“It’s like a double-edged sword”: Mentor Perspectives on Ethics and Responsibility in a Learning Analytics–Supported Virtual Mentoring Program
Keywords:sociocritical framework, online mentorship, ethical reasoning, equitable learning environment, responsible learning analytics, research paper
Data-driven learning analytics (LA) exploits artificial intelligence, data-mining, and emerging technologies, rapidly expanding the collection and uses of learner data. Considerations of potential harm and ethical implications have not kept pace, raising concerns about ethical and privacy issues (Holstein & Doroudi, 2019; Prinsloo & Slade, 2018). This empirical study contributes to a growing critical conversation on fairness, equity, and responsibility of LA lending mentor voices in the context of an online mentorship program through which undergraduate students mentored secondary school students. Specifically, this study responds to a phenomenon shared by four mentors who recounted hiding from mentees that they had seen their LA data. Interviews reveal the convergent and divergent ideas of mentors regarding LA in terms of 1) affordances and constraints, 2) scope and boundaries, 3) ethical tensions and dilemmas, 4) paradoxical demands, and 5) what constitutes fairness, equity, and responsibility. The analysis integrates mentor voices with Slade and Prinsloo’s (2013) principles for an ethical framework for LA, Hacking’s (1982, 1986) dynamic nominalism, and Levinas’s (1989) ethics of responsibility. Design recommendations derived from mentor insights are extended in a discussion of ethical relationality, troubling learners as data-subjects, and considering the possibilities of the agency, transparency, and choice in LA system design.
Apple, M. W. (2004). Ideology and curriculum (3rd ed.). Routledge Falmer. https://doi.org/10.4324/9780203487563
Arnold, K. E., & Sclater, N. (2017). Student perceptions of their privacy in leaning analytics applications. Proceedings of the 7th International Conference on Learning Analytics and Knowledge (LAK ’17), 13–17 March 2017, Vancouver, BC, Canada (pp. 66–69). ACM Press. https://doi.org/10.1145/3027385.3027392
Buchanan, R. (2019). Digital ethical dilemmas in teaching. In M. A. Peters (Ed.), Encyclopedia of teacher education (pp. 1–6). Springer. https://doi.org/10.1007/978-981-13-1179-6_150-1
Cerratto Pargman, T., & McGrath, C. (2021). Mapping the ethics of learning analytics in higher education: A systematic literature review of empirical research. Journal of Learning Analytics, 8(2), 123–139. https://doi.org/10.18608/jla.2021.1
Cialdini, R. B., & Goldstein, N. J. (2004). Social influence: Compliance and conformity. Annual Review of Psychology, 55, 591–621. https://doi.org/10.1146/annurev.psych.55.090902.142015
Danaher, J. (2022). Techno-optimism: An analysis, an evaluation and a modest defence. Philosophy & Technology, 35, 54. https://doi.org/10.1007/s13347-022-00550-2
D’Ignazio, C., & Klein, L. F. (2020). Data feminism. MIT press.
Drachsler, H., & Greller, W. (2016). Privacy and analytics: It’s a DELICATE issue a checklist for trusted learning analytics. Proceedings of the 6th International Conference on Learning Analytics and Knowledge (LAK ʼ16), 25–29 April 2016, Edinburgh, UK (pp. 89–98). ACM Press. https://doi.org/10.1145/2883851.2883893
Ferguson, R., Hoel, T., Scheffel, M., & Drachsler, H. (2016). Guest editorial: Ethics and privacy in learning analytics. Journal of Learning Analytics, 3(1), 5–15. https://doi.org/10.18608/jla.2016.31.2
Hacking, I. (1982). Biopower and the avalanche of printed numbers. Humanities in Society, 5, 279–295.
Hacking, I. (1986). Making up people. In T. C. Heller, M. Sosna, D. E. Wellbery, A. I. Davidson, A. Swidler, & I. Watt (Eds.), Reconstructing individualism: Autonomy, individuality, and the self in Western thought (pp. 222–236). Stanford University Press.
Holstein, K., & Doroudi, S. (2019). Fairness and equity in learning analytics systems (FairLAK). Companion Proceedings of the 9th International Conference on Learning Analytics and Knowledge (LAK ’19), 23–27 March 2020, Frankfurt, Germany (pp. 500–503). Society for Learning Analytics Research (SoLAR). https://www.solaresearch.org/wp-content/uploads/2019/08/LAK19_Companion_Proceedings.pdf
Ifenthaler, D., & Schumacher, C. (2016). Student perceptions of privacy principles for learning analytics. Educational Technology Research and Development, 64, 923–938. https://doi.org/10.1007/s11423-016-9477-y
Johnson, J. A. (2018). Toward information justice: Technology, politics, and policy for data in higher education administration. Springer.
