调试
计算机科学
计算思维
领域(数学)
编码(社会科学)
探索性研究
编码(集合论)
多媒体
数学教育
人机交互
人工智能
心理学
程序设计语言
社会学
集合(抽象数据类型)
统计
纯数学
数学
人类学
作者
Abdessalam Ouaazki,Kristoffer Bergram,Adrian Holzer
标识
DOI:10.1109/tale56641.2023.10398358
摘要
Given the pervasive reliance on technology in modern society, teaching Computational Thinking (CT) abilities is becoming increasingly relevant. These abilities, such as modeling and coding, have become crucial for a larger audience of students, not only those who wish to become software engineers or computer scientists. Recent advances in Large Language Models (LLMs), such as ChatGPT, provide powerful assistance to complete computational tasks, by simplifying code generation and debugging, and potentially enhancing interactive learning. However, it is not clear if these advances make CT tasks more accessible and inclusive for all students, or if they further contribute to a digital skills divide, favoring the top students. To address this gap, we have created and evaluated a novel learning scenario for transversal CT skills that leveraged LLMs as assistants. We conducted an exploratory field study during the spring semester of 2022, to assess the effectiveness and user experience of LLM-augmented learning. Our results indicate that the usage of ChatGPT as a learning assistant improves learning outcomes. Furthermore, contrary to our predictions, the usage of ChatGPT by students does not depend on prior CT capabilities and as such does not seem to exacerbate prior inequalities.
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