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: 406
Google Scholar Citations: 38

Scholarly Citations

Viberg, O., Hatakka, M., Balter, O., Mavroudi, A. (2018). The current landscape of learning analytics in higher education. Computers in Human Behavior, 89, 98-110. doi: 10.1016/j.chb.2018.07.027

Zimmer, M. (2018). Addressing conceptual gaps in big data research ethics: An application of contextual integrity. Social Media + Society. Retrieved from http://journals.sagepub.com/doi/10.1177/2056305118768300

Jones, K. M. L. (2018). Advising the whole student: eAdvising analytics and the contextual suppression of advisor values. Education and Information Technologies, 1–22. doi: 10.1007/s10639-018-9781-8

Wachter, S. Ethical and normative challenges of identification in the Internet of Things. Proceedings from the Living in the Internet of Things: Cybersecurity of the IoT 2018. London, UK. doi: 10.1049/cp.2018.0013

Banihashem, S.K., Aliabadi, K., Ardakani, S.P., Delaver, A., &Ahmadabadi, M.N. (2018). Learning analytics: A critical literature review. Interdiscip J Virtual Learn Med Sci, 9(2). doi: 10.5812/ijvlms.63024

Sichau, D. & Fässler, L. (2018). An Open Learning Analytics Systems Ensuring Students’ Privacy. In T. Bastiaens, J. Van Braak, M. Brown, L. Cantoni, M. Castro, R. Christensen, G. Davidson-Shivers, K. DePryck, M. Ebner, M. Fominykh, C. Fulford, S. Hatzipanagos, G. Knezek, K. Kreijns, G. Marks, E. Sointu, E. Korsgaard Sorensen, J. Viteli, J. Voogt, P. Weber, E. Weippl & O. Zawacki-Richter (Eds.), Proceedings of EdMedia: World Conference on Educational Media and Technology (pp. 116-121). Amsterdam, Netherlands: Association for the Advancement of Computing in Education (AACE). Retrieved from https://www.learntechlib.org/primary/p/184188/

Moir, J. (2018). For the record: Learning analytics and student engagement. Proceedings from The 15th Enhancement Conference: Evaluation, Evidence & Enhancement: Inspiring Staff & Students. Glasgow, Scotland. Retrieved from http://www.enhancementthemes.ac.uk/docs/ethemes/evidence-for-enhancement/for-the-record-learning-analytics-and-student-engagement-(paper).pdf

West, D., Tasir, Z., Luzeckj, A., Kew, S. N., Toohey, D., Abdullah, Z.,…Price, R. (2018). Learning analytics experience among academics in Australia and Malaysia: A comparison. Australasian Journal of Educational Technology, 34(3), 122–139. doi: 10.14742/ajet.3836

Ocheja, P., Flanagan, B., & Ogata, H. (2018). Connecting decentralized learning records: A blockchain based learning analytics platform. Proceedings from LAK’18: The 8th International Conference on Learning Analytics and Knowledge (pp. 265–269). New York, NY: ACM. doi: 10.1145/3170358.3170365

Tsai, Y.-S., Moreno-Marcos, P. M., Tammets, K., Kollom, K., Gašević, D. (2018). SHEILA policy framework: Informing institutional strategies and policy processes of learning analytics. Proceedings from LAK ’18: The 8th International Conference on Learning Analytics and Knowledge (pp. 320–329). New York, NY: ACM. doi: 10.1145/3170358.3170367

Mittelstadt, B. (2018). From individual to group privacy in biomedical big data. In G. Cohen, H. Fernandez Lynch, E. Vayena, & U. Gasser (Eds.), Big data, health law, and bioethics. New York, NY: Cambridge University Press.

Jones, K. M. L., & LeClere, E. (2018). Contextual expectations and emerging informational harms: A primer on academic library participation in learning analytics initiatives. In P. Fernandez & K. Tilton (Eds.), Applying library values to emerging technology: Decision-making in the age of open access, maker spaces, and the ever-changing library. Chicago, IL: Association of College and Research Libraries.

