[论文解读] Using ChatGPT for Science Learning: A Study on Pre-service Teachers' Lesson Planning

Gyeong-Geon Lee, Xiaoming Zhaı|arXiv (Cornell University)|Jan 18, 2024

Online Learning and Analytics被引用 9

一句话总结

本研究分析韩国29名小学教师教育专业的在读师范生的课程计划，以了解 ChatGPT 如何整合到科学学习中、使用修改后的 TPACK 量表进行评估，并探讨教师的看法与担忧。

ABSTRACT

Despite the buzz around ChatGPT's potential, empirical studies exploring its actual utility in the classroom for learning remain scarce. This study aims to fill this gap by analyzing the lesson plans developed by 29 pre-service elementary teachers from a Korean university and assessing how they integrated ChatGPT into science learning activities. We first examined how the subject domains and teaching and learning methods/strategies were integrated with ChatGPT in the lesson plans. We then evaluated the lesson plans using a modified TPACK-based rubric. We further examined pre-service teachers' perceptions and concerns about integrating ChatGPT into science learning. Results show diverse applications of ChatGPT in different science domains. Fourteen types of teaching and learning methods/strategies were identified in the lesson plans. On average, the pre-service teachers' lesson plans scored high on the modified TPACK-based rubric, indicating a reasonable envisage of integrating ChatGPT into science learning, particularly in 'instructional strategies & ChatGPT'. However, they scored relatively lower on exploiting ChatGPT's functions toward its full potential compared to other aspects. The study also identifies both appropriate and inappropriate use cases of ChatGPT in lesson planning. Pre-service teachers anticipated ChatGPT to afford high-quality questioning, self-directed learning, individualized learning support, and formative assessment. Meanwhile, they also expressed concerns about its accuracy and the risks that students may be overly dependent on ChatGPT. They further suggested solutions to systemizing classroom dynamics between teachers and students. The study underscores the need for more research on the roles of generative AI in actual classroom settings and provides insights for future AI-integrated science learning.

研究动机与目标

• 评估科目领域以及教学/学习方法如何在课程计划中与 ChatGPT 整合。
• 使用修改后的基于 TPACK 的量表评估课程计划以衡量整合质量。
• 识别由 ChatGPT 在科学课程中促进的教学/学习方法类型。
• 探讨在读师范生对 AI 辅助科学学习的看法、担忧及提出的解决方案。

提出的方法

• 分析来自 29 名小学教师教育专业在读教师的课程计划，以识别领域、方法及 ChatGPT 的整合。
• 使用修改后的基于 TPACK 的量表评估 ChatGPT 在科学学习计划中的整合程度。
• 列举并分类由 ChatGPT 促进的教学/学习方法/策略（识别为14种类型）。
• 考察参与者对 ChatGPT 在学习中的作用、准确性和依赖性的看法、期望与担忧。
• 讨论在真实课堂情境中 AI 整合科学学习的影响及未来研究方向。

实验结果

研究问题

• RQ1 在科目领域与教学策略上，预服务教师如何将 ChatGPT 整合到科学课程计划中？
• RQ2 通过修改后的 TPACK 量表衡量 ChatGPT 融入科学学习的质量如何？
• RQ3 ChatGPT 在课程计划中促成了哪些教学/学习方法/策略？
• RQ4 对 ChatGPT 在科学学习中的作用，预服务教师的看法、担忧与建议是什么？

主要发现

• ChatGPT 在不同科学领域的应用呈现多样性。
• 在课程计划中识别出 14 种教学与学习方法/策略。
• 平均而言，课程计划在修改后的基于 TPACK 的量表上得分较高，表明对 ChatGPT 融入科学学习的整合有较为合理的预期，尤其是在教学策略与 ChatGPT 方面。
• 在读师范生预期 ChatGPT 将支持高质量提问、自主学习、个性化学习支持和形成性评估。
• 他们对准确性以及学生过度依赖 ChatGPT 的风险表示担忧。
• 建议包括将教师与学生之间的课堂互动系统化。