Advertisement

Case Studies of Elementary Children’s Engagement in Computational Thinking Through Scratch Programming

  • Sze Yee Lye
  • Joyce Hwee Ling Koh
Chapter

Abstract

Scratch is a programming environment designed to facilitate children’s engagement in computational thinking through the creation of interactive multimedia products. It is purported that children’s engagement in computational thinking can possibly build their problem-solving skills, which is a key twenty-first-century competency. As such, Scratch programming has attracted considerable attention in the educational field recently, especially through the integration of Scratch programming into the school curriculum. Despite this increased interest, there is limited understanding of the possible achievements and challenges that children with different programming abilities may have when engaging in computational thinking. Such studies are critical for understanding the computational thinking of elementary students and are useful for helping educators to better design programming lessons. To address this gap, this study examines three case studies of how elementary children with different programming abilities approach Scratch programming. Using a multiple case study approach, the narratives of children’s programming moves, utterances, and behaviours during Scratch programming will be compared to understand the possible achievements as well as the challenges that children could face when engaging in computational thinking through Scratch programming. Based on the findings, we proposed some possible instructional implications for supporting children’s engagement in computational thinking through K-12 programming lessons.

Keywords

K-12 computational thinking K-12 programming STEM education Scratch programming Problem-solving 

References

  1. Agresti, A. (2007). An introduction to categorical data analysis. Hoboken, NJ: John Wiley.CrossRefGoogle Scholar
  2. Alexander, P. A. (1992). Domain knowledge: Evolving themes and emerging concerns. Educational Psychologist, 27(1), 33.CrossRefGoogle Scholar
  3. Ananiadou, K., & Claro, M. (2009). 21st century skills and competences for new millennium learners in OECD countries. OECD Education working papers, 41. doi:  https://doi.org/10.1787/218525261154.
  4. Barr, V., & Stephenson, C. (2011). Bringing computational thinking to K-12: What is involved and what is the role of the computer science education community? ACM Inroads, 2(1), 48–54.  https://doi.org/10.1145/1929887.1929905.CrossRefGoogle Scholar
  5. Baytak, A., & Land, S. M. (2011). An investigation of the artifacts and process of constructing computers games about environmental science in a fifth grade classroom. Etr&D-Educational Technology Research and Development, 59(6), 765–782.  https://doi.org/10.1007/s11423-010-9184-z.CrossRefGoogle Scholar
  6. Bednarik, R. (2012). Expertise-dependent visual attention strategies develop over time during debugging with multiple code representations. International Journal of Human-Computer Studies, 70(2), 143–155.  https://doi.org/10.1016/j.ijhcs.2011.09.003.CrossRefGoogle Scholar
  7. Berland, M., Martin, T., Benton, T., Smith, C. P., & Davis, D. (2013). Using learning analytics to understand the learning pathways of novice programmers. Journal of the Learning Sciences, 22(4), 564–599.  https://doi.org/10.1080/10508406.2013.836655.CrossRefGoogle Scholar
  8. Bers, M. U., Flannery, L., Kazakoff, E., & Sullivan, A. (2014). Computational thinking and tinkering: Exploration of an early childhood robotics curriculum. Computers & Education, 72, 145–157.  https://doi.org/10.1016/j.compedu.2013.10.020.CrossRefGoogle Scholar
  9. Binkley, M., Erstad, O., Herman, J., Raizen, S., Ripley, M., Miller-Ricci, M., & Rumble, M. (2012). Defining twenty-first century skills. In P. Griffin, B. McGaw, & E. Care (Eds.), Assessment and teaching of 21st century skills (pp. 17–66). Dordrecht, Netherlands: Springer.CrossRefGoogle Scholar
  10. Brennan, K., & Resnick, M. (2012). New frameworks for studying and assessing the development of computational thinking. Paper presented at the annual American Educational Research Association meeting, Vancouver, BC, Canada. http://web.media.mit.edu/~kbrennan/files/Brennan_Resnick_AERA2012_CT.pdf
