通过使用 ICAPB 模型对机器人支持的协作学习中预先了解和参与的影响

IF 8.9 1区 教育学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Jia-Hua Zhao, Qi-Fan Yang, Li-Wen Lian, Xian-Yong Wu
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引用次数: 0

摘要

在计算机支持的协作学习环境中存在着一些挑战,例如可能会分散学生的注意力,使学生感到无聊和孤独,这可能会对协作学习和知识建构的质量产生不利影响。另一方面,作为一种创新的学习工具,实体机器人被认为是成功的协作学习促进者,可以提高学生的参与度,增强社会存在感,提高学习效果。同时,在机器人支持的协作学习(RSCL)环境中,基于布鲁姆分类法设计的任务能进一步确保学生的注意力和认知能力的提高。虽然一些研究人员在以往的机器人研究中探讨了如何保持学生的参与度,但由于缺乏常用的参与度评价注释方法,因此仍有一定难度。因此,本研究提出了交互式、建构式、主动式、被动式和行为式(ICAPB)参与度编码模型,将认知参与度和行为参与度结合起来,全面分析RSCL环境中的前置知识、学生参与度和学习成绩之间的关系。为了评估这种方法的有效性,我们在一所大学的急救课程中进行了一项实验。共有 36 名学生参与了这项研究,他们使用的是布卢姆分类法的协作机器人系统。结果表明,无论是高水平还是低水平的前置知识,都不会对学生的后测成绩产生显著影响。相反,学生的参与度对他们的学习成绩产生了明显的积极影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Impact of pre-knowledge and engagement in robot-supported collaborative learning through using the ICAPB model

Several challenges exist in computer-supported collaborative learning environments, such as the potential for distraction and student boredom and isolation, which may adversely affect the quality of collaborative learning and knowledge construction. On the other hand, as an innovative learning tool, physical robots are seen as successful collaborative learning facilitators that can raise student engagement, strengthen social presence, and boost learning results. Meanwhile, tasks designed based on Bloom's taxonomy further ensure students' attention and cognitive growth in robot-supported collaborative learning (RSCL) environments. Although some researchers have explored how to maintain engagement in previous studies on robots, it is still difficult due to the lack of a commonly employed annotation method for evaluating engagement. Therefore, this study proposed the interactive, constructive, active, passive, and behavioral (ICAPB) engagement coding model, combining cognitive and behavioral engagement, to comprehensively analyze the relationship between pre-knowledge, student engagement, and learning achievement in the RSCL environment. An experiment was conducted in a first-aid course at a university to evaluate the effectiveness of this approach. The study involved a total of 36 students using a collaborative robotic system with Bloom's taxonomy. The results showed that pre-knowledge, whether at a high or low level, did not significantly affect students' posttest scores. Instead, student engagement significantly positively impacted their learning achievement.

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来源期刊
Computers & Education
Computers & Education 工程技术-计算机:跨学科应用
CiteScore
27.10
自引率
5.80%
发文量
204
审稿时长
42 days
期刊介绍: Computers & Education seeks to advance understanding of how digital technology can improve education by publishing high-quality research that expands both theory and practice. The journal welcomes research papers exploring the pedagogical applications of digital technology, with a focus broad enough to appeal to the wider education community.
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