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)

TL;DR
This paper analyzes common errors made by novice high school students in ASP programming, specifically SPARC, to understand error types, their frequency, and difficulty, aiding in improving educational strategies.
Contribution
It presents a data-driven categorization of errors encountered by K-12 students in ASP programming, which is novel for this educational context.
Findings
Identified common error classes in student programs
Measured the frequency of each error class
Assessed the difficulty of resolving different error types
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…
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