Developing a Code of Practice for Using Data in Wellbeing Support

A. Cormack, D. Reeve
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引用次数: 1

Abstract

With student and staff wellbeing a growing concern, several authors have asked whether existing data might help institutions provide better support. By analogy with the established field of Learning Analytics, this might involve identifying causes of stress, improving access to information for those who need it, suggesting options, providing rapid feedback, even early warning of problems. But just investigating the possibility of such uses can create significant risks for individuals: feelings of creepiness or surveillance making wellbeing worse, inappropriate data visibility destroying trust, assessments or interventions becoming self-fulfilling prophecies. To help institutions decide whether and how to explore this area, and to reassure individuals that this is being done safely, we propose a Wellbeing Analytics Code of Practice. This starts from an existing Learning Analytics Code, confirms that its concerns and mitigations remain relevant, and adds additional safeguards and tools for the wellbeing context. These are derived from a detailed analysis of European and UK data protection law, extracting all rules and safeguards mentioned in relation to health data. We also develop context-specific tools for managing risk and evaluating data sources. Early feedback suggests that these documents will indeed increase confidence that this important area can be safely explored.
制定在福利支持中使用数据的行为准则
随着学生和教职员工的福祉日益受到关注,一些作者提出了这样的问题:现有的数据是否可以帮助院校提供更好的支持。与已建立的学习分析领域类似,这可能涉及识别压力的原因,为那些需要信息的人改善获取信息的途径,建议选项,提供快速反馈,甚至是问题的早期预警。但仅仅调查这些用途的可能性就会给个人带来重大风险:令人毛骨悚然的感觉或监控会让幸福感变得更糟,不恰当的数据可见性会破坏信任,评估或干预会成为自我实现的预言。为了帮助机构决定是否以及如何探索这一领域,并让个人放心,我们提出了《健康分析实践准则》。这从现有的学习分析代码开始,确认其关注和缓解措施仍然相关,并为健康环境增加额外的保障措施和工具。这些是根据对欧洲和英国数据保护法的详细分析得出的,提取了与健康数据有关的所有规则和保障措施。我们还开发了用于管理风险和评估数据源的特定环境工具。早期的反馈表明,这些文件确实会增加人们对这一重要领域能够安全探索的信心。
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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