Answer Set Programming (ASP), a modern development of Logic Programming, is one of the foremost paradigms in the important branch of artificial intelligence (AI) known as Knowledge Representation and Reasoning. ASP enables a natural integration of Computing and AI education 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. And while categorizing errors and measuring their difficulty has yielded insights into imperative languages like Java, 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, then measure how frequently each error occurs and how difficult it is to resolve. We find that errors related to the sort system of SPARC are the most worthy of emphasis from educators based on their frequency of occurrence and resolution difficulty.