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

Zach Hansen
(University of Nebraska Omaha)
Hanxiang Du
(University of Florida)
Wanli Xing
(University of Florida)
Rory Eckel
(Texas Tech University)
Justin Lugo
(MRC LLC)
Yuanlin Zhang
(Texas Tech University)

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.

In Yuliya Lierler, Jose F. Morales, Carmine Dodaro, Veronica Dahl, Martin Gebser and Tuncay Tekle: Proceedings 38th International Conference on Logic Programming (ICLP 2022), Haifa, Israel, 31st July 2022 - 6th August 2022, Electronic Proceedings in Theoretical Computer Science 364, pp. 12–24.
Published: 4th August 2022.

ArXived at: https://dx.doi.org/10.4204/EPTCS.364.2 bibtex PDF
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