Promoting pupils’ computational thinking skills and self-efficacy: a problem-solving instructional approach

Abstract

Computational thinking (CT) is a fundamental skill and an analytical ability that children in the twenty-first century should develop. Students should begin to work with algorithmic problem-solving and computational methods in K-12. Drawing on a conceptual framework (IGGIA) that combines CT and problem-solving, this study designed and implemented an interdisciplinary Scratch course in a primary school, examined the impact of the new problem-solving instructional approach (the adapted IGGIA) on pupils’ CT skills and self-efficacy, and explored the gender differences in these two aspects. A pretest–posttest nonequivalent group design was conducted among 63 fifth-grade students in two computer science classes over 14 weeks. Both quantitative and qualitative data were collected through the administration of CT scales, Scratch artifacts analysis and focus group interviews. The results revealed that the adapted IGGIA (1) significantly improved the CT skills of primary school students; (2) had a significant positive impact on pupils’ CT self-efficacy, especially on their critical thinking, algorithmic thinking and problem-solving; and (3) significantly enhanced girls’ CT skills and self-efficacy. These findings indicated that problem-solving instructional approaches could promote both cognitive and noncognitive aspects of students’ deeper computational learning.

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References

  1. Aho, A. V. (2012). Computation and computational thinking. Computer Journal, 55(7), 832–835.

    Article  Google Scholar 

  2. Askar, P., & Davenport, D. (2009). An investigation of factors related to self-efficacy for Java Programming among engineering students. Turkish Online Journal of Educational Technology, 8(1), 26–32.

    Google Scholar 

  3. Atmatzidou, S., & Demetriadis, S. (2016). Advancing students’ computational thinking skills through educational robotics: A study on age and gender relevant differences. Robotics and Autonomous Systems, 75, 661–670.

    Article  Google Scholar 

  4. Baek, Y., Yang, D., & Fan, Y. (2019). Understanding second grader’s computational thinking skills in robotics through their individual traits. Information Discovery and Delivery, 47(4), 218–228.

    Article  Google Scholar 

  5. 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.

    Article  Google Scholar 

  6. Basu, S., Biswas, G., & Kinnebrew, J. S. (2017). Learner modeling for adaptive scaffolding in a computational thinking-based science learning environment. User Modeling and User-Adapted Interaction, 27(1), 5–53.

    Article  Google Scholar 

  7. Benitti, F. B. V., & Spolaôr, N. (2017). How have robots supported STEM teaching? In M. S. Khine (Ed.), Robotics in STEM education: redesigning the learning experience (pp. 103–129). Springer.

    Chapter  Google Scholar 

  8. Brennan, K., & Resnick, M. (2012). New frameworks for studying and assessing the development of computational thinking. Proceedings of the 2012 Annual Meeting of the American Educational Research Association (pp. 1–25), Vancouver, Canada.

  9. Celik, V., & Yesilyurt, E. (2013). Attitudes to technology, perceived computer self-efficacy and computer anxiety as predictors of computer supported education. Computers & Education, 60(1), 148–158.

    Article  Google Scholar 

  10. Cheryan, S., Master, A., & Meltzoff, A. N. (2015). Cultural stereotypes as gatekeepers: Increasing girls’ interest in computer science and engineering by diversifying stereotypes. Frontiers in Psychology. https://doi.org/10.3389/fpsyg.2015.00049

    Article  Google Scholar 

  11. Chiazzese, G., Arrigo, M., Chifari, A., Lonati, V., & Tosto, C. (2019). Educational robotics in primary school: Measuring the development of computational thinking skills with the Bebras tasks. Informatics. https://doi.org/10.3390/informatics6040043

    Article  Google Scholar 

  12. Crews, T., & Butterfield, J. (2003). Gender differences in beginning programming: An empirical study on improving performance parity. Campus-Wide Information Systems, 20(5), 186–192.

    Article  Google Scholar 

  13. CSTA, & ISTE (2011). Operational Definition of Computational Thinking for K-12 Education. http://www.iste.org/docs/pdfs/Operational-Definition-of-Computational-Thinking.pdf. Accessed 2 Aug 2019.

