Fairness, Trust, Transparency, Equity, and Responsibility in Learning Analytics
DOI:
https://doi.org/10.18608/jla.2023.7983Keywords:
trust, transparency, equity, ethics, fairness, responsible learning analytics, editorialAbstract
Learning analytics has the capacity to provide potential benefit to a wide range of stakeholders within a range of educational contexts. It can provide prompt support to students, facilitate effective teaching, highlight aspects of course content that might be adapted, and predict a range of possible outcomes, such as students registering for more appropriate courses, supporting students’ self-efficacy, or redesigning a course’s pedagogical strategy. It will do all these things based on the assumptions and rules that learning analytics developers set out. As such, learning analytics can exacerbate existing inequalities such as unequal access to support or opportunities based on (any combination of) race, gender, culture, age, socioeconomic status, etc., or work to overcome the impact of such inequalities on realizing student potential. In this editorial, we introduce several selected articles that explore the principles of fairness, equity, and responsibility in the context of learning analytics. We discuss existing research and summarize the papers within this special section to outline what is known, and what remains to be explored. This editorial concludes by celebrating the breadth of work set out here, but also by suggesting that there are no simple answers to ensuring fairness, trust, transparency, equity, and responsibility in learning analytics. More needs to be done to ensure that our mutual understanding of responsible learning analytics continues to be embedded in the learning analytics research and design practice.
References
Baker, R. S., & Hawn, A. (2022). Algorithmic bias in education. International Journal of Artificial Intelligence in Education, 32, 1052–1092. https://doi.org/10.1007/s40593-021-00285-9
Berendt, B., Littlejohn, A., & Blakemore, M. (2020). AI in education: Learner choice and fundamental rights. Learning, Media and Technology, 45(3), 312–324. https://doi.org/10.1080/17439884.2020.1786399
Broughan, C., & Prinsloo, P. (2020). (Re)centring students in learning analytics: In conversation with Paulo Freire. Assessment & Evaluation in Higher Education, 45(4), 617–628. https://doi.org/10.1080/02602938.2019.1679716
Buckingham Shum, S. (2019). Critical data studies, abstraction and learning analytics: Editorial to Selwyn’s LAK keynote and invited commentaries. Journal of Learning Analytics, 6(3), 5–10. https://doi.org/10.18608/jla.2019.63.2
Buckingham Shum, S., Ferguson, R., & Martinez-Maldonado, R. (2019). Human-centred learning analytics. Journal of Learning Analytics, 6(2), 1–9. https://doi.org/10.18608/jla.2019.62.1
Cerratto Pargman, T. C., & McGrath, C. (2021). Mapping the terrain of ethics in learning analytics: A systematic literature review of empirical research. Journal of Learning Analytics 8(2), 123–139. https://doi.org/10.18608/jla.2021.1
Cerratto Pargman, T. C., McGrath, C., Viberg, O., & Knight, S. (2023). New vistas on responsible learning analytics: A data feminist perspective. Journal of Learning Analytics, 10(1), 133–148. https://doi.org/10.18608/jla.2023.7781
Chen, B., & Zhu, H. (2019). Towards value-sensitive learning analytics design. In S. Hsiao & J. Cunningham (Eds.), Proceedings of the 9th International Conference on Learning Analytics and Knowledge (LAK ’19), 4–8 March 2019, Tempe, AZ, USA (pp. 343–352). ACM Press. https://doi.org/10.1145/3303772.3303798
Conijn, R., Kahr, P., & Snijders, C. (2023). The effects of explanations in automated essay scoring systems on student trust and motivation. Journal of Learning Analytics, 10(1), 37–53. https://doi.org/10.18608/jla.2023.7801
D’Ignazio, C., & Klein, L. F. (2020). Data feminism. The MIT Press.
