Evaluating the Efficiency and Effectiveness of Adaptive Parsons Problems
Open accessAbstract
Practice is essential for learning. There is evidence that solving Parsons problems (putting mixed up code blocks in order) is a more efficient, but just as effective, form of practice than writing code from scratch. However, not all students successfully solve every Parsons problem. Making the problems adaptive, so that the difficulty changes based on the learner's performance, should keep the learner in Vygotsky's zone of proximal development and maximize learning gains. This paper reports on a study comparing the efficiency and effectiveness of learning from solving adaptive Parsons problems vs non-adaptive Parsons problem vs writing the equivalent code. The adaptive Parsons problems used both intra-problem and inter-problem adaptation. Intra-problem adaptation means that if the learner is struggling to solve the current problem, the problem can dynamically be made easier. Inter-problem adaptation means that the difficulty of the next problem is modified based on the learner's performance on the previous problem. This study provides evidence that solving intra-problem and inter-problem adaptive Parsons problems is a more efficient, but just as effective, form of practice as writing the equivalent code.
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Shah, M., & Garcia, D. D. (2020) · ACM Technical Symposium on Computer Science Education (SIGCSE) · doi:10.1145/3328778.3372640
Računalniško tekmovanje Pišek - Oblika vzpodbujanja učenja programiranja za vse
Lokar, M., & Mujkić, M. (2021) · Uporabna informatika · doi:10.31449/upinf.vol29.num1.121
Računalniško tekmovanje Pišek - Oblika vzpodbujanja učenja programiranja za vse
Lokar, M., & Mujkić, M. (2021) · Uporabna informatika · doi:10.31449/upinf.121
Developing a programming environment based on Parsons Problems for beginners in Python
Kim, Y., & Kim, J. (2026) · The Journal of Korean Association of Computer Education · doi:10.32431/kace.2026.29.1.003
Drill-type adaptive debugging to boost programming skills
Pozzan, G., Padova, C., Montuori, C., Arfé, B., & Vardanega, T. (2026) · Computer Science Education · doi:10.1080/08993408.2026.2672717
Große-Bölting, G., & Weißenrieder, F. (2026) · ACM Transactions on Computing Education · doi:10.1145/3844735
- Authors
- Ericson, B. J., Foley, J. D., & Rick, J.
- Affiliations
- Georgia Institute of Technology (US)
- Venue
- Proceedings of the 2018 ACM Conference on International Computing Education Research
- Publisher
- ACM
- Published
- 2018-08-08
- Pages
- 60-68
- OpenAlex
- W2886511030
- Cited by
- 70
- References
- 40
- Citations by year
- 2026: 5 · 2025: 5 · 2024: 7 · 2023: 16 · 2022: 11 · 2021: 10 · 2020: 8 · 2019: 8
- FWCI
- 7.36
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- ACM Conference on International Computing Education Research (ICER) (publishedVersion)
- Funders
- National Science Foundation (1432300, 1138378)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Social Sciences › Psychology › Developmental and Educational Psychology › Innovative Teaching and Learning Methods
- Keywords
- Parsons problems, adaptive learning, programming education, zone of proximal development, practice efficiency, learning effectiveness, writing software
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