  14. del Olmo-Muno, J., Cozar-Gutierrez, R., & Gonzalez-Calero, J. A. (2020). Computational thinking through unplugged activities in early years of primary education. Computers & Education. https://doi.org/10.1016/j.compedu.2020.103832

    Article  Google Scholar 

  15. 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.

    Article  Google Scholar 

  16. Djurdjevic-Pahl, A., Pahl, C., Fronza, I., & El Ioini, N. (2016). A pathway into computational thinking in primary schools. International symposium on emerging technologies for education (pp. 165–175). Springer.

    Google Scholar 

  17. Drabowicz, T. (2014). Gender and digital usage inequality among adolescents: A comparative study of 39 countries. Computers & Education, 74, 98–111.

    Article  Google Scholar 

  18. Espino, E. E. E., & González, C. G. (2016). Gender and computational thinking: review of the literature and applications. Proceedings of the XVII International Conference on Human Computer Interaction. https://doi.org/10.1145/2998626.2998665

    Article  Google Scholar 

  19. Grover, S., Pea, R., & Cooper, S. (2015). Designing for deeper learning in a blended computer science course for middle school students. Computer Science Education, 25(2), 199–237.

    Article  Google Scholar 

  20. Hendry, G. D., Frommer, M., & Walker, R. A. (1999). Constructivism and problem-based learning. Journal of Further and Higher Education, 23(3), 369–371.

    Article  Google Scholar 

  21. Hsu, T. C., Chang, S. C., & Hung, Y. T. (2018). How to learn and how to teach computational thinking: Suggestions based on a review of the literature. Computers & Education, 126, 296–310.

    Article  Google Scholar 

  22. Jones, B. F., Rasmussen, C. M., & Moffitt, M. C. (1997). Real-life problem solving: A collaborative approach to interdisciplinary learning (pp. 57–84). American Psychological Association.

    Book  Google Scholar 

  23. Kalelioglu, F., & Gülbahar, Y. (2014). The effects of teaching programming via Scratch on problem solving skills: A discussion from learners’ perspective. Informatics in Education, 13(1), 33–50.

    Google Scholar 

  24. Kalelioglu, F., Gülbahar, Y., & Kukul, V. (2016). A framework for computational thinking based on a systematic research review. Baltic Journal of Modern Computing, 4(3), 583–596.

    Google Scholar 

  25. Korkmaz, Ö., & Bai, X. (2019). Adapting computational thinking scale (CTS) for Chinese high school students and their thinking scale skills level. Participatory Educational Research, 6(1), 10–12.

    Article  Google Scholar 

  26. Krämer, N. C., Karacora, B., Lucas, G., Dehghani, M., Rüther, G., & Gratch, J. (2016). Closing the gender gap in STEM with friendly male instructors? On the effects of rapport behavior and gender of a virtual agent in an instructional interaction. Computers & Education, 99, 1–13.

    Article  Google Scholar 

  27. Lai, A. F., & Yang, S. M. (2011). The learning effect of visualized programming learning on 6 th graders’ problem solving and logical reasoning abilities. Proceedings of 2011 International Conference on Electrical and Control Engineering (pp. 6940–6944). Yichang, China, 16–18 September 2011.

  28. 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.

    Article  Google Scholar 

  29. Lykke, M., Coto, M., Mora, S., Vandel, N., & Jantzen, C. (2014). Motivating programming students by problem based learning and LEGO robots. Proceedings of 2014 IEEE Global Engineering Education Conference (pp. 544–555). Istanbul, Turkey, 3–5 April 2014.

  30. Maddrey, E. (2011). The Effect of Problem-Solving Instruction on the Programming Self-efficacy and Achievement of Introductory Computer Science Students. Ph.D. Thesis, Nova Southeastern University.

  31. Martin, D. P., & Rimm-Kaufman, S. E. (2015). Do student self-efficacy and teacher-student interaction quality contribute to emotional and social engagement in fifth grade math. Journal of School Psychology, 53(5), 359–373.

