Six Practical Recommendations Enabling Ethical Use of Predictive Learning Analytics in Distance Education
DOI:
https://doi.org/10.18608/jla.2023.7743Keywords:
learning analytics, practical ethics, learning analytics dashboards, case study, research paperAbstract
The progressive move of higher education institutions (HEIs) towards blended and online environments, accelerated by COVID-19, and their access to a greater variety of student data has heightened the need for ethical learning analytics (LA). This need is particularly salient in light of a lack of comprehensive, evidence-based guidelines on ethics that address gaps voiced in LA ethics research. Studies on the topic are predominantly conceptual, representing mainly institutional rather than stakeholder views, with some areas of ethics remaining underexplored. In this paper, we address this need by using a case of four years of interdisciplinary research in developing the award-winning Early Alerts Indicators (EAI) dashboard at a distance learning university. Through a lens focused on ethical considerations and informed by the practical approach to ethics, we conducted a case study review, using 10 relevant publications that report on the development and implementation of the tool. Our six practical recommendations on how to ethically engage with LA can inform an ethical development of LA that not only protects student privacy, but also ensures that LA tools are used in ways that effectively support student learning and development.
References
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
Bilal, A., Wingreen, S., & Sharma, R. (2020). Virtue ethics as a solution to the privacy paradox and trust in emerging technologies. Proceedings of the 3rd International Conference on Information Science and Systems (ICISS 2020), 19–22 March 2020, Cambridge, United Kingdom (pp. 224–228). ACM Press. https://doi.org/10.1145/3388176.3388196
Bodily, R., & Verbert, K. (2017). Review of research on student-facing learning analytics dashboards and educational recommender systems. IEEE Transactions on Learning Technologies, 10(4), 405–418. https://doi.org/10.1109/TLT.2017.2740172
Boyatzis, R. E. (1998). Transforming qualitative information: Thematic analysis and code development. Sage Publications.
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
Brundage, M. (2014). Limitations and risks of machine ethics. Journal of Experimental & Theoretical Artificial Intelligence, 26(3), 355–372. https://doi.org/10.1080/0952813X.2014.895108
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., & 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
Crigger, B.-J., & Wynia, M. (2013). Evaluating ethics quality. AJOB Primary Research, 4(1), 2–6. https://doi.org/10.1080/21507716.2012.756837
Dollinger, M., Liu, D., Arthars, N., & Lodge, J. M. (2019). Working together in learning analytics towards the co-creation of value. Journal of Learning Analytics, 6(2), 10–26. https://doi.org/10.18608/jla.2019.62.2
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
Ellis, P. (2020). Understanding ethics for nursing students (3rd ed.). Sage Publications.
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., 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
Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge, C., Madelin, R., Pagallo, U., Rossi, F., Schafer, B., Valcke, P., & Vayena, E. (2018). AI4People — An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Minds and Machines, 28, 689–707. https://doi.org/10.1007/s11023-018-9482-5
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
Hassani, B. K. (2021). Societal bias reinforcement through machine learning: A credit scoring perspective. AI and Ethics, 1, 239–247. https://doi.org/10.1007/s43681-020-00026-z
Herodotou, C., Boroowa, A., Hlosta, M., & Rienties, B. (2020c). What do distance learning students seek from student analytics? Proceedings of the 14th International Conference of the Learning Sciences (ICLS ’20), 19–23 June 2020, Nashville, TN, USA (pp. 1737–1738). International Society of the Learning Sciences. https://repository.isls.org/bitstream/1/6417/1/1737-1738.pdf
Herodotou, C., Hlosta, M., Boroowa, A., Rienties, B., Zdrahal, Z., & Mangafa, C. (2019b). Empowering online teachers through predictive learning analytics. British Journal of Educational Technology, 50(6), 3064–3079. https://doi.org/10.1111/bjet.12853
Herodotou, C., Maguire, C., McDowell, N., Hlosta, M., & Boroowa, A. (2021). The engagement of university teachers with predictive learning analytics. Computers & Education, 173, 104285. https://doi.org/10.1016/j.compedu.2021.104285
Herodotou, C., Naydenova, G., Boroowa, A., Gilmour, A., & Rienties, B. (2020a). How can predictive learning analytics and motivational interventions increase student retention and enhance administrative support in distance education? Journal of Learning Analytics, 7(2), 72–83. https://doi.org/10.18608/jla.2020.72.4
Herodotou, C., Rienties, B., Boroowa, A., Zdrahal, Z., & Hlosta, M. (2019a). A large-scale implementation of predictive learning analytics in higher education: The teachers’ role and perspective. Educational Technology Research and Development, 67, 1273–1306. https://doi.org/10.1007/s11423-019-09685-0
