使用聊天机器人提供形成性反馈：关于内在动机、认知负荷和学习成绩的纵向研究

IF 2.9 3区教育学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

IEEE Transactions on Learning Technologies Pub Date : 2024-02-08 DOI:10.1109/TLT.2024.3364015

Jiaqi Yin;Tiong-Thye Goh;Yi Hu

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引用次数: 0

摘要

本研究旨在考察基于聊天机器人的形成性反馈对内在动机、认知负荷和学习成绩的可持续影响。研究采用纵向准实验设计，共有 173 名本科生参加。实验采用被试间设计。学生可以从聊天机器人或教师那里获得形成性反馈。利用线性混合模型和 t 检验进行数据分析，结果显示如下。首先，随着时间的推移，基于聊天机器人的反馈提高了学习兴趣、感知选择和价值，同时降低了感知压力。其次，基于聊天机器人的反馈能有效减轻认知负荷，尤其是当学习内容涉及概念性或难度较大的知识时。最后，与基于教师的反馈相比，基于聊天机器人的反馈在支持掌握应用型知识方面更加高效和有效。这项研究对聊天机器人的设计具有实际意义，同时也丰富了在大规模课堂上提供持续性形成性反馈的方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Using a Chatbot to Provide Formative Feedback: A Longitudinal Study of Intrinsic Motivation, Cognitive Load, and Learning Performance

This study aimed to examine sustainable effects of chatbot-based formative feedback on intrinsic motivation, cognitive load, and learning performance. A longitudinal quasi-experimental design with 173 undergraduate students was conducted. The experiment is a between-subject design. Students either received formative feedback from a chatbot or a teacher. Utilizing linear mixed model and t-test for data analysis, results showed the following. First, chatbot-based feedback resulted in increased learning interest, perceived choice, and value while decreasing perceived pressure over time. Second, chatbot-based feedback was effective in reducing cognitive load, particularly when learning contents involved conceptual or difficult knowledge. Finally, chatbot-based feedback was found to be more efficient and effective in supporting the mastery of application-based knowledge compared with teacher-based feedback. This study has practical implications for the design of chatbots, and it also enriches the methods of providing ongoing formative feedback in large-scale classrooms.

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来源期刊

IEEE Transactions on Learning Technologies COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-

CiteScore

7.50

自引率

5.40%

发文量

审稿时长

>12 weeks

期刊介绍： The IEEE Transactions on Learning Technologies covers all advances in learning technologies and their applications, including but not limited to the following topics: innovative online learning systems; intelligent tutors; educational games; simulation systems for education and training; collaborative learning tools; learning with mobile devices; wearable devices and interfaces for learning; personalized and adaptive learning systems; tools for formative and summative assessment; tools for learning analytics and educational data mining; ontologies for learning systems; standards and web services that support learning; authoring tools for learning materials; computer support for peer tutoring; learning via computer-mediated inquiry, field, and lab work; social learning techniques; social networks and infrastructures for learning and knowledge sharing; and creation and management of learning objects.