    Article  Google Scholar 

  32. Moreno-León, J., Robles, G., & Román-González, M. (2015). Dr. Scratch: Automatic analysis of Scratch projects to assess and foster computational thinking. RED. Red-Revista De Educacion A Distancia, 46, 1–23.

    Google Scholar 

  33. Panoutsopoulos, B. (2011). Introducing science technology engineering and mathematics in robotics outreach programs. Technology Interface International Journal, 12(1), 47–53.

    Google Scholar 

  34. Papavlasopoulou, S., Giannakos, M. N., & Jaccheri, L. (2019). Exploring children’s learning experience in constructionism-based coding activities through design-based research. Computers in Human Behavior, 99, 415–427.

    Article  Google Scholar 

  35. Pereira, H. B. D. B., Zebende, G. F., & Moret, M. A. (2010). Learning computer programming: Implementing a fractal in a turing machine. Computers & Education, 55(2), 767–776.

    Article  Google Scholar 

  36. Pillay, N., & Jugoo, V. R. (2005). An investigation into student characteristics affecting novice programming performance. SIGCSE Bulletin, 37(4), 107–110.

    Article  Google Scholar 

  37. Pucher, R., & Lehner, M. (2011). Project based learning in computer science–a review of more than 500 projects. Procedia - Social and Behavioral Sciences, 29, 1561–1566.

    Article  Google Scholar 

  38. Robertson, J., & Howells, C. (2008). Computer game design: Opportunities for successful learning. Computers in Education, 50(2), 559–578.

    Article  Google Scholar 

  39. Román-González, M., Pérez-González, J., & Jiménez-Fernández, C. (2017). Which cognitive abilities underlie computational thinking? criterion validity of the computational thinking test. Computers in Human Behavior, 72, 678–691.

    Article  Google Scholar 

  40. Román-González, M., Pérez-González, J., Moreno-León, J., & Robles, G. (2018). Extending the nomological network of computational thinking with non-cognitive factors. Computers in Human Behavior, 80, 441–459.

    Article  Google Scholar 

  41. Rubio, M. A., Romero-Zaliz, R., Mañoso, C., & Angel, P. (2015). Closing the gender gap in an introductory programming course. Computers & Education, 82, 409–420.

    Article  Google Scholar 

  42. Ruona, W. E. A. (2005). Analyzing qualitative data. In R. A. Swanson & E. F. Holton (Eds.), Foundations and methods of inquiry (pp. 223–263). Berrett-Koehler.

    Google Scholar 

  43. Sáez-López, J. M., Román-González, M., & Vázquez-Cano, E. (2016). Visual programming languages integrated across the curriculum in elementary school: A two year case study using “Scratch” in five schools. Computers & Education, 97, 129–141.

    Article  Google Scholar 

  44. Sáinz, M., & López-Sáez, M. (2010). Gender differences in computer attitudes and the choice of technology-related occupations in a sample of secondary students in Spain. Computers & Education, 54(2), 578–587.

    Article  Google Scholar 

  45. Savery, J. R. (2006). Overview of problem-based learning: Definitions and distinctions. Interdisciplinary Journal of Problem-based Learning. https://doi.org/10.7771/1541-5015.1002

    Article  Google Scholar 

  46. Schunk, D. H., & Pajares, F. (2005). Competence perceptions and academic functioning. In A. J. Elliot & C. S. Dweck (Eds.), Handbook of competence and motivation (pp. 85–104). The Guilford Press.

    Google Scholar 

  47. Selby, C. C., & Woollard, J. (2013). Computational thinking: The developing definition. Special Interest Group on Computer Science Education (SIGCSE). Canterbury, England, 1–3 July 2013.

  48. Sohrabi, R., Mohammadi, A., & Aghdam, G. A. (2013). Effectiveness of group counseling with problem solving approach on educational self-efficacy improving. Procedia - Social and Behavioral Sciences, 84, 1782–1784.

    Article  Google Scholar 

  49. Shute, V. J., Sun, C., & Asbell-Clarke, J. (2017). Demystifying computational thinking. Educational Research Review, 22, 142–158.

