New Vistas on Responsible Learning Analytics
A Data Feminist Perspective
Keywords:data feminism, critical theory, ethical guidelines, learning analytics, responsibility, research paper
The focus of ethics in learning analytics (LA) frameworks and guidelines is predominantly on procedural elements of data management and accountability. Another, less represented focus is on the duty to act and LA as a moral practice. Data feminism as a critical theoretical approach to data science practices may offer LA research and practitioners a valuable lens through which to consider LA as a moral practice. This paper examines what data feminism can offer the LA community. It identifies critical questions for further developing and enabling a responsible stance in LA research and practice taking one particular case — algorithmic decision-making — as a point of departure.
Adejo, O., & Connolly, T. (2017). An integrated system framework for predicting students’ academic performance in higher educational institutions. International Journal of Computer Science and Information Technology, 9(3), 149–157. https://doi.org/10.14569/IJACSA.2017.080404
Al-Mahmood, R. (2020). The politics of learning analytics. In D. Ifenthaler & D. Gibson (Eds.), Adoption of data analytics in higher education learning and teaching (pp. 21–38). Springer. https://doi.org/10.1007/978-3-030-47392-1_2
Ames, M. G. (2019). The charisma machine: The life, death, and legacy of one laptop per child. The MIT Press.
Axelsson, P., & Mienna, C. S. (2020). The challenge of indigenous data in Sweden. In M. Walter, T. Kukutai, S. R. Carroll, & D. Rodriguez-Lonerbear (Eds.), Indigenous data sovereignty and policy (pp. 99–111). Routledge.
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
Bardzell, S. (2010). Feminist HCI: Taking stock and outlining an agenda for design. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI ʼ10), 10–15 April 2010, Atlanta, GA, USA (pp. 1301–1310). ACM Press. https://doi.org/10.1145/1753326.1753521
Bardzell, S., & Bardzell, J. (2011). Towards a feminist HCI methodology: Social science, feminism, and HCI. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI ʼ11), 7–12 May 2011, Vancouver, BC, Canada (pp. 675–684). ACM Press. https://doi.org/10.1145/1978942.1979041
Beauchamp, T. L., & Childress, J. F. (2001). Principles of biomedical ethics (5th ed.). Oxford University Press.
Benjamin, R. (2019). Race after technology: Abolitionist tools for the new Jim code. Polity Press.
Bowker, G. C., & Star, S. L. (2000). Sorting things out: Classification and its consequences. The MIT Press.
Brown, L. X. (2020). How automated test proctoring software discriminates against disabled students. Center for Democracy and Technology. https://cdt.org/insights/how-automated-test-proctoring-software-discriminates-against-disabled-students/
Buckingham Shum, S. (2012). Policy brief: Learning analytics. UNESCO Institute for Information Technologies in Education. https://iite.unesco.org/files/policy_briefs/pdf/en/learning_analytics.pdf
Buckingham Shum, S. (2018). Keynote: Transitioning education’s knowledge infrastructure: Shaping design or shouting from the touchline? 13th International Conference of the Learning Sciences (ICLS ’18), 23–27 June 2018, London, UK. https://simon.buckinghamshum.net/2018/06/icls2018-keynote/
Buckingham Shum, S. J., & Luckin, R. (2019). Learning analytics and AI: Politics, pedagogy and practices. British Journal of Educational Technology, 50(6), 2785–2793. https://doi.org/10.1111/bjet.12880
Capurro, R. (2008). Information ethics for and from Africa. Journal of the American Society for Information Science and Technology, 59(7), 1162–1170. https://doi.org/10.1002/asi.20850
Center for Democracy & Technology. (2019, November 12). Algorithmic systems in education: Incorporating equity and fairness when using student data. https://cdt.org/insights/algorithmic-systems-in-education-incorporating-equity-and-fairness-when-using-student-data/
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
Cerratto Pargman, T., McGrath, C., Viberg, O., Kitto, K., Knight, S., & Ferguson, R. (2021). Responsible learning analytics: Creating just, ethical, and caring. Proceedings of the 11th International Conference on Learning Analytics and Knowledge (LAK ’21), 12–16 April 2021, Irvine, CA, USA. ACM Press. https://oro.open.ac.uk/75925/
Chen, B., & Zhu, H. (2019). Towards value-sensitive learning analytics design. 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
Colonna, L. (2022). Implementing data protection by design in the ed tech context: What is the role of technology providers? Journal of Law, Technology & the Internet, 13(1), 84–106. http://dx.doi.org/10.2139/ssrn.4075685
Costanza-Chock, S. (2020). Design justice: Community-led practices to build the worlds we need. The MIT Press.
