Special Section on Fairness, Equity, and Responsibility in Learning Analytics
Mohammad Khalil, University of Bergen, Norway, email@example.com
Paul Prinsloo, University of South Africa, South Africa, firstname.lastname@example.org
Sharon Slade, EarthTrust, United Kingdom, email@example.com
AIMS & SCOPE
Learning Analytics is an interdisciplinary research field, founded in data science and influenced by a number of theoretical perspectives and technological advancements such as Artificial Intelligence (AI), increased data processing capabilities, and greater volumes, variety and velocity of data. It provides opportunities for data-driven decisions which aim to support teachers, learners and optimise pedagogy, and learning environments. Learning analytics increasingly relies on supervised and unsupervised algorithmic decision-making, resulting in concerns about equity, responsibility and fairness (Prinsloo, 2020). While the use of algorithmic decision-making systems raise specific concerns regarding fairness, equity and responsibility, such concerns might also be considered for learning analytics as a whole (Holstein & Doroudi, 2019). A critical interrogation of what might constitute ‘fairness’, ‘equity’, and ‘responsible’ in learning analytics would therefore be timely.
Fairness, equity and responsibility have been part and parcel of discourses in and outside of the learning analytics community since its emergence whether in work surrounding ethical and privacy issues in learning analytics (Slade & Prinsloo, 2013); responsible learning analytics (Prinsloo & Slade, 2018); human-centred (Buckingham Shum, Ferguson & Martinez-Maldonado, 2019) and student-centred learning analytics (Broughan & Prinsloo, 2020); value-sensitive design (Chen & Zhu, 2019); an ethics of care (Prinsloo & Slade, 2017); social justice (Wise, Sarmiento & Boothe Jr, 2021); student rights (Berendt, Littlejohn & Blakemore, 2020); perspectives from data feminism (Garcia et al., 2020); Critical Data Studies (Kitchin & Lauriault, 2014); and other related fields. For example, Francis et al. (2020) and Baker and Hawn (2021) raise fairness and equity questions on whether data-driven learning analytics support systems result in advantaging some students over others, while Pargman and McGrath (2020) suggest that responsible learning analytics should be scoped in terms of legal, ethical, and effective processes for the collection, analysis and reporting of students’ data.
This special issue intends to bring diverse and critical perspectives as well as approaches pertaining to fairness, equity and responsibility of learning analytics to highlight emerging and current conceptual and empirical research, and inform the broader research agenda in learning analytics. Within the broader rationale for this special section, we invite empirical, conceptual/theoretical, speculative papers on a range of topics, some of which are listed below.
TOPICS OF INTEREST
● Learning Analytics, AI and equity, fairness and responsibility: Issues of design, transparency, accountability and governance
● The design, implementation, risks and benefits of learning analytics for minority and disadvantaged groups, such as: students with disability, subcategories of unexplored demographic categories, migrants, etc.
● Theoretical perspectives on fairness, equity and responsibility in learning analytics, e.g., data feminism, intersectionality, Critical Data Stuies, and others
● Case studies of institutionalised approaches to improve/ensure fairness, equity and responsibility in learning analytics
● Stakeholder (e.g., students, faculty, management, ICT, etc.) views on fairness, equity and responsibility
● Scoping and systematic reviews on fairness, equity and responsibility in learning analytics
● Policy and framework analysis and/or development to ensure fairness, equity and responsibility in learning analytics
● The relationships/intersections of ethics in learning analytics with fairness, equity, and responsibility
Initial submission of a 500-1000 word abstract (including title, authors, outline of the proposed article, 3-5 keywords, key references) is highly encouraged. Authors are advised to be explicit with regard to the problem/foci, the research methodology, key findings and value contribution of their prospective paper.
Submit your abstract by email to firstname.lastname@example.org by March 22nd.
Final submissions will take place through JLA’s online submission system at http://learning-analytics.info. Queries may be sent to the special section editors
● (Optional) Abstract submission due: March 22nd, 2022
● Full submissions: DEADLINE EXTENDED TO FRIDAY 1 JULY 2022
● Decisions and comments sent to authors: September, 2022
● Revisions uploaded to the submission system: November, 2022
● Revised/final manuscripts due: December, 2022
Publication of special issue: Spring 2023
Baker, R. S., & Hawn, A. (2021). Algorithmic bias in education. International Journal of Artificial Intelligence in Education, 1-41.
Berendt, B., Littlejohn, A., & Blakemore, M. (2020). AI in education: learner choice and fundamental rights. Learning, Media and Technology, 45(3), 312-324.
Broughan, C., & Prinsloo, P. (2020). (Re) centring students in learning analytics: in conversation with Paulo Freire. Assessment & Evaluation in Higher Education, 45(4).
Buckingham Shum, S., Ferguson, R., & Martinez-Maldonado, R. (2019). Human-centred learning analytics. Journal of Learning Analytics, 6(2), 1-9.
Chen, B., & Zhu, H. (2019, March). Towards value-sensitive learning analytics design. In Proceedings of the 9th international conference on learning analytics & knowledge (pp. 343-352).
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.
Garcia, P., Sutherland, T., Cifor, M., Chan, A. S., Klein, L., D'Ignazio, C., & Salehi, N. (2020, October). No: critical refusal as feminist data practice. In Conference Companion Publication of the 2020 on Computer Supported Cooperative Work and Social Computing (pp.199-202).
Holstein, K., & Doroudi, S. (2019, March). Fairness and equity in learning analytics systems (FairLAK). In Companion proceedings of the ninth international learning analytics & knowledge conference (LAK 2019) (pp. 1-2).
Kitchin, R., & Lauriault, T. (2014). Towards critical data studies: Charting and unpacking data assemblages and their work.
Kruse, A. N. N. A., & Pongsajapan, R. (2012). Student-centered learning analytics. CNDLS Thought Papers, 1(9), 98-112.
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).
Prinsloo, P. (2017). Fleeing from Frankenstein’s monster and meeting Kafka on the way: Algorithmic decision-making in higher education. E-Learning and Digital Media, 14(3), 138-163.
Prinsloo, P. (2020). Of ‘black boxes’ and algorithmic decision-making in (higher) education–A commentary. Big Data & Society, 7(1), 2053951720933994.
Prinsloo, P. and Slade, S. (2018). Mapping responsible learning analytics: a critical proposal. In: Responsible Analytics & Data Mining in Education: Global Perspectives on Quality, Support, and Decision-Making. Routledge.
Slade, S., & Prinsloo, P. (2013). Learning analytics: Ethical issues and dilemmas. American Behavioral Scientist, 57(10), 1510-1529.
Wise, A. F., Sarmiento, J. P., & Boothe Jr, M. (2021, April). Subversive learning analytics. In LAK21: 11th International Learning Analytics and Knowledge Conference (pp. 639-645).