  11. Brown, A. L., Ash, D., Rutherford, M., Nakagawa, K., Gordon, A., & Campione, J. C. (1993). Distributed expertise in the classroom. In G. Saloman (Ed.), Distributed cognitions: Psychological and educational considerations (pp. 188–228). Cambridge, UK: Cambridge University Press.Google Scholar
  12. 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
  13. Choi, I., Land, S., & Turgeon, A. (2005). Scaffolding peer-questioning strategies to facilitate metacognition during online small group discussion. Instructional Science, 33(5–6), 483–511.  https://doi.org/10.1007/s11251-005-1277-4.CrossRefGoogle Scholar
  14. Cooper, S. (2010). The design of Alice. ACM Transactions on Computing Education, 10(4), 1–16.  https://doi.org/10.1145/1868358.1868362.CrossRefGoogle Scholar
  15. Creswell, J. W. (2013). Qualitative inquiry and research design: Choosing among five approaches (3rd ed.). Thousands Oaks, CA: Sage.Google Scholar
  16. Denner, J., Werner, L., & Ortiz, E. (2012). Computer games created by middle school girls: Can they be used to measure understanding of computer science concepts? Computers & Education, 58(1), 240–249.  https://doi.org/10.1016/j.compedu.2011.08.006.CrossRefGoogle Scholar
  17. Feng, C.-Y., & Chen, M.-P. (2014). The effects of goal specificity and scaffolding on programming performance and self-regulation in game design. British Journal of Educational Technology, 45(2), 285–302.  https://doi.org/10.1111/bjet.12022.CrossRefGoogle Scholar
  18. Fessakis, G., Gouli, E., & Mavroudi, E. (2013). Problem solving by 5–6 years old kindergarten children in a computer programming environment: A case study. Computers & Education, 63, 87–97.  https://doi.org/10.1016/j.compedu.2012.11.016.CrossRefGoogle Scholar
  19. Ge, X., & Land, S. (2003). Scaffolding students’ problem-solving processes in an ill-structured task using question prompts and peer interactions. Educational Technology Research and Development, 51(1), 21–38.  https://doi.org/10.1007/BF02504515.CrossRefGoogle Scholar
  20. Grover, S., & Pea, R. (2013). Computational thinking in K–12: A review of the state of the field. Educational Researcher, 42(1), 38–43.  https://doi.org/10.3102/0013189x12463051.CrossRefGoogle Scholar
  21. Haefliger, S., Von Krogh, G., & Spaeth, S. (2008). Code reuse in open source software. Management Science, 54(1), 180–193.CrossRefGoogle Scholar
  22. Hsieh, H.-F., & Shannon, S. E. (2005). Three approaches to qualitative content analysis. Qualitative Health Research, 15(9), 1277–1288.CrossRefGoogle Scholar
  23. Jonassen, D. (2011). Learning to solve problems: A handbook for designing problem-solving learning environments. New York: Routledge.Google Scholar
  24. Kafai, Y., & Burke, Q. (2013). Computer programming goes back to school. Phi Delta Kappan, 95(1), 61–65.CrossRefGoogle Scholar
  25. Kafai, Y., & Resnick, M. (Eds.). (1996). Constructionism in practice: Designing, thinking, and learning in a digital world. Mahwah, NJ: Lawrence Erlbaum.Google Scholar
  26. Kafai, Y., Fields, D. A., & Burke, Q. (2010). Entering the clubhouse: Case studies of young programmers joining the online scratch communities. Journal of Organizational and End User Computing, 22(2), 21–35.  https://doi.org/10.4018/joeuc.2010101906.CrossRefGoogle Scholar
  27. Kucan, L., & Beck, I. L. (1997). Thinking aloud and reading comprehension research: Inquiry, instruction, and social interaction. Review of Educational Research, 67(3), 271–299.  https://doi.org/10.3102/00346543067003271.CrossRefGoogle Scholar
  28. Lee, Y.-J. (2010). Developing computer programming concepts and skills via technology-enriched language-art projects: A case study. Journal of Educational Multimedia and Hypermedia, 19(3), 307–326.Google Scholar
  29. Lehrer, R., Lee, M., & Jeong, A. (1999). Reflective teaching of logo. Journal of the Learning Sciences, 8(2), 245–289.  https://doi.org/10.1207/s15327809jls0802_3.CrossRefGoogle Scholar
  30. Lewis, C. M. (2011). Is pair programming more effective than other forms of collaboration for young students? Computer Science Education, 21(2), 105–134.  https://doi.org/10.1080/08993408.2011.579805.CrossRefGoogle Scholar
  31. Li, D. D., & Lim, C. P. (2008). Scaffolding online historical inquiry tasks: A case study of two secondary school classrooms. Computers & Education, 50(4), 1394–1410.  https://doi.org/10.1016/j.compedu.2006.12.013.CrossRefGoogle Scholar