Crawford, K., & Joler, V. (2018, September 7). Anatomy of an AI system: The Amazon Echo as an anatomical map of human labor, data and planetary resources. AI Now Institute and Share Lab. https://anatomyof.ai/
Deahl, E., & Rhie, C. (2013). Local lotto: Exploring the lottery at the neighborhood level. Center for Urban Pedagogy, Brooklyn College, MIT Civic Data Design Lab. https://www.crowdsourcedcity.com/pdf/2013/local_lotto.pdf
Dietvorst, B. J., Simmons, J. P., & Massey, C. (2015). Algorithm aversion: People erroneously avoid algorithms after seeing them err. Journal of Experimental Psychology: General, 144(1), 114–126. https://doi.org/10.1037/xge0000033
D’Ignazio, C., & Klein, L. F. (2020). Data feminism. The MIT Press.
Dourish, P. (2016). Algorithms and their others: Algorithmic culture in context. Big Data & Society, 3(2), https://doi.org/10.1177/2053951716665128
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
Dye, M., Kumar, N., Schlesinger, A., Wong-Villacres, M., Ames, M. G., Veeraraghavan, R., O’Neill, J., Pal, J., & Gray, M. L. (2018). Solidarity across borders: Navigating intersections towards equity and inclusion. Companion of the 2018 ACM Conference on Computer-Supported Cooperative Work & Social Computing (CSCW ’18), 3–7 November 2018, Jersey City, NJ, USA (pp. 487–494). ACM Press. https://morganya.org/research/cscw_2018_solidarity.pdf
Engelfriet, A., Manderveld, J., & Jeunink, E. (2015). Learning analytics under the (Dutch) personal data protection act. SURFnet. https://www.surf.nl/binaries/content/assets/surf/nl/kennisbank/2015/surf_learning-analytics-onder-de-wet-wpb.pdf
European Commission. (2022). Final report of the Commission expert group on artificial intelligence and data in education and training: Executive summary. Directorate-General for Education, Youth, Sport and Culture. Publications Office of the European Union. https://data.europa.eu/doi/10.2766/65087
Ess, C. (2020). Digital media ethics (3rd ed.). Wiley.
Ferguson, R. (2012). Learning analytics: Drivers, developments and challenges. International Journal of Technology Enhanced Learning, 4(5–6), 304–317. https://doi.org/10.1504/IJTEL.2012.051816
Ferguson, R. (2019). Ethical challenges for learning analytics. Journal of Learning Analytics, 6(3), 25–30. https://doi.org/10.18608/jla.2019.63.5
Ferguson, R., & Clow, D. (2017). Where is the evidence? A call to action for learning analytics. Proceedings of the 7th International Conference on Learning Analytics and Knowledge (LAK ’17), 13–17 March 2017, Vancouver, BC, Canada (pp. 56–65). ACM Press. https://doi.org/10.1145/3027385.3027396
Figueras, C., Verhagen, H., & Cerratto Pargman, T. (2022). Exploring tensions in responsible AI in practice: An interview study on AI practices in and for Swedish public organizations. Scandinavian Journal of Information Systems, 34(2). https://aisel.aisnet.org/sjis/vol34/iss2/6
Ford, H., & Wajcman, J. (2017). ‘Anyone can edit’, not everyone does: Wikipedia’s infrastructure and the gender gap. Social Studies of Science, 47(4), 511–527. https://doi.org/10.1177/0306312717692172
Gebru, T., Morgenstern, J., Vecchione, B., Wortman Vaughan, J., Wallach, H., Daumé III, H., & Crawford, K. (2021). Datasheets for datasets. Communications of the ACM, 64(12), 86–92. https://doi.org/10.1145/3458723
Gitelman, L., & Jackson, V. (2013). Introduction: Raw data is an oxymoron. In L. Gitelman (Ed.), “Raw data” is an oxymoron (pp. 1–14). The MIT Press.
Green, B. (2022). Escaping the impossibility of fairness: From formal to substantive algorithmic fairness. Philosophy & Technology, 35, 90. https://doi.org/10.1007/s13347-022-00584-6
Greene, D., Hoffmann, A. L., & Stark, L. (2019). Better, nicer, clearer, fairer: A critical assessment of the movement for ethical artificial intelligence and machine learning. Proceedings of the 52nd Hawaii International Conference on System Sciences (HICSS-52), 8–11 January 2019, Grand Wailea, Maui, USA (pp. 2122–2131). IEEE Computer Society. https://hdl.handle.net/10125/59651
Haraway, D. (1988). Situated knowledges: The science question in feminism and the privilege of partial perspective. Feminist Studies, 14(3), 575–599. https://doi.org/10.2307/3178066
Hill Collins, P. (1990). Black feminist thought: Knowledge, consciousness, and the politics of empowerment. Routledge.
