok, Thailand.Google Scholar
  7. Fields, D. A., Quirk, L., Amely, J., & Maughan, J. (2016b). Combining “big data” and “thick data” analyses for understanding youth learning trajectories in a summer coding camp. In Proceedings of the 47th ACM technical symposium on computer science education (SIGCSE ‘16). New York, NY: ACM.Google Scholar
  8. Grover, S., & Pea, R. (2013). Computational thinking in K–12. A review of the state of the field. Educational Researcher, 42(1), 38–43.CrossRefGoogle Scholar
  9. Kelleher, C., & Pausch, R. (2005). Lowering the barriers to programming: A taxonomy of programming environments and languages for novice programmers. ACM Computing Surveys (CSUR), 37(2), 83–137.CrossRefGoogle Scholar
  10. Moreno-León, J., & Robles, G. (2015). Analyze your Scratch projects with Dr. Scratch and assess your computational thinking skills. In Proceedings of the 7th international Scratch conference (Scratch2015AMS). Amsterdam, Netherlands.Google Scholar
  11. 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.CrossRefGoogle Scholar
  12. Resnick, M., Maloney, J., Monroy-Hernández, A., Rusk, N., Eastmond, E., Brennan, K., et al. (2009). Scratch: Programming for all. Communications of the ACM, 52(11), 60–67.CrossRefGoogle Scholar
  13. Rich, P. J., Leatham, K. R., & Wright, G. A. (2013). Convergent cognition. Instructional Science, 41(2), 431–453.CrossRefGoogle Scholar
  14. Schutt, R., & O’Neil, C. (2013). Doing data science: Straight talk from the frontline. Sebastopol, CA: O’Reilly Media.Google Scholar
  15. Watters, A. (2011). Should computer science be required in K-12? Retrieved from http://ww2.kqed.org/mindshift/2011/12/15/should-computer-science-be-required-in-k-12/
  16. Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33–35.CrossRefGoogle Scholar
  17. Wing, J. M. (2011). Computational thinking. Retrieved from https://csta.acm.org/Curriculum/sub/CurrFiles/WingCTPrez.pdf

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Sarah Brasiel
    • 1
    Email author
  • Kevin Close
    • 1
  • Soojeong Jeong
    • 1
  • Kevin Lawanto
    • 1
  • Phil Janisiewicz
    • 2
  • Taylor Martin
    • 3
  1. 1.Department of Instructional Technology and Learning SciencesUtah State UniversityLoganUSA
  2. 2.Agile DynamicsHoustonUSA
  3. 3.O’Reilly MediaSebastopolUSA

Personalised recommendations