    Article  Google Scholar 

  50. Siu-Cheung, K., Ming, C. M., & Ming, L. (2018). A study of primary school students’ interest, collaboration attitude, and programming empowerment in computational thinking education. Computers & Education, 127, 178–189.

    Article  Google Scholar 

  51. Slavin, R. E. (2014). Educational psychology: Theory and practice (11th edition). Pearson

  52. Tang, X., Yin, Y., Lin, Q., Hadad, R., & Zhai, X. (2020). Assessing computational thinking: A systematic review of empirical studies. Computers & Education, 148, 1–22.

    Article  Google Scholar 

  53. Ting-Chia, H., Shao-Chen, C., & Yu-Ting, H. (2018). How to learn and how to teach computational thinking: Suggestions based on a review of the literature. Computers & Education, 126, 296–310.

    Article  Google Scholar 

  54. Tsai, C. Y. (2019). Improving students’ understanding of basic programming concepts through visual programming language: The role of self-efficacy. Computers in Human Behavior, 95, 224–232.

    Article  Google Scholar 

  55. Vekiri, I., & Chronaki, A. (2008). Gender issues in technology use: Perceived social support, computer self-efficacy and value beliefs, and computer use beyond school. Computers & Education, 51(3), 1392–1404.

    Article  Google Scholar 

  56. Voskoglou, M. G., & Buckley, S. (2012). Problem solving and computational thinking in a learning environment. Egyptian Computer Science Journal, 36(4), 28–46.

    Google Scholar 

  57. Wang, X. M., & Hwang, G. J. (2017). A problem posing-based practicing strategy for facilitating students’ computer programming skills in the team-based learning mode. Educational Technology Research & Development, 65(6), 1655–1671.

    Article  Google Scholar 

  58. Wei, X., Lin, L., Meng, N., Tan, W., & Kong, S. C. (2020). The effectiveness of partial pair programming on elementary school students’ computational thinking skills and self-efficacy. Computers & Education, 160, 1–15.

    Google Scholar 

  59. Weintrop, D., Beheshti, E., Horn, M., Orton, K., Jona, K., Trouille, L., & Wilensky, U. (2016). Defining computational thinking for mathematics and science classrooms. Journal of Science Education and Technology, 25(1), 127–147.

    Article  Google Scholar 

  60. Webb, H. C. (2013). Injecting Computational Thinking into Computing Activities for Middle School Girls. Ph.D. Thesis. The Pennsylvania State University.

  61. Werner, L. L., Hanks, B., & McDowell, C. (2004). Pair-programming helps female computer science students. Journal on Educational Resources in Computing. https://doi.org/10.1145/1060071.1060075

    Article  Google Scholar 

  62. Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33–35.

    Article  Google Scholar 

  63. Yadav, A., Mayfield, C., Zhou, N., Hambrusch, S., & Korb, J. T. (2014). Computational thinking in elementary and secondary teacher education. ACM Transactions on Computing Education (TOCE), 14(1), 1–16.

    Article  Google Scholar 

  64. Yukselturk, E., & Altiok, S. (2017). An investigation of the effects of programming with Scratch on the preservice IT teachers’ self-efficacy perceptions and attitudes towards computer programming. British Journal of Educational Technology, 48(3), 789–801.

    Article  Google Scholar 

  65. Zhang, L., & Nouri, J. (2019). A systematic review of learning computational thinking through Scratch in K-9. Computers & Education. https://doi.org/10.1016/j.compedu.2019.103607

    Article  Google Scholar 

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Acknowledgements

Thanks to Miss Xu Li for her teaching assistance in this research, and thanks to anonymous reviewers for comments on earlier drafts.

Funding

This study was supported by the Fundamental Research Funds for the Central Universities, SNNU (18SZZD01) and the National Natural Science Foundation of China (No. 61977044).

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Ma, H., Zhao, M., Wang, H. et al. Promoting pupils’ computational thinking skills and self-efficacy: a problem-solving instructional approach. Education Tech Research Dev 69, 1599–1616 (2021). https://doi.org/10.1007/s11423-021-10016-5

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Keywords

  • Problem-solving
  • IGGIA framework
  • Computational thinking skills
  • Computational thinking self-efficacy
  • Scratch programming