Communications of the Acm 4 (63):22-24 (2020)

Alan Rubel
University of Wisconsin, Madison
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 classrooms, learning management systems, and libraries, HEIs manage dormitories, gyms, dining halls, health facilities, career advising, police departments, and student employment. HEIs collect and use student data in all of these domains, ostensibly to understand learner behaviors and contexts, improve learning outcomes, and increase institutional efficiency through “learning analytics” (LA). ID card swipes and Wi-Fi log-ins can track student location, class attendance, use of campus facilities, eating habits, and friend groups. Course management systems capture how students interact with readings, video lectures, and discussion boards. Application materials provide demographic information. These data are used to identify students needing support, predict enrollment demands, and target recruiting efforts. These are laudable aims. However, current LA practices may be inconsistent with HEIs’ fiduciary responsibilities. HEIs often justify LA as advancing student interests, but some projects advance primarily organizational welfare and institutional interests. Moreover, LA advances a narrow conception of student interests while discounting privacy and autonomy. Students are generally unaware of the information collected, do not provide meaningful consent, and express discomfort and resigned acceptance about HEI data practices, especially for non-academic data (see Jones et al. forthcoming). The breadth and depth of student information available, combined with their fiduciary responsibility, create a duty that HEIs exercise substantial restraint and rigorous evaluation in data collection and use.
Keywords privacy  education  learning analytics  higher education  big data  surveillance  technology ethics
Categories (categorize this paper)
Edit this record
Mark as duplicate
Export citation
Find it on Scholar
Request removal from index
Revision history

Download options

PhilArchive copy

 PhilArchive page | Other versions
External links

Setup an account with your affiliations in order to access resources via your University's proxy server
Configure custom proxy (use this if your affiliation does not provide a proxy)
Through your library

References found in this work BETA

No references found.

Add more references

Citations of this work BETA

No citations found.

Add more citations

Similar books and articles

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.
Neo-Liberal Reform and the Big Data University.Evan Selinger - 2017 - Foundations of Science 22 (2):377-380.
Facing the Future: Seeking Ethics for Everyday Surveillance. [REVIEW]David Lyon - 2001 - Ethics and Information Technology 3 (3):171-180.
Big Data Privacy and Ethical Challenges.Paulette Lacroix - 2019 - In Mowafa Househ, Andre W. Kushniruk & Elizabeth M. Borycki (eds.), Big Data, Big Challenges: A Healthcare Perspective: Background, Issues, Solutions and Research Directions. Springer Verlag. pp. 101-111.
Teaching Academic Integrity: The Missing Link.Mariya Chankova - 2020 - Journal of Academic Ethics 18 (2):155-173.


Added to PP index

Total views
42 ( #233,996 of 2,374,863 )

Recent downloads (6 months)
42 ( #17,427 of 2,374,863 )

How can I increase my downloads?


My notes