设计分析协作素养和学生赋权

M. Worsley, Khalil J. Anderson, N. Melo, JooYoung Jang
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引用次数: 8

摘要

作为21世纪的一项重要技能,合作受到了全球的关注。虽然研究人员研究协作已经有近一个世纪了,但这个领域正在研究的许多问题都忽视了学生学习如何阅读和应对不同协作环境的必要性。现有的研究侧重于记录预测协作体验有效性的各种因素,或者关注当下改变用户行为。这些都是有价值的研究工作,有助于发展我们对合作的理论认识。然而,也有必要将学生的看法和经验与合作作为一个重要的研究领域。基于对131名大学生的调查,我们发现学生合作相关的关注可以在七个不同的类别或维度上表现出来:气候、兼容性、沟通、冲突、背景、贡献和建设性。这些类别扩展了先前对协作的研究,可以帮助该领域确保未来的协作分析工具被设计成支持学生思考和利用协作的方式。最后,我们在我们的协作分析工具BLINC中描述了许多这些维度的实例,并建议这七个维度对于重新定位多模式学习分析(MMLA)和协作分析社区具有指导意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Designing Analytics for Collaboration Literacy and Student Empowerment
Collaboration has garnered global attention as an important skill for the 21st century. While researchers have been doing work on collaboration for nearly a century, many of the questions that the field is investigating overlook the need for students to learn how to read and respond to different collaborative settings. Existing research focuses on chronicling the various factors that predict the effectiveness of a collaborative experience, or on changing user behaviour in the moment. These are worthwhile research endeavours for developing our theoretical understanding of collaboration. However, there is also a need to centre student perceptions and experiences with collaboration as an important area of inquiry. Based on a survey of 131 university students, we find that student collaboration-related concerns can be represented across seven different categories or dimensions: Climate, Compatibility, Communication, Conflict, Context, Contribution, and Constructive. These categories extend prior research on collaboration and can help the field ensure that future collaboration analytics tools are designed to support the ways that students think about and utilize collaboration. Finally, we describe our instantiation of many of these dimensions in our collaborative analytics tool, BLINC, and suggest that these seven dimensions can be instructive for re-orienting the Multimodal Learning Analytics (MMLA) and collaboration analytics communities.
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