The ethics of using learning analytics to categorize students on risk
There are good reasons for higher education institutions to use learning analytics to risk-screen students. Institutions can use learning analytics to better predict which students are at greater risk of dropping out or failing, and use the statistics to treat ‘risky’ students differently. This paper analyses this practice using normative theories of discrimination. The analysis suggests the principal ethical concern with the differing treatment is the failure to recognize students as individuals, which may impact on students as agents. This concern is cross-examined drawing on a philosophical argument that suggests there is little or no distinctive difference between assessing individuals on group risk statistics and using more ‘individualized’ evidence. This paper applies this argument to the use of learning analytics to risk-screen students in higher education. The paper offers reasons to conclude that judgment based on group risk statistics does involve a distinctive failure in terms of assessing persons as individuals. However, instructional design offers ways to mitigate this ethical concern with respect to learning analytics. These include designing features into courses that promote greater use of effort-based factors and dynamic rather than static risk factors, and greater use of sets of statistics specific to individuals.
KeywordsAnalytics Ethics Education Screening Discrimination Retention Risk
Compliance with ethical standards
Conflict of interest
The author declares that she has no conflict of interest.
- Arneson, R. J. (2006). What is wrongful discrimination? San Diego Law Review, 43, 775–808.Google Scholar
- Bichsel, J. (2012). Analytics in higher education: benefits, barriers, progress, and recommendations (Research Report). Louisville, CO: EDUCAUSE Center for Applied Research. Retrieved August 2012 from http://www.educause.edu/ecar.
- Campbell, J., deBlois P., & Oblinger, D. (2007). Academic analytics: A new tool for a new era. EDUCAUSE Review, 42(4), 41–57.Google Scholar
- Crawford, K., & Schultz, J. (2013). Big data and due process: Toward a framework to redress predictive privacy harms. NYU School of Law. Public Law Research Paper No. 13-64, 93-128.Google Scholar
- Edmonds, D. (2006). Caste wars: A philosophy of discrimination. London & New York: Routledge.Google Scholar
- Eidelson, B. (2013). Treating people as individuals. In D. Hellman & S. Moreau (Eds.), The philosophical foundations of discrimination law (pp. 354–395). Oxford: Oxford University Press.Google Scholar
- Hellman, D. (2008). When is discrimination wrong? Cambridge: Harvard University Press.Google Scholar
- Information Commissioner’s Office (n.d.) Data protection principles. Retrieved November 2015 from https://ico.org.uk/for-organisations/guide-to-data-protection/data-protection-principles/.
- International Working Group on Data Protection in Telecommunications (IWGDPT). (2014). Working paper on big data and privacy: Privacy principles under pressure in the age of big data analytics. Retrived June 26, 2015 from http://dzlp.mk/sites/default/files/u972/WP_Big_Data_final_clean_675.48.12%20%281%29.pdf.
- Jia, P. (2014). Using predictive risk modeling to identify students at high risk of paper non-completion and program non-retention at university. MBus thesis. Auckland University of Technology.Google Scholar
- Johnson, J. A. (2014). The ethics of big data in higher education. International Review of Information Ethics, 21, 3–10.Google Scholar
- Kay, D., Korn, N., & Oppenheim, C. (2012). Legal, risk and ethical aspects of analytics in higher education. CETIS Analytics Series, 1(6), 1–30.Google Scholar
- Kovacic, Z. (2010) Early prediction of student success: Mining students enrolment data. Proceedings of informing science & IT education conference (InSITE) 2010.Google Scholar
- Lippert-Rasmussen, K. (2006). Private discrimination: A prioritarian, desert-accommodating account. San Diego Law Review, 43, 817–856.Google Scholar
- Lokken, F., & Mullins, C. (2015). ITC 2014 distance education survey results. Retrieved November 2015 from http://www.itcnetwork.org/membership/itc-distance-education-survey-results.html.
