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Background and context. “Explain in Plain English” (EiPE) questions ask students to explain the high-level purpose of code, requiring them to understand the macrostructure of the program’s intent. A lot is known about techniques that experts use to comprehend code, but less is known about how we should teach novices to develop this capability. Objective. Identify techniques that can be taught to students to assist them in developing their ability to comprehend code and contribute to the body of knowledge of how novices develop their code comprehension skills. Method. We developed interventions that could be taught to novices motivated by previous research about how experts comprehend code: prompting students to identify beacons, identify the role of variables, tracing, and abstract tracing. We conducted think-aloud interviews of introductory programming students solving EiPE questions, varying which interventions each student was taught. Some participants were interviewed multiple times throughout the semester to observe any changes in behavior over time. Findings. Identifying beacons and the name of variable roles were rarely helpful, as they did not encourage students to integrate their understanding of that piece in relation to other lines of code. However, prompting students to explain each variable’s purpose helped them focus on useful subsets of the code, which helped manage cognitive load. Tracing was helpful when students incorrectly recognized common programming patterns or made mistakes comprehending syntax (text-surface). Prompting students to pick inputs that potentially contradicted their current understanding of the code was found to be a simple approach to them effectively selecting inputs to trace. Abstract tracing helped students see high-level, functional relationships between variables. In addition, we observed student spontaneously sketching algorithmic visualizations that similarly helped them see relationships between variables. Implications. Because students can get stuck at many points in the process of code comprehension, there seems to be no silver bullet technique that helps in every circumstance. Instead, effective instruction for code comprehension will likely involve teaching a collection of techniques. In addition to these techniques, meta-knowledge about when to apply each technique will need to be learned, but that is left for future research. At present, we recommend teaching a bottom-up, concrete-to-abstract approach.
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Program Comprehension with Physical Computing: A Structure, Function, and Behavior Analysis of Think-Alouds with High School Students.
Comprehending programs is key to learning programming. Previous studies highlight novices’ naive approaches to comprehend ing the structural, functional, and behavioral aspects of programs. And yet, with the majority of them examining on-screen program ming environments, we barely know about program comprehension within physical computing—a common K-12 programming context. In this study, we qualitatively analyzed think-aloud inter view videos of 22 high school students individually comprehending a given text-based Arduino program while interacting with its corresponding functional physical artifact to answer two questions: 1) How do novices comprehend the given text-based Arduino pro gram? And, 2) What role does the physical artifact play in program comprehension? We found that novices mostly approached the program bottom-up, initially comprehending structural and later functional aspects, along different granularities. The artifact provided two distinct modes of engagement, active and interactive, that supported the program’s structural and functional comprehension. However, behavioral comprehension i.e. understanding program execution leading to the observed outcome was inaccessible to many. Our findings extend program comprehension literature in two ways: (a) it provides one of the very few accounts of high school students’ code comprehension in a physical computing con text, and, (b) it highlights the mediating role of physical artifacts in program comprehension. Further, they point directions for future pedagogical and tool designs within physical computing to better support students’ distributed program comprehension.
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- Award ID(s):
- 1742140
- PAR ID:
- 10309426
- Date Published:
- Journal Name:
- Proceedings of the 26th ACM Conference on Innovation and Technology in Computer Science Education
- Volume:
- 1
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Background: Code comprehension research has identified gaps between the strategies experts and novices use in comprehending code. In computer science (CS) education, code comprehension has recently received increased attention, and research has identified correlations between code comprehension and code writing. While there is a long history of identifying expert code-comprehension strategies, there has been less work to understand and support the incremental development of code comprehension expertise. Purpose: The goal of the paper is to identify potential code-comprehension strategies that educators could teach students. Methods: In this paper, I analyze and present examples from a novice programmer engaged in a code-comprehension task. Findings: I identify five code-comprehension strategies that overlap with previously identified, expert code-comprehension strategies. While an expert would use these strategies to produce correct inferences about code, I primarily examine a novice’s unsuccessful attempts to comprehend code using these strategies. Implications: My case study provides an existence proof that shows that these five strategies can be used by a novice. This is essential for identifying potential strategies to teach novices. My primary empirical contribution is identifying potential building blocks for developing code-comprehension expertise. My primary theoretical contribution is proposing to build code-comprehension pedagogy on specific, expert strategies that I show are usable by a novice. More broadly, I hope to encourage CS education researchers to focus on understanding the complex processes of learning that occur in between the end points of novice and expert.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3430665.3456371
