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    基于隐私伦理视角的国外学习分析政策分析

    Analysis of Foreign Policies of Learning Analysis from Perspective of Privacy Ethics

【作      者】:

王 楠, 苟江凤


【关 键 词 】:

隐私伦理;学习分析政策;学习分析;比较研究


【栏      目】:

历史与国际比较


【中文摘要】:

随着学习分析技术应用日益普遍,与其相关的隐私伦理问题也受到重视。由于社会发展、隐私意识等原因,我国基于隐私伦理视角的学习分析政策研究和实践较为滞后。研究从隐私伦理视角出发,基于多项已有研究提出学习分析政策的隐私伦理分析框架,采用比较研究方法,选取国外近年典型学习分析政策作为样本,开展基于隐私伦理视角的政策分析研究。研究发现:隐私伦理是学习分析政策制定的主要目的之一;数据主体、数据客体、合作伙伴等利益相关者针对隐私伦理问题的责任上有着差异化的体现;在数据应用层面,政策普遍关注数据透明度、数据匿名化、知情同意、隐私泄露、访问权限等方面;同时,最小化不利影响和数据有效性是学习分析可持续发展的重要因素。研究认为,积极制定学习分析政策、区分利益相关者责任权利、建立系统化数据应用隐私伦理保障以及避免过度干预和数据偏见等能够支持学习分析可持续发展。


【英文摘要】:

With the increasing popularity of learning analytics, the privacy ethics related to learning analytics has also been paid attention to. Due to the social development and privacy awareness, the research and practice of learning analysis policies based on privacy ethics in China lag behind. From the perspective of privacy ethics, this study proposes a framework for privacy ethics analysis of learning analysis policies based on a number of existing studies. Then, by adopting the comparative research method, this study selects typical foreign learning analysis policies in recent years as samples to carry out policy analysis from the perspective of privacy ethics. The research finds that privacy ethics is one of the main purposes of the policy-making of learning analytics ; the stakeholders such as data subjects, data objects and partners have different responsibilities in privacy ethics issues. In terms of data application, policies generally focus on data transparency, data anonymity, informed consent, privacy disclosure, access rights and so on. Meanwhile, the minimization of adverse effects and data validity are important factors for the sustainable development of learning analytics.The study believes that the sustainable development of learning analysis can be supported by actively formulating learning analysis policies, distinguishing the responsibilities and rights of stakeholders, establishing a systematic guarantee of data application privacy ethics and avoiding excessive intervention and data bias can support.

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