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Worked examples (solutions to typical programming problems presented as a source code in a certain language and are used to explain the topics from a programming class) are among the most popular types of learning content in programming classes. Most approaches and tools for presenting these examples to students are based on line-by-line explanations of the example code. However, instructors rarely have time to provide line-by-line explanations for a large number of examples typically used in a programming class. In this paper, we explore and assess a human-AI collaboration approach to authoring worked examples for Java programming. We introduce an authoring system for creating Java worked examples that generates a starting version of code explanations and presents it to the instructor to edit if necessary. We also present a study that assesses the quality of explanations created with this approach.
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Authoring Worked Examples for JAVA Programming with Human AI Collaboration
Worked examples are among the most popular types of learning content in programming classes. However, instructors rarely have time to provide line-by-line explanations for a large number of examples typically used in a programming class. In this paper, we explore and assess a human-AI collaboration approach to authoring worked examples for Java programming. We introduce an authoring system for creating Java worked examples that generate a starting version of code explanations and presents it to the instructor to edit if necessary. We also present a study that assesses the quality of explanations created with this approach.
more »
« less
- Award ID(s):
- 2213789
- PAR ID:
- 10518163
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- Proceedings of ACM/SIGAPP Symposium on Applied Computing, SAC 2024
- ISBN:
- 9798400702433
- Page Range / eLocation ID:
- 101 to 103
- Subject(s) / Keyword(s):
- Code Examples Authoring Tool Human-AI Collaboration
- Format(s):
- Medium: X
- Location:
- Avila Spain
- 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/10.1145/3605098.3636160