  32. Lin, J. M. C., & Liu, S. F. (2012). An investigation into parent-child collaboration in learning computer programming. Educational Technology & Society, 15(1), 162–173.Google Scholar
  33. Martin, T., Berland, M., Benton, T., & Smith, C. P. (2013). Learning programming with IPRO: The effects of a mobile, social programming environment. Journal of Interactive Learning Research, 24(3), 301–328.Google Scholar
  34. Meerbaum-Salant, O., Armoni, M., & Ben-Ari, M. (2013). Learning computer science concepts with scratch. Computer Science Education, 23(3), 239–264.  https://doi.org/10.1080/08993408.2013.832022.CrossRefGoogle Scholar
  35. Merriam, S. B. (2009). Qualitative research: A guide to design and implementation. San Francisco, California: Jossey-Bass.Google Scholar
  36. NRC. (2012). A framework for K-12 science education: Practices, crosscutting concepts, and core ideas. The National Academies Press.Google Scholar
  37. Palinscar, A. S., & Brown, A. L. (1984). Reciprocal teaching of comprehension-fostering and comprehension-monitoring activities. Cognition and Instruction, 1(2), 117–175.  https://doi.org/10.1207/s1532690xci0102_1.CrossRefGoogle Scholar
  38. Palumbo, D. B. (1990). Programming language/problem-solving research: A review of relevant issues. Review of Educational Research, 60(1), 65–89.  https://doi.org/10.3102/00346543060001065.CrossRefGoogle Scholar
  39. Papert, S. (1980). Mindstorms: Children, computers, and powerful ideas. New York: Basic Books.Google Scholar
  40. Papert, S. (1994). The children’s machine: Rethinking school in the age of the computer. New York: Basic Books.Google Scholar
  41. Puntambekar, S., & Kolodner, J. L. (2005). Toward implementing distributed scaffolding: Helping students learn science from design. Journal of Research in Science Teaching, 42(2), 185–217.  https://doi.org/10.1002/tea.20048.CrossRefGoogle Scholar
  42. 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.  https://doi.org/10.1145/1592761.1592779.CrossRefGoogle Scholar
  43. Sáez López, J. M., González, M. R., & Cano, E. V. (2016). Visual programming languages integrated across the curriculum in elementary school: A two year case study using “scratch” in five schools. Computers & Education.  https://doi.org/10.1016/j.compedu.2016.03.003.
  44. Schünemann, N., Spörer, N., & Brunstein, J. C. (2013). Integrating self-regulation in whole-class reciprocal teaching: A moderator–mediator analysis of incremental effects on fifth graders’ reading comprehension. Contemporary Educational Psychology, 38(4), 289–305.  https://doi.org/10.1016/j.cedpsych.2013.06.002.CrossRefGoogle Scholar
  45. Su, A. Y. S., Yang, S. J. H., Hwang, W. Y., Huang, C. S. J., & Tern, M. Y. (2014). Investigating the role of computer-supported annotation in problem-solving-based teaching: An empirical study of a scratch programming pedagogy. British Journal of Educational Technology, 45(4), 647–665.  https://doi.org/10.1111/bjet.12058.CrossRefGoogle Scholar
  46. Tangney, B., Oldham, E., Conneely, C., Barrett, S., & Lawlor, J. (2010). Pedagogy and processes for a computer programming outreach workshop—the bridge to college model. IEEE Transactions on Education, 53(1), 53–60.CrossRefGoogle Scholar
  47. Vessey, I. (1985). Expertise in debugging computer programs: A process analysis. International Journal of Man-Machine Studies, 23(5), 459–494.  https://doi.org/10.1016/S0020-7373(85)80054-7.CrossRefGoogle Scholar
  48. Wiedenbeck, S., Fix, V., & Scholtz, J. (1993). Characteristics of the mental representations of novice and expert programmers: An empirical study. International Journal of Man-Machine Studies, 39(5), 793–812.  https://doi.org/10.1006/imms.1993.1084.CrossRefGoogle Scholar
  49. Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33–35.CrossRefGoogle Scholar
  50. Wyeth, P. (2008). How young children learn to program with sensor, action, and logic blocks. Journal of the Learning Sciences, 17(4), 517–550.  https://doi.org/10.1080/10508400802395069.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Sze Yee Lye
    • 1
  • Joyce Hwee Ling Koh
    • 2
  1. 1.ICT DepartmentTeck Whye Primary SchoolSingaporeSingapore
  2. 2.Higher Education Development CentreUniversity of OtagoDunedinNew Zealand

Personalised recommendations