在纤维制作的算法中学习:后人文主义视角:捕捉人与材料的协作

IF 4.2 2区 教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH
Anna Keune
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引用次数: 0

摘要

计算机支持的协作学习研究的一个主要任务是研究人们如何在协作环境中学习,以指导学习捕捉和设计方法的开发。计算机支持的协作学习研究具有研究物质世界如何在协作学习中发挥作用的传统。在这一领域,正在出现一种物质转向,即考虑数字和有形技术如何积极促进协作学习过程。当先进的算法以并不总是透明的方式融入教育环境时,研究有形材料如何以可视和算法的方式产生协作学习就显得尤为重要。然而,捕捉非人类代理如何参与协作学习所需的方法仍未得到充分发展。本研究以当前研究协作学习中的材料的 CSCL 研究为基础,引入后人文主义视角,旨在从方法论上去中心化人类,并从实证角度探究这些视角是否以及如何促进对协作学习过程的实证理解。本研究以纤维工艺(如编织和布料处理)作为计算学习的背景,利用视频数据对人类和非人类参与者在协作中的差异进行了后人文主义分析,以探究中学生和纤维工艺组件如何随着时间的推移执行算法。研究结果表明,青少年和工艺材料都对算法的执行做出了积极贡献。在编织过程中,算法变成了青少年与材料之间一个维度一个维度的重复动作。在织物操作中,算法则是各组成部分的重复汇合。通过分析人类和非人类之间的差异,将人类去中心化,将人与材料的合作作为一个富有成效的分析单位,以了解材料和人如何共同促进产生可被认可为计算性能的东西。这项研究的结果有助于计算机辅助学习研究中正在进行的关于如何在协作学习中考虑计算材料的讨论,并提出了一种新的方法来捕捉协作学习随着时间推移的物理扩展。这项研究对未来捕捉协作式计算学习和设计物理计算学习机会的研究具有重要意义,这些机会将展示不断发展的技术。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Learning within fiber-crafted algorithms: Posthumanist perspectives for capturing human-material collaboration

Learning within fiber-crafted algorithms: Posthumanist perspectives for capturing human-material collaboration

A key commitment of computer-supported collaborative learning research is to study how people learn in collaborative settings to guide development of methods for capture and design for learning. Computer-supported collaborative learning research has a tradition of studying how the physical world plays a part in collaborative learning. Within the field, a material turn is emerging that considers how digital and tangible technologies actively contribute to collaborative learning processes. Studying how tangible materials produce collaborative learning visibly and algorithmically is particularly important at a time when advanced algorithms are integrated into educational contexts in ways that are not always transparent. However, the needed methodologies for capturing how non-human agents take part in collaborative learning remains underdeveloped. The present study builds on current CSCL research that investigates materials in collaborative learning and introduces posthumanist perspectives with the aim to decenter humans methodologically and to probe empirically whether and how these perspectives contribute to empirical understanding of collaborative learning processes. Taking fiber crafts (e.g., weaving and fabric manipulation) as a context for computational learning, the present study conducted a posthumanist analysis of differences among human and non-human participants in collaboration using video data to investigate how middle school youths and fiber craft components performed algorithms over time. The findings show how both youths and craft materials actively contributed to the performance of algorithms. In weaving, algorithms became repeated youth-material movements one dimension at a time. In fabric manipulation, algorithms became a repeated confluence of component parts. Decentering humans through an analysis of differences among human and non-human introduced human-material collaboration as a productive unit of analysis for understanding how materials and people together contribute to producing what can be recognized as computational performance. The findings of this research contribute to ongoing conversations in CSCL research on how computational materials can be considered in collaborative learning and present a new approach to capture collaborative learning as physical expansion over time. The study has implications for future research on capturing collaborative computational learning and designing physical computational learning opportunities that show technology as evolving.

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来源期刊
CiteScore
8.00
自引率
18.60%
发文量
20
期刊介绍: An official publication of the International Society of the Learning Sciences, the International Journal of Computer-Supported Collaborative Learning (IJCSCL) fosters a deep understanding of the nature, theory, and practice of computer-supported collaborative learning (CSCL). The journal serves as a forum for experts from such disciplines as education, computer science, information technology, psychology, communications, linguistics, anthropology, sociology, and business. Articles investigate how to design the technological settings for collaboration and how people learn in the context of collaborative activity.
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