Jones, K. M. L., & Salo, D. (2018). Learning analytics and the academic library: Professional ethics commitments at a crossroads. College & Research Libraries, 79(3), 304–323. doi: 10.5860/crl.79.3.304

Marshall, S. J. (2018). Shaping the university of the future: Using technology to catalyse change in university learning and teaching. Singapore: Springer Nature.

Asher, A. D. (2017). Risk, benefits, and user privacy: Evaluating the ethics of library data. In B. Newman & B. Tijerina (Eds.), Protecting patron privacy: A LITA guide (pp. 43–56). Lanham, MD: Rowman & Littlefield. Retrieved from https://scholarworks.iu.edu/dspace/bitstream/handle/2022/22035/Asher–Risk_Benefits_User_Privacy_Final.pdf?sequence=1

Lester, J., Klein, C., Rangwala, H., & Johri, A. (2017). Learning analytics in higher education. ASHE Higher Education Report, 43(5), 9–135. doi: 10.1002/aehe.20121

Crooks, R. (2017). Representationalism at work: Dashboards and data analytics in urban education. Educational Media International, 54(4), 289–303. doi: 10.1080/09523987.2017.1408267

Lotmore, K. W. (2017). The decline of financial privacy and its costs to society. Trusts & Trustees, 23(9), 944–954. doi: 10.1093/tandt/ttx130

Gracia-Moreno, C., Cerisier, J.-F., Devauchelle, B., Gamboa, F., & Pierrot, L. (2017). Collaborative knowledge building through simultaneous private and public workspaces. In É. Lavoué, H. Drachsler, K. Verbert, J. Broisin, & M. Pérez-Sanagustín (Eds.), Data driven approaches in digital education. EC-TEL 2017. Lecture Notes in Computer Science, 10474 (pp. 553–556). Cham, Switzerland: Springer. doi: 10.1007/978-3-319-66610-5_61

Parks, C. (2017). Beyond compliance: Students and FERPA in the age of big data. Journal of Intellectual Freedom & Privacy, 2(2), 23–33. doi: 10.5860/jifp.v2i2.6253

Sigmund, T. (2017). Students and online privacy. In M. Houska, I. Krejci, M. Flegl, M. Fejfarova, H. Urbancova, & J. Husak, Proceedings of the 14th International Conference on Efficiency and Responsibility in Education 2017 (ERIE) (pp. 381–387). Prague, Czech Republic: Czech University of Life Sciences Prague. Retrieved from http://erie.pef.czu.cz/Documents/ERIE2017.pdf

Griffiths, D. (2017). An ethical waiver for learning analytics?. In É. Lavoué, H. Drachsler, K. Verbert, J. Broisin, M. Pérez-Sanagustín (Eds.), Data driven approaches in digital education (pp. 557560). Cham, Switzerland: Springer. doi: 10.1007/978-3-319-66610-5_62

Newman, B., & Tijerina, B. (2017). Protecting student privacy: A LITA guide. Lanham, MD: Rowman & Littlefield.

Roberts, L. D., Howell, J. A., & Seaman, K. (2017). Give me a customizable dashboard: Personalized learning analytics dashboards in higher education. Technology, Knowledge & Learning, 22(3), 317–333. doi: 10.1007/s10758-017-9316-1

Zeide, E. (2017). The structural consequences of big data-driven education. Big Data, 5(2), 164172. doi: 10.1089/big.2016.0061

Ovetz, R. (2017). Click to save and return to course: Online education, adjunctification, and the disciplining of academic labour. Work Organisation, Labour & Globalisation, 11(1), 4870. doi: 10.13169/workorgalaboglob.11.1.0048

Arnold, K. E., & Sclater, N. (2017). Student perceptions of their privacy in learning analytics applications. Proceeding from LAK ’17: The Seventh International Learning Analytics & Knowledge Conference (pp. 6669). New York, NY: ACM. doi: 10.1145/3027385.3027392