Ferguson, R., Ochoa, X., & Kovanović, V. (2022). Learning analytics: Practitioners, take note. Journal of Learning Analytics, 9(3), 1–7. https://doi.org/10.18608/jla.2022.7937
Francis, P., Broughan, C., Foster, C., & Wilson, C. (2020). Thinking critically about learning analytics, student outcomes, and equity of attainment. Assessment & Evaluation in Higher Education, 45(6), 811–821. https://doi.org/10.1080/02602938.2019.1691975
Garcia, P., Sutherland, T., Cifor, M., Chan, A. S., Klein, L., D’Ignazio, C., & Salehi, N. (2020). No: Critical refusal as feminist data practice. Companion of the 2020 ACM Conference on Computer-Supported Cooperative Work & Social Computing (CSCW ’20) 17–21 October 2020, Virtual (pp. 199–202). ACM Press. https://doi.org/10.1145/3406865.3419014
Gedrimiene, E., Celik, I., Mäkitalo, K., & Muukkonen, H. (2023). Transparency and trustworthiness in user intentions to follow career recommendations from a learning analytics tool. Journal of Learning Analytics, 10(1), 54–70. https://doi.org/10.18608/jla.2023.7791
Grimm, A., Steegh, A., Colakoglu, J., Kubsch, M., & Neumann, K. (2023a). Positioning responsible learning analytics in the context of STEM identities of under-served students. Frontiers in Education, 7. https://doi.org/10.3389/feduc.2022.1082748
Grimm, A., Steegh, A., Kubsch, M., & Neumann, K. (2023b). Learning analytics in physics education: Equity-focused decision-making lacks guidance! Journal of Learning Analytics, 10(1), 71–84. https://doi.org/10.18608/jla.2023.7793
Heiser, R. E., Dello Stritto, M. E., Brown, A. S., & Croft., B. (2023). Amplifying student and administrator perspectives on equity and bias in learning analytics: Alone together in higher education. Journal of Learning Analytics, 10(1), 8–23. https://doi.org/10.18608/jla.2023.7775
Hernández-de-Menéndez, M., Morales-Menendez, R., Escobar, C. A., & Ramírez Mendoza, R. A. (2022). Learning analytics: State of the art. International Journal on Interactive Design and Manufacturing, 16(3), 1209–1230. https://doi.org/10.1007/s12008-022-00930-0
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
Jones, K. M., Asher, A., Goben, A., Perry, M. R., Salo, D., Briney, K. A., & Robertshaw, M. B. (2020). “We’re being tracked at all times”: Student perspectives of their privacy in relation to learning analytics in higher education. Journal of the Association for Information Science and Technology, 71(9), 1044–1059. https://doi.org/10.1002/asi.24358
Khalil, M., & Ebner, M. (2016). De-identification in learning analytics. Journal of Learning Analytics, 3(1), 129–138. https://doi.org/10.18608/jla.2016.31.8
Khalil, M., Prinsloo, P., & Slade, S. (2018). User consent in MOOCs: Micro, meso, and macro perspectives. International Review of Research in Open and Distributed Learning, 19(5). https://doi.org/10.19173/irrodl.v19i5.3908
Kitchin, R. (2014). Big data, new epistemologies and paradigm shifts. Big Data & Society, 1(1). https://doi.org/10.1177/2053951714528481
Kitchin, R., & Lauriault, T. P. (2014). Towards critical data studies: Charting and unpacking data assemblages and their work. Pre-print version of chapter published in J. Eckert, A. Shears, & J. Thatcher (Eds.), Thinking big data in geography: New regimes, new research (ch. 1). University of Nebraska Press, 2018. https://ssrn.com/abstract=2474112
Ladjal, D., Joksimović, S., Rakotoarivelo, T., & Zhan, C. (2022). Technological frameworks on ethical and trustworthy learning analytics. British Journal of Educational Technology, 53(4), 733–736. https://doi.org/10.1111/bjet.13236
Lee, H. L., & Gargroetzi, E. C. (2023). “It’s like a double-edged sword”: Mentor perspectives on ethics and responsibility in a learning analytics–supported virtual mentoring program. Journal of Learning Analytics, 10(1), 85–100. https://doi.org/10.18608/jla.2023.7787
Li, W., Sun, K., Schaub, F., & Brooks, C. (2022). Disparities in students’ propensity to consent to learning analytics. International Journal of Artificial Intelligence in Education, 32(3), 564–608. https://doi.org/10.1007/s40593-021-00254-2
Meaney, M. J., & Fikes, T. (2023). The promise of MOOCs revisited? Demographics of learners preparing for university. Journal of Learning Analytics, 10(1), 113–132. https://doi.org/10.18608/jla.2023.7807
Ochoa, X., Knight, S., & Wise, A. F. (2020). Learning analytics impact: Critical conversations on relevance and social responsibility. Journal of Learning Analytics, 7(3), 1–5. https://doi.org/10.18608/jla.2020.73.1
Patterson, C. R., York, E., Maxham, D., Molina, R., & Mabrey III, P. (2023). Applying a responsible innovation framework in developing an equitable early alert system: A case study. Journal of Learning Analytics, 10(1), 24–36. https://doi.org/10.18608/jla.2023.7795
Prinsloo, P. (2019). A social cartography of analytics in education as performative politics. British Journal of Educational Technology, 50(6), 2810–2823. https://doi.org/10.1111/bjet.12872
Prinsloo, P., Khalil, M., & Slade, S. (2023). Learning analytics as data ecology: A tentative proposal. Journal of Computing in Higher Education. https://doi.org/10.1007/s12528-023-09355-4
Prinsloo, P., & Slade, S. (2017). An elephant in the learning analytics room: The obligation to act. In A. Wise, P. Winne, & G. Lynch (Eds.), Proceedings of the 7th International Conference on Learning Analytics and Knowledge (LAK ’17), 13–17 March 2017, Vancouver, BC, Canada (pp. 46–55). ACM Press. https://doi.org/10.1145/3027385.3027406
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: Global perspectives on quality, support, and decision-making (ch. 4). Routledge.
