Cited by 42Teaching and Learning ProgrammingEducational Robotics and EngineeringProblem and Project Based Learning

Evaluating the Effectiveness of Parsons Problems for Block-based Programming

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Zhi, Rui, Chi, Min, Barnes, Tiffany, Price, Thomas W.·2019·Proceedings of the 2019 ACM Conference on International Computing Education Research

Abstract

Parsons problems are program puzzles, where students piece together code fragments to construct a program. Similar to block-based programming environments, Parsons problems eliminate the need to learn syntax. Parsons problems have been shown to improve learning efficiency when compared to writing code or fixing incorrect code in lab studies, or as part of a larger curriculum. In this study, we directly compared Parsons problems with block-based programming assignments in classroom settings. We hypothesized that Parsons problems would improve students' programming efficiency on the lab assignments where they were used, without impacting performance on the subsequent, related homework or the later programming project. Our results confirmed our hypothesis, showing that on average Parsons problems took students about half as much time to complete compared to equivalent programming problems. At the same time, we found no evidence to suggest that students performed worse on subsequent assignments, as measured by performance and time on task. The results indicate that the effectiveness of Parsons problems is not simply based on helping students avoid syntax errors. We believe this is because Parsons problems dramatically reduce the programming solution space, letting students focus on solving the problem rather than having to solve the combined problem of devising a solution, searching for needed components, and composing them together.

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Research works printed but not linked: 3

  1. [10]10. Geela Venise Firmalo Fabic, Antonija Mitrovic, and Kourosh Neshatian. 2017. A Comparison of Different Types of Learning Activities in a Mobile Python Tutor. Proceedings of the 25th International Conference on Computers in Education (2017), 604-613.
  2. [24]24. Thomas W Price, Yihuan Dong, and Dragan Lipovac. 2017. iSnap: towards intelligent tutoring in novice programming environments. In Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education. ACM, 483-488.
  3. [32]32. Rui Zhi, Thomas W. Price, Nicholas Lytle, Yihuan Dong, and Tiffany Barnes. 2018. Reducing the State Space of Programming Problems through Data-Driven Feature Detection. In EDM Workshop.

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Authors
Zhi, R., Chi, M., Barnes, T., & Price, T. W.
Affiliations
North Carolina State University (US)
Venue
Proceedings of the 2019 ACM Conference on International Computing Education Research
Publisher
ACM
Published
2019-07-30
Pages
51-59
OpenAlex
W2964982419
Cited by
42
References
31
Citations by year
2026: 3 · 2025: 5 · 2024: 6 · 2023: 7 · 2022: 10 · 2021: 8 · 2020: 3
FWCI
6.08
Type
conference paper
Language
en
Open access
Locations
ACM Conference on International Computing Education Research (ICER) (publishedVersion)
Funders
National Science Foundation (1623470)
Topics
  • Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
  • Physical Sciences › Computer Science › Artificial Intelligence › Educational Robotics and Engineering
  • Social Sciences › Education › Problem and Project Based Learning
Keywords
Parsons problems, block-based programming, programming education, programming efficiency, syntactic errors, solution space reduction, programming exercises, homework performance

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