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. 89108). 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. 327335). 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), 124. Retrieved from http://ir.stthomas.edu/ustjlpp/vol11/iss1/1/

Daniel, B. K. (2017). Big data and learning analytics in higher education: Current theory and practice. Switzerland: Springer. doi: 10.1007/978-3-319-06520-5

Jones, K. M. L. (2017). Learning analytics and its paternalistic influences. In P. Zaphiris & A. Ioannou (Eds.), Learning and collaboration technologies. Technology in education. LCT 2017. Lecture notes in computer science, 10296 (pp. 281–292). Cham, Switzerland: Springer. doi: 10.1007/978-3-319-58515-4_22

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. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2). doi: 10.1177/2053951716679679

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), 306309. Retrieved from http://www.isko.org/ko434toc.pdf

Prinsloo, P., & Slade, S. (2016). Student vulnerability, agency, and learning analytics: An exploration. Journal of Learning Analytics, 3(1), 159–182. doi: 10.18608/jla.2016.31.10

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), 16. 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), 111. doi: 10.3389/fpsyg.2016.01959

Grey Literature Citations

Raths, D. (2018, May 2). When learning analytics violate student privacy. Campus Technology. Retrieved from https://campustechnology.com/Articles/2018/05/02/When-Learning-Analytics-Violate-Student-Privacy.aspx

Asher, A., Briney, K., Goben, A., Jones, K. M., Perry, M., Robertshaw, M., B., & Salo, D. (2018, February 13). Student learning analytics in libraries — thoughts and resources. Love Data Week. Retrieved from http://lovedataweek.org/2018/02/13/student-learning-analytics-in-libraries-thoughts-and-resources/

Dobrovoda, E. (2017). Big data in education: Using educational data mining and learning analytics to improve policy in the field of education (Unpublished master’s thesis). Talinn University of Technology, Talinn: Estonia.

National Academy of Engineering. (2017). Big data bibliography – Computer and physical sciences. National Academy of Sciences, Online Ethics Center (OEC). Retrieved from http://www.onlineethics.org/Resources/40348/39960/39965.aspx?layoutChange=Normal

Mather, V. (2017, July 11). Student data: Ethical issues and challenges [Linkedin post]. Retrieved from https://www.linkedin.com/pulse/student-data-ethical-issues-challenges-vanessa-mather/

Grey, D., McIntosh, E., & Stylianoudaki, P. (2017). Student dashboards – The case for building communities of practice [PowerPoint slides]. Retrieved from http://apps.nacada.ksu.edu/apps/intlproposals.php/presenters/summary/5/26

Kilińska, D., & Kobbelgaard, F. (2017). The devil is in the detail: A study on quantifying socially shared metacognitive regulation of learning in face-to-face group work (Unpublished masters thesis). Aalborg University, Aalborg: Denmark. Retrieved from http://projekter.aau.dk/projekter/files/259646360/Masters_Thesis_Frederik_Kobbelgaard_Daria_Kilinska.pdf

Adkins Murphy, H. (2017, March 18). Ethics and learning analytics: A short reading list [web log post]. Retrieved from http://edc17.education.ed.ac.uk/hmurphy/tag/evernote/

Holmmer, M., & Fischer, R. (2017, February). Introduction to Information Ethics for Big Data Science: MIT 803 [Syllabus, University of Pretoria]. Retrieved from http://www.cs.up.ac.za/files/MIT803/Download/1385/

Maltby, H., Swain, D., & Karamalla-Gaiballa, S. (2017, January). Learner analytics – Predicting student success: Assessing the strength of the relationship between student engagement and attainment. Planning Support Office, University of Manchester. Retrieved from http://www.staffnet.manchester.ac.uk/media/services/tlso/content/files/cheril/Dan-Swain-CHERIL-report-1-1.8.pdf

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

Hickey, D. (2015, Summer). 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

Jones, K. M. L. (2015). All the data we can get: A contextual study of learning analytics and student privacy. Retrieved from ProQuest Digital Dissertations. (3740991)

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

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