Prinsloo, P., Slade, S., & Khalil, M. (2019). Student data privacy in MOOCs: A sentiment analysis. Distance Education, 40(3), 395–413. https://doi.org/10.1080/01587919.2019.1632171
Riazy, S., Simbeck, K., & Schreck, V. (2021). Systematic literature review of fairness in learning analytics and application of insights in a case study. In H. C. Lane, S. Zvacek, & J. Uhomoibhi (Eds.), Computer supported education: 12th international conference, CSEDU 2020, virtual event, May 2–4, 2020, revised selected papers (pp. 430–449). Communications in Computer and Information Science, vol. 1473. Springer. https://doi.org/10.1007/978-3-030-86439-2_22
Shibani, A., Knight, S., & Buckingham Shum, S. (2022). Questioning learning analytics? Cultivating critical engagement as student automated feedback literacy. Proceedings of the 12th International Conference on Learning Analytics and Knowledge (LAK ’22), 21–25 March 2022, Online (pp. 326–335). ACM Press. https://doi.org/10.1145/3506860.3506912
Skinner-Thompson, S. (2021). Privacy at the margins. Cambridge University Press. https://doi.org/10.1017/9781316850350
Slade, S., & Prinsloo, P. (2013). Learning analytics: Ethical issues and dilemmas. American Behavioral Scientist, 57(10), 1510–1529. https://doi.org/10.1177/0002764213479366
Swauger, S., & Kalir, R. (2023). Learning analytics and the abolitionist imagination. Journal of Learning Analytics, 10(1), 101–112. https://doi.org/10.18608/jla.2023.7813
Uttamchandani, S., & Quick, J. (2022). An introduction to fairness, absence of bias, and equity in learning analytics. In C. Lang, G. Siemens, A. F. Wise, D. Gašević, & A. Merceron (Eds.), The handbook of learning analytics, 2nd ed. (pp. 205–212). Society for Learning Analytics Research (SoLAR). https://solaresearch.org/wp-content/uploads/hla22/HLA22_Chapter_20_Uttamchandani.pdf
Vasquez Verdugo, J., Gitiaux, X., Ortega, C., & Rangwala, H. (2022). FairED: A systematic fairness analysis approach applied in a higher educational context. Proceedings of the 12th International Conference on Learning Analytics and Knowledge (LAK ’22), 21–25 March 2022, Online (pp. 271–281). ACM Press. https://doi.org/10.1145/3506860.3506902
Wise, A. F., Sarmiento, J. P., & Boothe Jr., M. (2021). Subversive learning analytics. Proceedings of the 11th International Conference on Learning Analytics and Knowledge (LAK ’21), 12–16 April 2021, Irvine, CA, USA (pp. 639–645). ACM Press. https://doi.org/10.1145/3448139.3448210
Zhu, M., Sari, A. R., & Lee, M. M. (2022). Trends and issues in MOOC learning analytics empirical research: A systematic literature review (2011–2021). Education and Information Technologies, 27(7), 10135–10160. https://doi.org/10.1007/s10639-022-11031-6
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