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  1. Student Privacy in Learning Analytics: An Information Ethics Perspective.Alan Rubel & Kyle M. L. Jones - 2016 - The Information Society 32 (2):143-159.
    In recent years, educational institutions have started using the tools of commercial data analytics in higher education. By gathering information about students as they navigate campus information systems, learning analytics “uses analytic techniques to help target instructional, curricular, and support resources” to examine student learning behaviors and change students’ learning environments. As a result, the information educators and educational institutions have at their disposal is no longer demarcated by course content and assessments, and old boundaries between information used for assessment (...)
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    Data Analytics in Higher Education: Key Concerns and Open Questions.Alan Rubel & Kyle M. L. Jones - 2017 - University of St. Thomas Journal of Law and Public Policy 1 (11):25-44.
    “Big Data” and data analytics affect all of us. Data collection, analysis, and use on a large scale is an important and growing part of commerce, governance, communication, law enforcement, security, finance, medicine, and research. And the theme of this symposium, “Individual and Informational Privacy in the Age of Big Data,” is expansive; we could have long and fruitful discussions about practices, laws, and concerns in any of these domains. But a big part of the audience for this symposium is (...)
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  3. A Matter of Trust: : Higher Education Institutions as Information Fiduciaries in an Age of Educational Data Mining and Learning Analytics.Kyle M. L. Jones, Alan Rubel & Ellen LeClere - forthcoming - JASIST: Journal of the Association for Information Science and Technology.
    Higher education institutions are mining and analyzing student data to effect educational, political, and managerial outcomes. Done under the banner of “learning analytics,” this work can—and often does—surface sensitive data and information about, inter alia, a student’s demographics, academic performance, offline and online movements, physical fitness, mental wellbeing, and social network. With these data, institutions and third parties are able to describe student life, predict future behaviors, and intervene to address academic or other barriers to student success (however defined). Learning (...)
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  4. The Temptation of Data-Enabled Surveillance: Are Universities the Next Cautionary Tale?Alan Rubel & Kyle M. L. Jones - 2020 - Communications of the Acm 4 (63):22-24.
    There is increasing concern about “surveillance capitalism,” whereby for-profit companies generate value from data, while individuals are unable to resist (Zuboff 2019). Non-profits using data-enabled surveillance receive less attention. Higher education institutions (HEIs) have embraced data analytics, but the wide latitude that private, profit-oriented enterprises have to collect data is inappropriate. HEIs have a fiduciary relationship to students, not a narrowly transactional one (see Jones et al, forthcoming). They are responsible for facets of student life beyond education. In addition to (...)
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