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.

Related papers

• Finite Groundings for ASP with Functions: A Journey through Consistency [21.53198582611571]
  It is known that enhancing ASP with function symbols makes basic reasoning problems highly undecidable. We show reductions that give an intuition for the high level of undecidability. These insights allow for a more fine-grained analysis where we characterize ASP programs as "frugal" and "non-proliferous"
  arXiv  Detail & Related papers  (2024-05-08T11:50:08Z)
• Improving LLM Classification of Logical Errors by Integrating Error Relationship into Prompts [1.7095867620640115]
  A key aspect of programming education is understanding and dealing with error message. 'logical errors' in which the program operates against the programmer's intentions do not receive error messages from the compiler. We propose an effective approach for detecting logical errors with LLMs that makes use of relations among error types in the Chain-of-Thought and Tree-of-Thought prompts.
  arXiv  Detail & Related papers  (2024-04-30T08:03:22Z)
• Few-Shot Class Incremental Learning with Attention-Aware Self-Adaptive Prompt [58.880105981772324]
  We propose a novel framework named Attention-aware Self-adaptive Prompt (ASP) ASP encourages task-invariant prompts to capture shared knowledge by reducing specific information from the attention aspect. In summary, ASP prevents overfitting on base task and does not require enormous data in few-shot incremental tasks.
  arXiv  Detail & Related papers  (2024-03-14T20:34:53Z)
• CNL2ASP: converting controlled natural language sentences into ASP [4.747324197963404]
  We propose a novel tool, called CNL2ASP, for translating English sentences expressed in a controlled natural language (CNL) form into ASP.
  arXiv  Detail & Related papers  (2023-11-17T13:10:58Z)
• Can Large Language Models Understand Real-World Complex Instructions? [54.86632921036983]
  Large language models (LLMs) can understand human instructions, but struggle with complex instructions. Existing benchmarks are insufficient to assess LLMs' ability to understand complex instructions. We propose CELLO, a benchmark for evaluating LLMs' ability to follow complex instructions systematically.
  arXiv  Detail & Related papers  (2023-09-17T04:18:39Z)
• Dcc --help: Generating Context-Aware Compiler Error Explanations with Large Language Models [53.04357141450459]
  dcc --help was deployed to our CS1 and CS2 courses, with 2,565 students using the tool over 64,000 times in ten weeks. We found that the LLM-generated explanations were conceptually accurate in 90% of compile-time and 75% of run-time cases, but often disregarded the instruction not to provide solutions in code.
  arXiv  Detail & Related papers  (2023-08-23T02:36:19Z)
• Tools and Methodologies for Verifying Answer Set Programs [0.0]
  ASP is a powerful declarative programming paradigm commonly used for solving challenging search and optimization problems. As an approach to Knowledge Representation and Reasoning, ASP benefits from its simplicity, conciseness and rigorously defined semantics. My research is concerned with extending the theory and tools supporting the verification of ASP progams.
  arXiv  Detail & Related papers  (2022-08-05T10:50:21Z)
• Reducing language context confusion for end-to-end code-switching automatic speech recognition [50.89821865949395]
  We propose a language-related attention mechanism to reduce multilingual context confusion for the E2E code-switching ASR model. By calculating the respective attention of multiple languages, our method can efficiently transfer language knowledge from rich monolingual data.
  arXiv  Detail & Related papers  (2022-01-28T14:39:29Z)
• Aggregate Semantics for Propositional Answer Set Programs [14.135212040150389]
  We present and compare the main aggregate semantics that have been proposed for propositional ASP programs. We highlight crucial properties such as computational complexity and expressive power, and outline the capabilities and limitations of different approaches.
  arXiv  Detail & Related papers  (2021-09-17T17:38:55Z)
• On the Robustness of Language Encoders against Grammatical Errors [66.05648604987479]
  We collect real grammatical errors from non-native speakers and conduct adversarial attacks to simulate these errors on clean text data. Results confirm that the performance of all tested models is affected but the degree of impact varies.
  arXiv  Detail & Related papers  (2020-05-12T11:01:44Z)
• The ILASP system for Inductive Learning of Answer Set Programs [79.41112438865386]
  Our system learns Answer Set Programs, including normal rules, choice rules and hard and weak constraints. We first give a general overview of ILASP's learning framework and its capabilities. This is followed by a comprehensive summary of the evolution of the ILASP system.
  arXiv  Detail & Related papers  (2020-05-02T19:04:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.