Exploring How Multiple Levels of GPT-Generated Programming Hints Support or Disappoint Novices

• URL: http://arxiv.org/abs/2404.02213v1
• Date: Tue, 2 Apr 2024 18:05:26 GMT
• Title: Exploring How Multiple Levels of GPT-Generated Programming Hints Support or Disappoint Novices
• Authors: Ruiwei Xiao, Xinying Hou, John Stamper,
• Abstract summary: We investigated whether different levels of hints can support students' problem-solving and learning. We conducted a think-aloud study with 12 novices using the LLM Hint Factory. We discovered that high-level natural language hints alone can be helpless or even misleading.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent studies have integrated large language models (LLMs) into diverse educational contexts, including providing adaptive programming hints, a type of feedback focuses on helping students move forward during problem-solving. However, most existing LLM-based hint systems are limited to one single hint type. To investigate whether and how different levels of hints can support students' problem-solving and learning, we conducted a think-aloud study with 12 novices using the LLM Hint Factory, a system providing four levels of hints from general natural language guidance to concrete code assistance, varying in format and granularity. We discovered that high-level natural language hints alone can be helpless or even misleading, especially when addressing next-step or syntax-related help requests. Adding lower-level hints, like code examples with in-line comments, can better support students. The findings open up future work on customizing help responses from content, format, and granularity levels to accurately identify and meet students' learning needs.

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