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Early programming courses, such as CS1, are an important time to capture the interest of the students while imparting important technical knowledge. Yet many CS1 sections use contrived assignments and activities that tend to make students uninterested and doubt the usefulness of the content. We demonstrate that one can make an interesting CS1 experience for students by coupling interesting datasets with visual representations and interactive applications. Our approach enables teaching an engaging early programming course without changing the content of that course. This approach relies on the BRIDGES system that has been under development for the past 5 years; BRIDGES provides easy access to datasets and interactive applications. The assignments we present are all scaffolded to be directly integrated into most early programming courses to make routine topics more compelling and exciting.
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Use of Large Language Models for Extracting Knowledge Components in CS1 Programming Exercises
programming concepts in programming assignments in a CS1 course. We seek to answer the following research questions: RQ1. How effectively can large language models identify knowledge components in a CS1 course from programming assignments? RQ2. Can large language models be used to extract program-level knowledge components, and how can the information be used to identify students’ misconceptions? Preliminary results demonstrated a high similarity between course-level knowledge components retrieved from a large language model and that of an expert-generated list.
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- PAR ID:
- 10501958
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- Special Interest Group on Computer Science Education bulletin
- ISBN:
- 9798400704246
- Page Range / eLocation ID:
- 1762 to 1763
- Format(s):
- Medium: X
- Location:
- Portland OR USA
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3626253.3635592
