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DeepCode: An Annotated Set of Instructional Code Examples to Foster Deep Code Comprehension and Learning

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Intelligent Tutoring Systems (ITS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13284))

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Abstract

We present here a novel instructional resource, called DeepCode, to support deep code comprehension and learning in intro-to-programming courses (CS1 and CS2). DeepCode is a set of instructional code examples which we call a codeset and which was annotated by our team with comments (e.g., explaining the logical steps of the underlying problem being solved) and related instructional questions that can play the role of hints meant to help learners think about and articulate explanations of the code. While DeepCode was designed primarily to serve our larger efforts of developing an intelligent tutoring system (ITS) that fosters the monitoring, assessment, and development of code comprehension skills for students learning to program, the codeset can be used for other purposes such as assessment, problem-solving, and in various other learning activities such as studying worked-out code examples with explanations and code visualizations. We present here the underlying principles, theories, and frameworks behind our design process, the annotation guidelines, and summarize the resulting codeset of 98 annotated Java code examples which include 7,157 lines of code (including comments), 260 logical steps, 260 logical step details, 408 statement level comments, and 590 scaffolding questions.

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Acknowledgments

This work was supported by the National Science Foundation under award 1822816. All findings and opinions expressed or implied are solely the authors’.

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Correspondence to Vasile Rus .

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Rus, V., Brusilovsky, P., Tamang, L.J., Akhuseyinoglu, K., Fleming, S. (2022). DeepCode: An Annotated Set of Instructional Code Examples to Foster Deep Code Comprehension and Learning. In: Crossley, S., Popescu, E. (eds) Intelligent Tutoring Systems. ITS 2022. Lecture Notes in Computer Science, vol 13284. Springer, Cham. https://doi.org/10.1007/978-3-031-09680-8_4

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  • DOI: https://doi.org/10.1007/978-3-031-09680-8_4

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