- Long, P., & Siemens, G. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE Review, 46(5), 31–40.Google Scholar
- Mayer-Schönberger, V., & Cukier, K. (2014). Learning with big data. Boston/New York: Houghton Mifflin Harcourt.Google Scholar
- OAAI, Marist College (2012). Open academic analytics initiative. Retrieved November 2015 from https://confluence.sakaiproject.org/pages/viewpage.action?pageId=75671025.
- Oblinger, D. (2012). Let’s Talk … Analytics. EDUCAUSE Review, 47(4), 10–13.Google Scholar
- Open University Oct. (2014). Ethics use of student data for learning analytics policy FAQs. Retrieved October 2015 from http://www.open.ac.uk/students/charter/sites/www.open.ac.uk.students.charter/files/files/ecms/web-content/ethical-student-data-faq.pdf.
- Open University Sep. (2014). Policy on ethical use of student data for learning analytics. Retrieved October 2015 from http://www.open.ac.uk/students/charter/sites/www.open.ac.uk.students.charter/files/files/ecms/web-content/ethical-use-of-student-data-policy.pdf.
- Polonetsky, J., & Tene, O. (2014). The ethics of student privacy: Building trust for ed tech. International Review of Information Ethics, 21, 25–34.Google Scholar
- Prinsloo, P., & Slade, S. (2014). Educational triage in open distance learning: Walking a moral tightrope. The International Review of Research in Open and Distance Learning, 15(4), 306–331.Google Scholar
- Richards, N., & King, J. (2014). Big data ethics. Wake Forest Law Review, 49, 393–432.Google Scholar
- Schauer, F. (2003). Profiles, probabilities and stereotypes. Cambridge: Harvard University Press.Google Scholar
- Sclater, N. (2014a). Code of practice for learning analytics: A literature review of the ethical and legal issues. London: Jisc.Google Scholar
- Sclater, N. (2014b). Learning analytics: The current state of play in UK higher and further education. JISC. Retrieved October 2015 from http://repository.jisc.ac.uk/5657/1/Learning_analytics_report.pdf.
- Sclater, N. & Bailey, P. (2015). Code of practice for learning analytics. JISC, London. Retrieved October 2015 from https://www.jisc.ac.uk/guides/code-of-practice-for-learning-analytics.
- Vuong, A., Nixon, T., & Towle, B. (2011). A method for finding prerequisites in a curriculum. In M. Pechenizkiy, T. Calders, C. Conati, S. Ventura, C. Romero, & J. Stamper (Eds.), Proceedings of the 4th international conference on educational data mining. Eindhoven, The Netherlands, July 6-8, 2011 (pp. 211–216).Google Scholar
- Wagner, E. & Hartman, J. (2013). Welcome to the era of big data and predictive analytics in higher education. SHEEO (State Higher Education Executive Officers Association Higher Education Policy Conference 2013 [presentation]. Retrieved November 2015 from http://www.sheeo.org/sites/default/files/0808-1430-plen.pdf.
- Willis, J., E. (2014). Learning analytics and ethics: A framework beyond utilitarianism. EDUCAUSE Review. Retrieved October 2015 from http://er.educause.edu/articles/2014/8/learning-analytics-and-ethics-a-framework-beyond-utilitarianism.
- Willis, J. E., & Pistilli, M. D. (2014). Ethical discourse: Guiding the future of learning analytics. EDUCAUSE Review. Retrieved October 2015 from http://www.educause.edu/ero/article/ethical-discourse-guiding-future-learning-analytics.
- Witt, P. H., Dattilio, F. M., & Bradford, J. M. W. (2011). Sex offender evaluations. In E. Drogin, F.M. Dattilio, R.L. Sadoff, & T. G. Gutheil (Eds.), Handbook of forensic assessment: Psychological and psychiatric perspectives. Wiley Online.Google Scholar
- Woodley, A., & Simpson, O. (2014). Student Dropout: The elephant in the room. In O. Zawacki-Richter & T. Anderson (Eds.), Online distance education: towards a research agenda (pp. 459–483). Edmonton: AU Press.Google Scholar