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. 1–2). Society for Learning Analytics Research (SoLAR). https://arxiv.org/pdf/2104.12920.pdf
Holstein, K., & Doroudi, S. (2022). Equity and artificial intelligence in education. In W. Holmes & K. Poryaska-Pomsta (Eds.), The ethics of artificial intelligence in education (pp. 151–173). Routledge. https://doi.org/10.4324/9780429329067
Howard, S. K., Swist, T., Gašević, D., Bartimote, K., Knight, S., Gulson, K., Apps, T., Peloche, J., Hutchinson, N., & Selwyn, N. (2022). Educational data journeys: Where are we going, what are we taking and making for AI? Computers and Education: Artificial Intelligence, 3, 100073. https://doi.org/10.1016/j.caeai.2022.100073
Howell, J. A., Roberts, L. D., Seaman, K., & Gibson, D. C. (2018). Are we on our way to becoming a “helicopter university”? Academics’ views on learning analytics. Technology, Knowledge and Learning, 23(1), 1–20. https://doi.org/10.1007/s10758-017-9329-9
Hsu, S., Li, T. W., Zhang, Z., Fowler, M., Zilles, C., & Karahalios, K. (2021). Attitudes surrounding an imperfect AI autograder. Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (CHI ’21), 8–13 May 2021, Yokohama, Japan (pp. 1–15). ACM Press. https://doi.org/10.1145/3411764.3445424
Jones, K. M. (2019a). Advising the whole student: eAdvising analytics and the contextual suppression of advisor values. Education and Information Technologies, 24(1), 437–458. https://doi.org/10.1007/s10639-018-9781-8
Jones, K. M. (2019b). Just because you can doesn’t mean you should: Practitioner perceptions of learning analytics ethics. Portal, 19(3), 407–428. https://doi.org/10.1353/pla.2019.0025
kanarinka. (2016, March 22). Missing women, blank maps, and data voids: What gets counted counts. Civic Media. https://civic.mit.edu/index.html%3Fp=1153.html
Khan, B. H., Corbeil, J. R., & Corbeil, M. E. (Eds.). (2018). Responsible analytics and data mining in education: Global perspectives on quality, support, and decision making. Routledge.
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
Klein, C., Lester, J., Rangwala, H., & Johri, A. (2019). Technological barriers and incentives to learning analytics adoption in higher education: Insights from users. Journal of Computing in Higher Education, 31(3), 604–625. https://doi.org/10.1007/s12528-019-09210-5
Knight, S., Matuk, C., & DesPortes, K. (2022). Learning at the intersection of data literacy and social justice. Educational Technology & Society, 25(4), 70–79. https://www.j-ets.net/collection/published-issues/25_4#h.lfle3lw90b07
Krause, H. (2019, January 21). An introduction to the data biography. We All Count. https://weallcount.com/2019/01/21/an-introduction-to-the-data-biography/
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
Lawson, C., Beer, C., Rossi, D., Moore, T., & Fleming, J. (2016). Identification of ‘at risk’ students using learning analytics: The ethical dilemmas of intervention strategies in a higher education institution. Educational Technology Research and Development, 64, 957–968. https://doi.org/10.1007/s11423-016-9459-0
McMillan Cottom, T. (2016). Black cyberfeminism: Intersectionality, institutions and digital sociology. In J. Daniels, K. Gregory, & T. McMillan Cottom (Eds.), Digital sociologies (pp. 211-232). Policy Press. https://ssrn.com/abstract=2747621
Meng, A., DiSalvo, C., & Zegura, E. (2019). Collaborative data work towards a caring democracy. Proceedings of the ACM on Human–Computer Interaction, 3(CSCW), article 42. ACM Press. https://doi.org/10.1145/3359144
Mutimukwe, C., Viberg, O., Oberg, L.-M., & Cerratto-Pargman, T. (2022). Students’ privacy concerns in learning analytics: Model development. British Journal of Educational Technology, 53(4), 932–951. https://doi.org/10.1111/bjet.13234
Noddings, N. (2012). The caring relation in teaching. Oxford Review of Education, 38(6), 771–781. https://doi.org/10.1080/03054985.2012.745047
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
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. (2020). Of ‘black boxes’ and algorithmic decision-making in (higher) education: A commentary. Big Data & Society, 7(1). https://doi.org/10.1177/2053951720933994
Prinsloo, P., & Slade, S. (2017a). Big data, higher education and learning analytics: Beyond justice, towards an ethics of care. In B. K. Daniel (Ed.), Big data and learning analytics in higher education: Current theory and practice (pp. 109–124). Springer.
