WORKSHOP: Fostering ethical and equitable learning analytics, LAK 24

WHEN

Monday 18th March 2024

1:30pm to 5:00 pm

WHERE

LAK24 Conference

Kyoto, Japan

WORKSHOP ABSTRACT

The importance of the ethical use of data; tackling unintended bias and value judgements in the selection of data and algorithms; and the need to facilitate equity, fairness and transparency in learning analytics to support positive social change in education systems is increasingly recognized by researchers, practitioners and policy makers that are part of the learning analytics (LA) community.

There is a growing and rich literature identifying the key issues and promoting a varied set of tool based and values based interventions and frameworks to support these commitments (Holmes, et al., 2022; Viberg et al., 2023). However, there is a need to further support the development of ethical and equitable learning analytics in practice (Baker & Hawn, 2022; Williamson & Kizilcec, 2022). This is a challenging area to navigate, as not only are there significant debates about the underlying philosophical position that these discussions involve (Hakimi et al., 2021); but also, the recognition that often the attempt to encode complex social concepts, such as fairness, accountability, privacy, and equity into specific practices and guidelines is fraught with difficulty (Khalil, et al., 2023; Selwyn, 2019; Stark et al., 2021; Viberg et al. 2022).

In practice, this leads to two significant challenges for stakeholders working in this space. One is how to support the complex interchange of knowledge across varied “knowledge traditions” – as those in the LA community are tasked with translating work from varied related academic fields, such as, Philosophy and Technology, the Sociology of Education and Critical Data Studies, whilst also connecting with and designing with an awareness of policy and educator demands in a highly varied range of legal, cultural, educational, social, and technological contexts around the world (Eynon, 2023). Second is how to capture individual expertise and experience of those in the LA community who are developing responsible LA (Cerratto-Pargman et al., 2022) in a way that can be shared and further developed by others working in this space.

WORKSHOP OBJECTIVES AND OUTCOMES

The goal of this workshop is to address both knowledge translation challenges in order to support the development of ethical and equitable LA. It will focus on:

• Mapping the landscape of current resources available to the community to use in their practice
• Identifying the current dilemmas faced by stakeholders in developing responsible LA
• Sharing the varied ways that LA scholars are translating knowledge and expertise from different academic, practical and policy sources into their own practice; and the strengths and challenges of doing so
• Exploring the potential and need for other kinds of resources that reflect varied “real-world” experiences to support equitable and ethical LA
• Determining ways to better support dissemination of knowledge translation practices across the LA community.

Taken together, the workshop aims to develop a series of recommendations for how different stakeholders both within and beyond the LA community could support varied forms of knowledge creation and translation to inform the development of ethical and equitable learning analytics.

These recommendations will be written up as a short open access document synthesizing key outcomes and agreed follow-on activities. Such activities may include a special issue proposal on this topic, and future events to develop a LA community around these important issues.

WORKSHOP ORGANISERS

• Rebecca Eynon, University of Oxford
• Simon Knight, University of Technology Sydney
• Olga Viberg, KTH Royal Institute of Technology
• Alyssa Wise, Vanderbilt University

WORKSHOP FORMAT

This interactive workshop will be held as a half-day event. Participants will have the opportunity to share their own approaches and experiences of this area and learn from others via a series of interactive sessions. The draft workshop schedule is as follows:

• Introductions
• Guided roundtable: Resources, dilemmas, practices and dissemination for responsible LA 
  Participants give 3-5-minute presentations based on submitted abstracts  
• Coffee Break
• Breakout sessions
  A: Resources for Responsible Analytics: Gap Analysis  
  Brainstorming available resources to the LA community and relating the value of these for specific stakeholders and problems using a series of equity-based scenarios.
  B: OER for Responsible Analytics
  Review and input into the development of open educational resources, based on ‘real-world’ experiences of educators, to help inform the development of equitable LA
• Report back
• Conclusions and next steps

MORE INFORMATION

To find out more about LAK24 and to register for the workshop please see https://www.solaresearch.org/events/lak/lak24/.

To contact the workshop organisers please email edtechequity@education.ox.ac.uk.

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 

Cerratto Pargman, T., McGrath, C. et al., (2021). Responsible learning analytics: creating just, ethical, and caring. In: Companion Proceedings 11th International Conference on Learning Analytics & Knowledge (LAK21). https://oro.open.ac.uk/75925/ 

Eynon, R. (2023). The Future Trajectory of the AIED Community: Defining the ‘Knowledge Tradition’ in Critical Times. International Journal of Artificial Intelligence in Education, 1-6. https://doi.org/10.1007/s40593-023-00354-1 

Hakimi, L., Eynon, R., & Murphy, V.A. (2021). The ethics of using digital trace data in education: A thematic review of the research landscape. Review of Educational Research, 91(5), 671-717. https://doi.org/10.3102/00346543211020116 

Holmes, W., Porayska-Pomsta, K., Holstein, K. et al. (2022). Ethics of AI in Education: Towards a Community-Wide Framework. International Journal of Artificial Intelligence in Education, 32, 504–526. https://doi.org/10.1007/s40593-021-00239-1 

Khalil, M., Prinsloo, P., & Slade, S. (2023). Fairness, Trust, Transparency, Equity, and Responsibility in Learning Analytics. Journal of Learning Analytics, 10(1), 1-7. https://doi.org/10.18608/jla.2023.7983  

Selwyn, N. (2019). What’s the problem with learning analytics? Journal of Learning Analytics, 6(3), 11-19. https://doi.org/10.18608/jla.2019.63.3  

Stark, L., Greene, D., & Hoffmann, A.L. (2021). Critical perspectives on governance mechanisms for AI/ML systems. The cultural life of machine learning: An incursion into critical AI studies, pp.257-280. https://doi.org/10.1007/978-3-030-56286-1 

Viberg, O., Mutimukwe, C., & Grönlund, Åke. (2022). Privacy in LA Research: Understanding the Field to Improve the Practice.  Journal of Learning Analytics, 9(3), 169-182. https://doi.org/10.18608/jla.2022.7751 

Viberg, O., Jivet, I., Scheffel, M. (2023). Designing Culturally Aware Learning Analytics: A Value Sensitive Perspective. In: Viberg, O., Grönlund, Å. (eds) Practicable Learning Analytics. Advances in Analytics for Learning and Teaching. Springer, Cham. https://doi.org/10.1007/978-3-031-27646-0_10 

Williamson, K., & Kizilcec, R. (2022). Learning analytics dashboard research has neglected diversity, equity and inclusion. In, L@S’ 21: Proceedings of the Eighth ACM Conference on Learning @ Scale, pp. 287-290, https://doi.org/10.1145/3430895.3460160