Difficult Concepts and Practices of Computational Thinking Using Block-based Programming



  • Hyunchang Moon Texas Tech University
  • Jongpil Cheon Texas Tech University
  • Kyungbin Kwon Indiana University


computational thinking, CT difficulties, CT challenges, block-based programming, Scratch


To help novice learners overcome the obstacles of learning computational thinking (CT) through programming, it is vital to identify difficult CT components. This study aimed to determine the computational concepts and practices that learners may have difficulties acquiring and discuss how programming instructions should be designed to facilitate learning CT in online learning environments. Participants included 92 undergraduate students enrolled in an online course. Data were collected from a CT knowledge test and coding journals. Results revealed that four computational concepts (i.e., parallelism, conditionals, data, and operators) and two computational practices (i.e., testing and debugging and abstracting and modularizing) were identified as CT components that were difficult to learn. The findings of this study imply that CT instructions should offer additional instructional supports to enhance the mastery of difficult computational concepts and practices. Further research is necessary to investigate instructional approaches to successful CT learning.


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How to Cite

Moon, H., Cheon, J., & Kwon, K. (2022). Difficult Concepts and Practices of Computational Thinking Using Block-based Programming. International Journal of Computer Science Education in Schools, 5(3), 3–16. https://doi.org/10.21585/ijcses.v5i3.129