Education and Information Technologies

, Volume 23, Issue 4, pp 1483–1500 | Cite as

Comparing loops misconceptions in block-based and text-based programming languages at the K-12 level

  • Monika Mladenović
  • Ivica Boljat
  • Žana Žanko


Novice programmers are facing many difficulties while learning to program. Most studies about misconceptions in programming are conducted at the undergraduate level, yet there is a lack of studies at the elementary school (K-12) level, reasonably because computer science neither programming are regularly still not the part of elementary school curricula’s. Are the misconceptions about loops at elementary school level equal to those at the undergraduate level? Can we “prevent” the misconceptions by using the different pedagogical approach, visual programming language and shifting the programming context toward game programming? In this paper, we tried to answer these questions. We conducted the student misconceptions research on one of the fundamental programming concepts – the loop. The research is conducted in the classroom settings among 207 elementary school students. Students were learning to program in three programming languages: Scratch, Logo and Python. In this paper, we present the results of this research.


Programming Misconceptions Loop Block-based programming languages Text-based programming languages K-12 


  1. Berland, M., Martin, T., Benton, T., Petrick Smith, C., & Davis, D. (2013). Using learning analytics to understand the learning pathways of novice programmers. The Journal of the Learning Sciences, 22(4), 564–599.CrossRefGoogle Scholar
  2. Bonar, J., & Soloway, E. (1983). Uncovering principles of novice programming. In Proceedings of the 10th ACM SIGACT-SIGPLAN symposium on principles of programming languages (pp. 10–13).Google Scholar
  3. Brown, J. S. (2000). GROWING UP DIGITAL. How the web changes work, education, and the ways people learn. The Magazine of Higher. Learning, 32(2), 11–20.Google Scholar
  4. Brusilovsky, P., Calabrese, E., Hvorecky, J., Kouchnirenko, A., & Miller, P. (1997). Mini ­ languages: A way to learn programming principles. Education and Information Technologies, 2(1), 65–83. Scholar
  5. Clegg, T. L., & Kolodner, J. L. (2007). Bricoleurs and planners engaging in scientific reasoning: A tale of two groups in one learning community. Research and Practice in Technology Enhanced Learning, 2(3), 239–265.CrossRefGoogle Scholar
  6. Dann, W., & Cooper, S. (2009). Education: Alice 3: Concrete to abstract. Communications of the ACM, 52(8), 27–29. Scholar
  7. Dehnadi, S. (2009). A cognitive study of learning to program in introductory programming courses. London: Middlesex University.Google Scholar
  8. Fusco, E. (1981). Matching curriculum to students cognitive levels. Educational Leadership, 39(1), 47.Google Scholar
  9. Garneli, V., Giannakos, M. N., & Chorianopoulos, K. (2015). Computing education in K-12 schools: A review of the literature. In Global Engineering Education Conference (EDUCON), 2015 IEEE (pp. 543–551). Tallinn, Estonia: IEEE.Google Scholar
  10. Gomes, A., & Mendes, A. J. N. (2007). Learning to program-difficulties and solutions. International Conference on. Engineering Education, 1–5.Google Scholar
  11. Grover, S., & Basu, S. (2017). Measuring student learning in introductory block-based programming. In Proceedings of the 2017 ACM SIGCSE technical symposium on computer science education - SIGCSE ‘17 (pp. 267–272). New York, New York: ACM Press. Scholar
  12. 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
  13. Grover, S., Cooper, S., & Pea, R. (2014). Assessing computational learning in K-12. In Proceedings of the 2014 conference on Innovation & technology in computer science education - ITiCSE ‘14 (pp. 57–62).
  14. 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. Scholar
  15. Guzdial, M. (2004). Programming environments for novicmes and Culturees. Computer Science Education Research, 2004, 127–154.Google Scholar
  16. Kafai, Y. B. (2006). Playing and making games for learning. Games and Culture, 1(1), 36–40. Scholar
  17. Kafai, Y. B., & Burke, Q. (2015). Constructionist gaming: Understanding the benefits of making games for learning. Educational Psychologist, 50(4), 313–334. Scholar
