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Ericson, Barbara, Pearce, Janice, Rodger, Susan, Csizmadia, Andrew, Garcia, Rita, Gutierrez, Francisco, Liaskos, Konstantinos, Padiyath, Aadarsh, Scott, Michael 
ORCID: https://orcid.org/0000-0002-6803-1490, Smith, David, Warriem, Jayakrishnan and Bernuy, Angela
(2023)
Multi-Institutional Multi-National Studies of Parsons Problems.
In:
Proceedings of the 2023 Working Group Reports on Innovation and Technology in Computer Science Education.
Association for Computing Machinery, New York, NY, 0-0.
ISBN 979-8-4007-0139-9
(Submitted)
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ITiCSE_2023_WorkingGroup_Parsons_Report.pdf - Submitted Version Available under License Creative Commons Attribution. Download (7MB) | Preview |
Abstract / Summary
Students are often asked to learn programming by writing code from scratch. However, many novices struggle to write code and get frustrated when their code does not work. Parsons problems can reduce the difficulty of a coding problem by providing mixed-up blocks that the learner assembles in the correct order. They can include distractor blocks with common errors that are not needed in a correct solution, but which may help students learn to recognize and fix errors. There is evidence that students find Parsons problems engaging, useful for learning to program, easier than writing code from scratch, typically faster to solve than writing code from scratch with equivalent learning gains, and useful for learning patterns. Most of the research on Parsons problems prior to this work has been in single-institution studies, so we saw a need for replication across multiple contexts.
A 2022 ITiCSE Parsons working group conducted an extensive literature review of Parsons problems, designed several experimental studies for Parsons problems in Python, and created `study-in-a-box' materials to help instructors run the experimental studies, but the 2022 working group had only sufficient time to pilot two of the studies.
Our 2023 ITiCSE Parsons working group reviewed these studies, revised some studies, created new studies, conducted think-aloud observations on some studies, and ran revised and new experimental studies. The think-aloud observations and experimental studies provided evidence for using Parsons problems to help students learn common algorithms such as swap, and the usefulness of distractors in helping students learn to recognize, fix, and avoid common errors. In addition, we review Parsons problem papers published after the 2022 literature review and provide a literature review of multi-national (MIMN) studies conducted in computer science education to understand better the motivations and challenges in performing such MIMN studies.
In summary, this article contributes an analysis of recent Parsons problem research papers, an itemization of considerations for MIMN studies, the results from our MIMN studies of Parsons problems, and a discussion of recent and future directions for MIMN studies of Parsons problems.
| Item Type: | Book Section |
|---|---|
| ISBN: | 979-8-4007-0139-9 |
| Subjects: | Computer Science, Information & General Works Education Research Technology |
| Courses by Department: | The Games Academy > Computing for Games |
| Related URLs: | |
| Depositing User: | Michael Scott |
| Date Deposited: | 30 Oct 2023 16:48 |
| Last Modified: | 10 Apr 2024 14:48 |
| URI: | https://falmouth-test.eprints-hosting.org/id/eprint/5216 |
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