Prinsloo, P., & Slade, S. (2017b). Ethics and learning analytics: Charting the (un)charted. In C. Lang, G. Siemens, A. Wise, & D. Gašević (Eds.), Handbook of learning analytics (pp. 49–57). Society for Learning Analytics Research. https://doi.org/10.18608/hla17.004
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 & data mining in education: Global perspectives on quality, support, and decision making. Routledge.
Prinsloo, P., Slade, S., & Khalil, M. (2022). The answer is (not only) technological: Considering student data privacy in learning analytics. British Journal of Educational Technology, 53(4), 876–893. https://doi.org/10.1111/bjet.13216
Quijano, A., & Ennis, M. (2000). Coloniality of power, eurocentrism, and Latin America. Nepentla: Views from South, 1(3), 533–580. https://edisciplinas.usp.br/pluginfile.php/347342/mod_resource/content/1/Quijano%20(2000)%20Colinality%20of%20power.pdf
Reich, J., & Ito, M. (2017). From good intentions to real outcomes: Equity by design in learning technologies. Digital Media + Learning Research Hub. https://clalliance.org/wp-content/uploads/2017/11/GIROreport_1031.pdf
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 https://doi.org/10.3339/fpsyg.2016.01959
Sarmiento, J. P., & Wise, A. F. (2022). Participatory and co-design of learning analytics: An initial review of the literature. Proceedings of the 12th International Conference on Learning Analytics and Knowledge (LAK ’22), 21–25 March 2022, Online (pp. 535–541). ACM Press. https://doi.org/10.1145/3506860.3506910
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
Sclater, M., & Lally, V. (2016). Technology enhanced informal learning, interdisciplinarity, and cultural historical activity theory. Proceedings of the 9th International Conference of Education, Research and Innovation (ICERI2016), 14–16 November 2016, Seville, Spain (pp. 860–867). International Academy of Technology, Education and Development (IATED). https://doi.org/10.21125/iceri.2016.1198
Scott, J., & Nichols, T. P. (2017). Learning analytics as assemblage: Criticality and contingency in online education. Research in Education, 98(1), 83–105. https://doi.org/10.1177/0034523717723391
Siemens, G. (2013). Learning analytics: The emergence of a discipline. American Behavioral Scientist, 57(10), 1380–1400. https://doi.org/10.1177/0002764213498851
Slade, S., & Prinsloo, P. (2013). Learning analytics: Ethical issues and dilemmas. American Behavioral Scientist, 57(10), 1510–1529. https://doi.org/10.1177/0002764213479366
Slade, S., & Tait, A. (2019). Global guidelines: Ethics in learning analytics. International Council for Open and Distance Education. https://www.learntechlib.org/p/208251/
The New York Times. (2019, March 14). College admissions scandal: Your questions answered. https://www.nytimes.com/2019/03/14/us/college-admissions-scandal-questions.html
Tronto, J. C. (1998). An ethic of care. Generations: Journal of the American Society on Aging, 22(3), 15–20. http://www.jstor.org/stable/44875693
Tsai, Y.-S., Scheffel, M., & Gašević, D. (2018). Enabling systematic adoption of learning analytics through a policy framework. Proceedings of the 13th European Conference on Technology Enhanced Learning (EC-TEL 2018), 3–5 September 2018, Leeds, UK (pp. 556–560). Springer. https://doi.org/10.1007/978-3-319-98572-5_44
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
Whittlestone, J., Nyrup, R., Alexandrova, A., Dihal, K., & Cave, S. (2019). Ethical and societal implications of algorithms, data, and artificial intelligence: A roadmap for research. Nuffield Foundation.
Williamson, K., & Kizilcec, R. (2022). A review of learning analytics dashboard research in higher education: Implications for justice, equity, diversity, and inclusion. Proceedings of the 12th International Conference on Learning Analytics and Knowledge (LAK ’22), 21–25 March 2022, Online (pp. 260–270). ACM Press. https://doi.org/10.1145/3506860.3506900
Willis, J. E., Slade, S., & Prinsloo, P. (2016). Ethical oversight of student data in learning analytics: A typology derived from a cross-continental, cross-institutional perspective. Educational Technology Research and Development, 64, 881–901. https://doi.org/10.1007/s11423-016-9463-4
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
Wong, P.-H. (2012). Dao, harmony and personhood: Towards a Confucian ethics of technology. Philosophy & Technology, 25, 67–86. https://doi.org/10.1007/s13347-011-0021-z
Wood, J. T. (2009). Feminist standpoint theory. In S. W. Littlejohn & K. A. Foss (Eds.), Encyclopedia of communication theory (pp. 397–399). SAGE Reference Online. https://edge.sagepub.com/system/files/77593_2.2ref.pdf
How to Cite
Copyright (c) 2023 Journal of Learning Analytics
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons License, Attribution - NonCommercial-NoDerivs 3.0 Unported (CC BY-NC-ND 3.0) license that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).