  18. Ke, F., & Fengfeng. (2014). An implementation of design-based learning through creating educational computer games: A case study on mathematics learning during design and computing. Computers & Education, 73, 26–39. Scholar
  19. 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
  20. Kölling, M., & McKay, F. (2016). Heuristic evaluation for novice programming systems. ACM Transactions on Computing Education (TOCE), 16(3), 12.Google Scholar
  21. Kuechler, W. L., & Simkin, M. G. (2003). How well do multiple choice tests evaluate student understanding in computer programming classes? Journal of Information Systems Education, 14(4), 389.Google Scholar
  22. Lahtinen, E., Ala-Mutka, K., & Järvinen, H.-M. (2005). A study of the difficulties of novice programmers. ACM SIGCSE Bulletin, 37(3), 14. Scholar
  23. Linn, M. C., & Dalbey, J. (1985). Cognitive consequences of programming instruction: Instruction, access, and ability. Educational Psychologist, 20(4), 191–206. Scholar
  24. Louis, C., Lawrence, M., & Keith, M. (2011). Research methods in education. Oxford, UK: Routledge. Scholar
  25. Maloney, J. H., Peppler, K., Kafai, Y., Resnick, M., & Rusk, N. (2008). Programming by choice. ACM SIGCSE Bulletin, 40(1), 367. Scholar
  26. Mayer, R. E. (1981). The psychology of how novices learn computer programming. ACM Computing Surveys, 13(1), 121–141. Scholar
  27. McCracken, M., Almstrum, V., Diaz, D., Guzdial, M., Hagan, D., Kolikant, Y. B.-D., et al. (2001). A multi-national, multi-institutional study of assessment of programming skills of first-year CS students. SIGCSE Bull, 33(4), 125–180. Scholar
  28. McKenna, P. (2004). Gender and black boxes in the programming curriculum. Journal on Educational Resources in Computing, 4(1), 6--es. Scholar
  29. Mladenović, M., Krpan, D., & Mladenović, S. (2016a). Introducing programming to elementary students novices by using game development in python and scratch. In EDULEARN16 Proceedings (pp. 1622–1629). IATED.  10.21125/edulearn.2016.1323.
  30. Mladenović, S., Krpan, D., & Mladenovic, M. (2016b). Using games to help novices embrace programming: From elementary to higher education. International Journal of Engineering Education, 32(1), 521–531.Google Scholar
  31. Mladenović, M., Rosić, M., & Mladenović, S. (2016c). Comparing elementary students ’ programming success based on programming environment. International Journal of Modern Education and Computer Science, 8(August), 1–10.
  32. Myers, B. A. (1990). Taxonomies of visual programming and program visualization. Journal of Visual Languages and Computing, 1(1), 97–123.CrossRefGoogle Scholar
  33. Papert, S. (1980). Mindstorms: Children, computers, and powerful ideas. New York: Basic Books, Inc..Google Scholar
  34. Papert, S. (1993). The children’s machine: Rethinking school in the age of the computer. BasicBooks Retrieved from
  35. Papert, S. (2010). Does easy do it? Children, games, and learning. Game Developer.
  36. Piaget, J. (1952). The origins of intelligence in children. American Psychological Association ({APA}).
  37. Prensky, M. (2001). Digital Natives,Digital Immigrants Part 1. On the Horizon, 9, 1–6. Scholar
  38. Robins, A., Rountree, J., & Rountree, N. (2003). Learning and teaching programming: A review and discussion. Computer Science Education, 13(2), 137–172.CrossRefGoogle Scholar
  39. Sekiya, T., & Yamaguchi, K. (2013). Tracing quiz set to identify novices’ programming misconceptions. In Proceedings of the 13th Koli calling international conference on computing education research - Koli calling ‘13 (pp. 87–95). New York: ACM Press.
  40. Turkle, S., & Papert, S. (1992). Epistemological pluralism and the revaluation of the concrete. The Journal of Mathematical Behavior, 11(1), 3–33.Google Scholar
  41. Winslow, L. E. (1996). Programming pedagogy---a psychological overview. ACM SIGCSE Bulletin, 28(3), 17–22. Scholar
  42. Zur-Bargury, I., Pârv, B., & Lanzberg, D. (2013). A nationwide exam as a tool for improving a new curriculum. In Proceedings of the 18th ACM conference on Innovation and technology in computer science education - ITiCSE ‘13 (p. 267). New York, New York: ACM Press.

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  1. 1.Faculty of ScienceUniversity of SplitSplitCroatia
  2. 2.Elementary school “Mejasi”SplitCroatia

Personalised recommendations