Student privacy in learning analytics: An information ethics perspective

Publication Information

APA Citation

Rubel, A. & Jones, K. M. L. (2016). Student privacy in learning analytics: An information ethics perspective. The Information Society, 32(2), 143–159. doi: 10.1080/01972243.2016.1130502

Description

Higher education institutions have started using big data analytics tools. By gathering information about students as they navigate information systems, learning analytics employs techniques to understand student behaviors and to improve instructional, curricular, and support resources and learning environments. However, learning analytics presents important moral and policy issues surrounding student privacy. We argue that there are five crucial questions about student privacy that we must address in order to ensure that whatever the laudable goals and gains of learning analytics, they are commensurate with respecting students’ privacy and associated rights, including (but not limited to) autonomy interests. We address information access concerns, the intrusive nature of information-gathering practices, whether or not learning analytics is justified given the potential distribution of consequences and benefits, and issues related to student autonomy. Finally, we question whether learning analytics advances the aims of higher education or runs counter to those goals.

Access the Publication
The Information Society

Share this Publication

Metrics and Citations

Alternative Metrics

SSRN Downloads: 316
Google Scholar Citations: 8

Scholarly Citations

Arnold, K. E., & Sclater, N. (2017). Student perceptions of their privacy in learning analytics applications. Proceedings of the Seventh International Learning Analytics & Knowledge Conference (pp. 66-69). Vancouver, BC: Association for Computing Machinery (ACM). Retrieved from http://dl.acm.org/citation.cfm?id=3027392

Roberts, L. D., Chang, V., & Gibson, D. (2017). Ethical considerations in adopting a university-and system-wide approach to data and learning analytics. In B. K. Daniel (ed.), Big data and learning analytics in higher education (pp. 89-108). Switzerland: Springer International Publishing. Retrieved from http://link.springer.com/chapter/10.1007/978-3-319-06520-5_7

Zeide, E. (2017). Unpacking student privacy. In C. Lang, G. Siemens, A. Wise, & D. Gašević (Eds.), Handbook of Learning Analytics (pp. 327-335). Alberta, CA: Society for Learning Analytics Research (SoLAR). doi: 10.18608/hla17.028

Sloan, R. H., & Warner, R. (2017). Relational privacy: surveillance, common knowledge, and coordination. University of St. Thomas Journal of Law and Public Policy, 11(1), 1-24. Retrieved from http://ir.stthomas.edu/ustjlpp/vol11/iss1/1/

Mittelstadt, B. (2016). Automation, algorithms, and politics: Auditing for transparency in content personalization systems. International Journal of Communication10(2016), 4991–5002. Retrieved from http://ijoc.org/index.php/ijoc/article/view/6267

Mittelstadt, B., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2): 1-21. Retrieved from http://philpapers.org/rec/MITTEO-12

Ridenour, L. (2016). “Indexing it all: The subject in the age of documentation, information, and data” [Review of the book Indexing it All: The Subject in the Age of Documentation, Information, and Data, by Ronald E. Day]. Knowledge Organization, 43(4), 306-309. Retrieved from http://www.isko.org/index.php

Prinsloo, P., & Slade, S. (2016). Student vulnerability, agency, and learning analytics: An exploration. Journal of Learning Analytics, 3(1), 159–182. Retrieved from https://epress.lib.uts.edu.au/journals/index.php/JLA/article/view/4447

Hong, N. W. W., Chew, E., & Sze-Meng, J. W. (2016). The review of educational robotics research and the need for real-world interaction analysis. 14th International Conference on Control, Automation, Robotics, and Vision (ICARCV), 1-6. Phuket, Thailand. doi: 10.1109/ICARCV.2016.7838707

Roberts, L. D., Howell, J. A., Seaman, K., & Gibson, D. C. (2016). Student attitudes toward learning analytics in higher education: “The Fitbit version of the learning world.” Frontiers in Psychology, 7 (Article 1959), 1-11. doi.org/10.3389/fpsyg.2016.01959

Daniel, B. K. (2016). Big data and learning analytics in higher education: Current theory and practice. Switzerland: Springer. doi: 10.1007/978-3-319-06520-5

Grey Literature Citations

Martin, J. (2016, Jan. 26). 2016 winter retreat focuses on course evaluation [Blog post]. Retrieved from https://teachingacademy.wisc.edu/2016-winter-retreat-focuses-on-course-evaluation/

Hickey, D. (Summer 2015). Introduction to Educational Data Sciences P574: Topical Seminar in Learning Sciences [Canvas course site]. Retrieved from https://iu.instructure.com/courses/1457882/pages/5-introduction-to-learning-analytics

University of Wisconsin-Madison. (2015, Mar. 23). Ethics. Delta Roundtable: Using Data Analytics to Support Students’ Success. Retrieved from https://delta.wisc.edu/Data_Analytics_Resources.pdf

Harfield, T. (2014, Dec. 12). A work in progress: Because you can’t steer a parked car [Blog post]. Retrieved from http://timothyharfield.com/blog/2014/12/12/twila_20141212/