Prompting for Comprehension: Exploring the Intersection of Explain in Plain English Questions and Prompt Writing Article Swipe
David H. Smith
,
Paul Denny
,
Max Fowler
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1145/3657604.3662039
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1145/3657604.3662039
Learning to program requires the development of a variety of skills including the ability to read, comprehend, and communicate the purpose of code. In the age of large language models (LLMs), where code can be generated automatically, developing these skills is more important than ever for novice programmers. The ability to write precise natural language descriptions of desired behavior is essential for eliciting code from an LLM, and the code that is generated must be understood in order to evaluate its correctness and suitability. In introductory computer science courses, a common question type used to develop and assess code comprehension skill is the 'Explain in Plain English' (EiPE) question. In these questions, students are shown a segment of code and asked to provide a natural language description of that code's purpose. The adoption of EiPE questions at scale has been hindered by: 1) the difficulty of automatically grading short answer responses and 2) the ability to provide effective and transparent feedback to students. To address these shortcomings, we explore and evaluate a grading approach where a student's EiPE response is used to generate code via an LLM, and that code is evaluated against test cases to determine if the description of the code was accurate. This provides a scalable approach to creating code comprehension questions and enables feedback both through the code generated from a student's description and the results of test cases run on that code. We evaluate students' success in completing these tasks, their use of the feedback provided by the system, and their perceptions of the activity.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1145/3657604.3662039
- OA Status
- gold
- Cited By
- 15
- References
- 38
- Related Works
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- OpenAlex ID
- https://openalex.org/W4400642852
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4400642852Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1145/3657604.3662039Digital Object Identifier
- Title
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Prompting for Comprehension: Exploring the Intersection of Explain in Plain English Questions and Prompt WritingWork title
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-07-09Full publication date if available
- Authors
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David H. Smith, Paul Denny, Max FowlerList of authors in order
- Landing page
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https://doi.org/10.1145/3657604.3662039Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://doi.org/10.1145/3657604.3662039Direct OA link when available
- Concepts
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Intersection (aeronautics), Plain English, Comprehension, Computer science, Natural language processing, Linguistics, Engineering, Programming language, Transport engineering, PhilosophyTop concepts (fields/topics) attached by OpenAlex
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15Total citation count in OpenAlex
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2025: 14, 2024: 1Per-year citation counts (last 5 years)
- References (count)
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38Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.completing | 242 |
| abstract_inverted_index.developing | 37 |
| abstract_inverted_index.difficulty | 144 |
| abstract_inverted_index.questions, | 111 |
| abstract_inverted_index.understood | 75 |
| abstract_inverted_index.communicate | 18 |
| abstract_inverted_index.comprehend, | 16 |
| abstract_inverted_index.correctness | 81 |
| abstract_inverted_index.description | 126, 199, 226 |
| abstract_inverted_index.development | 5 |
| abstract_inverted_index.perceptions | 256 |
| abstract_inverted_index.transparent | 159 |
| abstract_inverted_index.descriptions | 55 |
| abstract_inverted_index.introductory | 85 |
| abstract_inverted_index.programmers. | 47 |
| abstract_inverted_index.suitability. | 83 |
| abstract_inverted_index.automatically | 146 |
| abstract_inverted_index.comprehension | 99, 213 |
| abstract_inverted_index.shortcomings, | 166 |
| abstract_inverted_index.automatically, | 36 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.97414257 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |