A Preliminary Data-driven Analysis of Common Errors Encountered by Novice SPARC Programmers

Answer Set Programming (ASP), a modern development of Logic Programming, enables a natural integration of Computing with STEM subjects. This integration addresses a widely acknowledged challenge in K-12 education, and early empirical results on ASP-based integration are promising. Although ASP is co...

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Veröffentlicht in:arXiv.org
Hauptverfasser: Hansen, Zach, Du, Hanxiang, Xing, Wanli, Eckel, Rory, Lugo, Justin, Zhang, Yuanlin
Format: Paper
Sprache:Englisch
Veröffentlicht: Ithaca Cornell University Library, arXiv.org 05.08.2022
Schlagworte:
Declarative programming
Empirical analysis
Error analysis
Imperative programming
Logic programming
Mathematical programming
Programming languages
Students
ISSN:2331-8422
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Zusammenfassung:Answer Set Programming (ASP), a modern development of Logic Programming, enables a natural integration of Computing with STEM subjects. This integration addresses a widely acknowledged challenge in K-12 education, and early empirical results on ASP-based integration are promising. Although ASP is considered a simple language when compared with imperative programming languages, programming errors can still be a significant barrier for students. This is particularly true for K-12 students who are novice users of ASP. Categorizing errors and measuring their difficulty has yielded insights into imperative languages like Java. However, little is known about the types and difficulty of errors encountered by K-12 students using ASP. To address this, we collected high school student programs submitted during a 4-session seminar teaching an ASP language known as SPARC. From error messages in this dataset, we identify a collection of error classes, and measure how frequently each class occurs and how difficult it is to resolve.
Bibliographie:SourceType-Working Papers-1
ObjectType-Working Paper/Pre-Print-1
content type line 50
ISSN:2331-8422
DOI:10.48550/arxiv.2208.03090