Herodotou, C., Rienties, B., Hlosta, M., Boroowa, A., Mangafa, C., & Zdrahal, Z. (2020b). The scalable implementation of predictive learning analytics at a distance learning university: Insights from a longitudinal case study. The Internet and Higher Education, 45, 100725. https://doi.org/10.1016/j.iheduc.2020.100725
Hlosta, M., Herodotou, C., Bayer, V., & Fernandez, M. (2021). Impact of predictive learning analytics on course awarding gap of disadvantaged students in STEM. In I. Roll, D. McNamara, S. Sosnovsky, R. Luckin, & V. Dimitrova (Eds.), Proceedings of the 22nd International Conference on Artificial Intelligence in Education (AIED 2021), 14–18 June 2021, Online from Utrecht, The Netherlands (Part I, pp. 190–195). Springer. https://doi.org/10.1007/978-3-030-78270-2_34
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
Lang, C., Macfadyen, L. P., Slade, S., Prinsloo, P., & Sclater, N. (2018). The complexities of developing a personal code of ethics for learning analytics practitioners: Implications for institutions and the field. Proceedings of the 8th International Conference on Learning Analytics and Knowledge (LAK ’18), 5–9 March 2018, Sydney, NSW, Australia (pp. 436–440). ACM Press. https://doi.org/10.1145/3170358.3170396
Lee, K., & Fanguy, M. (2022). Online exam proctoring technologies: Educational innovation or deterioration? British Journal of Educational Technology, 53(3), 475–490. https://doi.org/10.1111/bjet.13182
Ministry of Housing, Communities & Local Government (2019). The English indices of deprivation 2019: Statistical release. https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/835115/IoD2019_Statistical_Release.pdf
Neubert, M. J., & Montañez, G. D. (2020). Virtue as a framework for the design and use of artificial intelligence. Business Horizons, 63(2), 195–204. https://doi.org/10.1016/j.bushor.2019.11.001
Open University. (2014). Ethical use of student data for learning analytics: Student policies and regulations. The Open University UK. https://help.open.ac.uk/documents/policies/ethical-use-of-student-data
Porter, J. (2020, August 17). UK ditches exam results generated by biased algorithm after student protests. The Verge. https://www.theverge.com/2020/8/17/21372045/uk-a-level-results-algorithm-biased-coronavirus-covid-19-pandemic-university-applications
Prinsloo, P., & Slade, S. (2017). An elephant in the learning analytics room: The obligation to act. 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
Rets, I., Herodotou, C., Bayer, V., Hlosta, M., & Rienties, B. (2021). Exploring critical factors of the perceived usefulness of a learning analytics dashboard for distance university students. International Journal of Educational Technology in Higher Education, 18, 46. https://doi.org/10.1186/s41239-021-00284-9
Rienties, B., Herodotou, C., Olney, T., Schencks, M., & Boroowa, A. (2018). Making sense of learning analytics dashboards: A technology acceptance perspective of 95 teachers. The International Review of Research in Open and Distributed Learning, 19(5). https://doi.org/10.19173/irrodl.v19i5.3493
Rienties, B., Olney, T., Nichols, M., & Herodotou, C. (2020). Effective usage of learning analytics: What do practitioners want and where should distance learning institutions be going? Open Learning: The Journal of Open, Distance and e-Learning, 35(2), 178–195. https://doi.org/10.1080/02680513.2019.1690441
Ryan, G. W., & Bernard, H. R. (2003). Techniques to identify themes. Field Methods, 15(1), 85–109. https://doi.org/10.1177/1525822X02239569
Schumacher, C., & Ifenthaler, D. (2018). Features students really expect from learning analytics. Computers in Human Behavior, 78, 397–407. https://doi.org/10.1016/j.chb.2017.06.030
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
Tsai, Y.-S., & Gašević, D. (2017). Learning analytics in higher education — challenges and policies: A review of eight learning analytics policies. Proceedings of the 7th International Conference on Learning Analytics and Knowledge (LAK ’17), 13–17 March 2017, Vancouver, BC, Canada (pp. 233–242). ACM Press. https://doi.org/10.1145/3027385.3027400
Tzimas, D., & Demetriadis, S. (2021). Ethical issues in learning analytics: A review of the field. Educational Technology Research and Development, 69, 1101–1133. https://doi.org/10.1007/s11423-021-09977-4
van den Broek, E., Sergeeva, A., & Huysman, M. (2021). When the machine meets the expert: An ethnography of developing AI for hiring. MIS Quarterly, 45(3b), 1557–1580.
Viberg, O., Hatakka, M., Bälter, O., & Mavroudi, A. (2018). The current landscape of learning analytics in higher education. Computers in Human Behavior, 89, 98–110. https://doi.org/10.1016/j.chb.2018.07.027
Yin, R. K. (2013). Case study research: Design and methods (3rd ed.). Sage Publications.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Journal of Learning Analytics
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
TEST
