A Picture Is Worth a Thousand Words: Exploring Diagram and Video-Based OOP Exercises to Counter LLM Over-Reliance
CoRR(2024)
摘要
Much research has highlighted the impressive capabilities of large language
models (LLMs), like GPT and Bard, for solving introductory programming
exercises. Recent work has shown that LLMs can effectively solve a range of
more complex object-oriented programming (OOP) exercises with text-based
specifications. This raises concerns about academic integrity, as students
might use these models to complete assignments unethically, neglecting the
development of important skills such as program design, problem-solving, and
computational thinking. To address this, we propose an innovative approach to
formulating OOP tasks using diagrams and videos, as a way to foster
problem-solving and deter students from a copy-and-prompt approach in OOP
courses. We introduce a novel notation system for specifying OOP assignments,
encompassing structural and behavioral requirements, and assess its use in a
classroom setting over a semester. Student perceptions of this approach are
explored through a survey (n=56). Generally, students responded positively to
diagrams and videos, with video-based projects being better received than
diagram-based exercises. This notation appears to have several benefits, with
students investing more effort in understanding the diagrams and feeling more
motivated to engage with the video-based projects. Furthermore, students
reported being less inclined to rely on LLM-based code generation tools for
these diagram and video-based exercises. Experiments with GPT-4 and Bard's
vision abilities revealed that they currently fall short in interpreting these
diagrams to generate accurate code solutions.
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