Feedback sources in essay writing: peer-generated or AI-generated feedback?

IF 8.6 1区 教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH
Seyyed Kazem Banihashem, Nafiseh Taghizadeh Kerman, Omid Noroozi, Jewoong Moon, Hendrik Drachsler
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Abstract

Peer feedback is introduced as an effective learning strategy, especially in large-size classes where teachers face high workloads. However, for complex tasks such as writing an argumentative essay, without support peers may not provide high-quality feedback since it requires a high level of cognitive processing, critical thinking skills, and a deep understanding of the subject. With the promising developments in Artificial Intelligence (AI), particularly after the emergence of ChatGPT, there is a global argument that whether AI tools can be seen as a new source of feedback or not for complex tasks. The answer to this question is not completely clear yet as there are limited studies and our understanding remains constrained. In this study, we used ChatGPT as a source of feedback for students’ argumentative essay writing tasks and we compared the quality of ChatGPT-generated feedback with peer feedback. The participant pool consisted of 74 graduate students from a Dutch university. The study unfolded in two phases: firstly, students’ essay data were collected as they composed essays on one of the given topics; subsequently, peer feedback and ChatGPT-generated feedback data were collected through engaging peers in a feedback process and using ChatGPT as a feedback source. Two coding schemes including coding schemes for essay analysis and coding schemes for feedback analysis were used to measure the quality of essays and feedback. Then, a MANOVA analysis was employed to determine any distinctions between the feedback generated by peers and ChatGPT. Additionally, Spearman’s correlation was utilized to explore potential links between the essay quality and the feedback generated by peers and ChatGPT. The results showed a significant difference between feedback generated by ChatGPT and peers. While ChatGPT provided more descriptive feedback including information about how the essay is written, peers provided feedback including information about identification of the problem in the essay. The overarching look at the results suggests a potential complementary role for ChatGPT and students in the feedback process. Regarding the relationship between the quality of essays and the quality of the feedback provided by ChatGPT and peers, we found no overall significant relationship. These findings imply that the quality of the essays does not impact both ChatGPT and peer feedback quality. The implications of this study are valuable, shedding light on the prospective use of ChatGPT as a feedback source, particularly for complex tasks like argumentative essay writing. We discussed the findings and delved into the implications for future research and practical applications in educational contexts.

Abstract Image

论文写作中的反馈来源:同伴生成的反馈还是人工智能生成的反馈?
同伴反馈是一种有效的学习策略,尤其是在教师工作量大的大班教学中。然而,对于撰写议论文等复杂的任务,如果没有同伴的支持,可能无法提供高质量的反馈,因为这需要高水平的认知处理、批判性思维能力以及对主题的深刻理解。随着人工智能(AI)的蓬勃发展,特别是在 ChatGPT 出现之后,全球都在争论人工智能工具是否可以被视为复杂任务的新反馈来源。由于研究有限,我们对这一问题的理解仍然有限,因此答案还不完全清楚。在本研究中,我们使用 ChatGPT 作为学生议论文写作任务的反馈源,并比较了 ChatGPT 生成的反馈与同伴反馈的质量。参与者包括来自荷兰一所大学的 74 名研究生。研究分两个阶段展开:首先,在学生就给定主题之一撰写论文时收集他们的论文数据;随后,通过让同伴参与反馈过程并使用 ChatGPT 作为反馈源,收集同伴反馈和 ChatGPT 生成的反馈数据。我们采用了两种编码方案(包括论文分析编码方案和反馈分析编码方案)来衡量论文和反馈的质量。然后,采用 MANOVA 分析来确定同伴和 ChatGPT 生成的反馈之间的区别。此外,还采用了斯皮尔曼相关性分析来探讨论文质量与同伴和 ChatGPT 所产生的反馈之间的潜在联系。结果表明,由 ChatGPT 和同伴生成的反馈之间存在明显差异。ChatGPT 提供了更多描述性的反馈,包括关于文章如何写作的信息,而同伴提供的反馈则包括关于发现文章中问题的信息。从总体结果来看,ChatGPT 和学生在反馈过程中可能起到互补作用。关于作文质量与 ChatGPT 和同伴提供的反馈质量之间的关系,我们发现总体上没有显著的关系。这些发现意味着,论文的质量并不影响 ChatGPT 和同伴反馈的质量。这项研究的意义非常宝贵,它揭示了将 ChatGPT 用作反馈源的前景,尤其是对于议论文写作这样的复杂任务。我们讨论了研究结果,并深入探讨了其对未来研究和教育环境中实际应用的影响。
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来源期刊
CiteScore
19.30
自引率
4.70%
发文量
59
审稿时长
76.7 days
期刊介绍: This journal seeks to foster the sharing of critical scholarly works and information exchange across diverse cultural perspectives in the fields of technology-enhanced and digital learning in higher education. It aims to advance scientific knowledge on the human and personal aspects of technology use in higher education, while keeping readers informed about the latest developments in applying digital technologies to learning, training, research, and management.
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