A Preliminary Data-driven Analysis of Common Errors Encountered by
Novice SPARC Programmers
- URL: http://arxiv.org/abs/2208.03090v1
- Date: Fri, 5 Aug 2022 10:48:25 GMT
- Title: A Preliminary Data-driven Analysis of Common Errors Encountered by
Novice SPARC Programmers
- Authors: 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)
- Abstract summary: This study focuses on the types and difficulty of programming errors encountered by K-12 students using ASP.
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.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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