计算机科学
反转课堂
工程教育
功率(物理)
数学教育
情报检索
工程管理
工程类
数学
物理
量子力学
作者
Mo Wang,Minjuan Wang,Xin Xu,Lanqing Yang,Dunbo Cai,Minghao Yin
出处
期刊:IEEE Transactions on Learning Technologies
[Institute of Electrical and Electronics Engineers]
日期:2023-10-16
卷期号:17: 629-641
被引量:37
标识
DOI:10.1109/tlt.2023.3324714
摘要
This research project investigates the impact of prompt engineering, a key aspect of chat generative pretrained transformer (ChatGPT), on college students' information retrieval in flipped classrooms. In recent years, an increasing number of students have been using AI-based tools, such as ChatGPT rather than traditional research engines to learn and to complete course assignments. Despite this growing trend, previous research has largely overlooked the influence of prompt engineering on students' use of ChatGPT and effective strategies for improving the quality of information retrieval in learning settings. To address this research gap, this study examines the information quality obtained from ChatGPT in a flipped classroom by evaluating its effectiveness in task completion among 26 novice undergraduates from the same major and cohort. The experimental results provide evidence that proficient mastery of prompt engineering improves the quality of information obtained by students using ChatGPT. Consequently, by acquiring proficiency in prompt engineering, students can maximize the positive impact of ChatGPT, obtain high-quality information, and enhance their learning efficiency in flipped classrooms.
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