Kitto, K., & Knight, S. (2019). Practical ethics for building learning analytics. British Journal of Educational Technology, 50(6), 2855–2870. https://doi.org/10.1111/bjet.12868
Kruse, A., & Pongsajapan, R. (2012). Student-centered learning analytics. CNDLS Thought Papers, 1(9), 98–112.
Larkin, M., Watts, S., & Clifton, E. (2006). Giving voice and making sense in interpretative phenomenological analysis. Qualitative Research in Psychology, 3(2), 102–120. https://doi.org/10.1191/1478088706qp062oa
Levinas, E. (1961). Totalité et infini: Essai sur l’exteriorite. La Haye.
Levinas, E. (1989). The Levinas reader: Emmanuel Levinas, Seán Hand (Ed.). Blackwell Publishers.
Mackenzie, C., Rogers, W., & Dodds, S. (2014). Vulnerability: New essays in ethics and feminist philosophy. Oxford University Press. https://doi.org/10.1093/acprof:oso/9780199316649.001.0001
McDermott, R. (2001). The acquisition of a child by a learning disability. In J. Collins & D. Cook (Eds.), Understanding learning: Influences and outcomes (pp. 269–305). Paul Chapman Press. https://youcubed2.wpenginepowered.com/wp-content/uploads/2018/10/McDermott-2001.pdf
Noble, S. U. (2018). Algorithms of oppression. New York University Press.
O’Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. Broadway Books.
Pardo, A., & Siemens, G. (2014). Ethical and privacy principles for learning analytics. British Journal of Educational Technology, 45(3), 438–450. https://doi.org/10.1111/bjet.12152
Prinsloo, P., & Slade, S. (2015). Student privacy self-management: Implications for learning analytics. Proceedings of the 5th International Conference on Learning Analytics and Knowledge (LAK ʼ15), 16–20 March 2015, Poughkeepsie, NY, USA (pp. 83–92). ACM Press. https://doi.org/10.1145/2723576.2723585
Prinsloo, P., & Slade, S. (2016). Student vulnerability, agency, and learning analytics: An exploration. Journal of Learning Analytics, 3(1), 159–182. https://doi.org/10.18608/jla.2016.31.10
Prinsloo, P., & Slade, S. (2018). Mapping responsible learning analytics: A critical proposal. In B. H. Khan, J. R. Corbeil, & M. E. Corbeil (Eds.), Responsible analytics and data mining in education (pp. 63–79). Routledge.
Ratcliffe, J. (2002). Scenario planning: Strategic interviews and conversations. Foresight, 4(1), 19–30. https://doi.org/10.1108/14636680210425228
Sclater, N. (2016). Developing a code of practice for learning analytics. Journal of Learning Analytics, 3(1), 16–42. https://doi.org/10.18608/jla.2016.31.3
Slade, S., & Prinsloo, P. (2013). Learning analytics: Ethical issues and dilemmas. American Behavioral Scientist, 57(10), 1510–1529. https://doi.org/10.1177/0002764213479366
Smith, J. A., Flowers, P., Larkin, M. (2021). Interpretative phenomenological analysis: Theory, method and research. SAGE Publications.
Sung, J., Lee, H. H., Lee, U., Lee, B., Jang, D., Park, Y., & Oh, H. (2022). Learning analytics for learning design in online mentorship program: Understanding mentors facing learning analytics. [Manuscript submitted for publication].
Swenson, J. (2014). Establishing an ethical literacy for learning analytics. Proceedings of the 4th International Conference on Learning Analytics and Knowledge (LAK ʼ14), 24–28 March 2014, Indianapolis, IN, USA (pp. 246–250). ACM Press. https://doi.org/10.1145/2567574.2567613
Tsai, Y.-S., Poquet, O., Gašević, D., Dawson, S., & Pardo, A. (2019). Complexity leadership in learning analytics: Drivers, challenges and opportunities. British Journal of Educational Technology, 50(6), 2839–2854. https://doi.org/10.1111/bjet.12846
Tsai, Y.-S., Whitelock-Wainwright, A., & Gašević, D. (2021). More than figures on your laptop: (Dis)trustful implementation of learning analytics. Journal of Learning Analytics, 8(3), 81–100. https://doi.org/10.18608/jla.2021.7379
West, D., Huijser, H., & Heath, D. (2016). Putting an ethical lens on learning analytics. Educational Technology Research and Development, 64, 903–922. https://doi.org/10.1007/s11423-016-9464-3
Willis, J. E. (2014). Learning analytics and ethics: A framework beyond utilitarianism. Educause Review.
Wise, A. F., & Jung, Y. (2019). Teaching with analytics: Towards a situated model of instructional decision-making. Journal of Learning Asnalytics, 6(2), 53–69. https://doi.org/10.18608/jla.2019.62.4
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