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

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 this dataset, we identify a collection of error classes, and measure how frequently each class occurs and how difficult it is to resolve.