Block 2: Week 8 Summary

Teaching

Learning systems are typically powered by biased algorithms [Noble, 2018] and limited types of behavioural data on student activity, rather than learning [Eynon, 2015]. Despite this, the learning system itself is permitted to define the research question [Eynon, 2013] based on the data it is able to collect, rather than the needs of teachers, and without teacher input.

That this data is presented on a dashboard to the teacher, suggests that it has innate use [Bulger, 2016] and value; the decisions behind what is presented and how, may not be made explicit [Williamson et al, 2020]. The dashboard is offered as a way to make a complex situation easy to understand for teachers [Williamson, 2017], but this should not be seen as innately positive. Simplification is transformation: through reductions and assumptions, data is changed. Hence, it has manipulated a situation it perceives as a problem, and made it appear solvable [Eynon, 2013]: a demonstration of the solutionism at its heart.

With limited data and little context, the dashboard presents students in a superficial, datafied form, based on proxies for their learning [Bulger, 2016], effectively misrepresented to their teachers. For those who see this situation only as an issue of too little data, this could lead to the call for more personal data collection [Knox et al, 2020], or a push towards more activity being done where data can be collected [Brown, 2020], positioning the learning system and the teachers using it, in a surveillance role.

If the means by which the dashboard arrives at pronouncements is known, this could help teachers, whose practice it appears to influence [Wise and Jung, 2019], understand how to judge its usefulness [Brown, 2020]. However, to do this, teachers need critical data literacy [Raffaghelli & Stewart, 2020; Sander, 2020]; but, if the teacher is disempowered, and the learning system uses its data to self-regulate, even this will not help [Williamson et al, 2020].

A dashboard may appear to offer personalisation to the teacher but, like personalised learning for students, this may be an illusion, and really no more than choice of pre-selected options. Teachers may have some control over the data gathered by the learning system, and whether students can see it, but they then have to deal with the effect that having this essentially surveillance role [Tsai, et al. 2020], has on their relationship with students [Williamson et al, 2020] and how it makes them see themselves as teachers [Harrison et al. 2020]. True empowerment may only be possible if teachers had control over the decision to have data collected at all.

If the dashboard is visible to the institution, this may impact how a teacher views it: a proxy for their teaching [Williamson et al, 2020] and a means whereby they are datafied [Williamson, 2016]. They may experience this as centralised control and surveillance of their performance, nudged towards ‘correct’ behaviour, like their students, [Knox et al, 2020] or to make changes in their teaching in order to influence the dashboard, rather than for pedagogic reasons [Brown, 2020; Harrison et al. 2020].

Word count: 509

References

Bulger, M., 2016. Personalized Learning: The Conversations We’re Not Having. Data & Society working paper. Available: https://datasociety.net/pubs/ecl/PersonalizedLearning_primer_2016.pdf

Brown, M., 2020. Seeing students at scale: how faculty in large lecture courses act upon learning analytics dashboard data. Teaching in Higher Education. 25(4), pp. 384-400.

Eynon, R., 2013. The rise of Big Data: what does it mean for education, technology, and media research? Learning, Media and Technology, 38 (3), pp. 237-240.

Eynon, R., 2015. The quantified self for learning: critical questions for education. Learning, Media and Technology, 40 (4), pp. 407-411, DOI: 10.1080/17439884.2015.1100797

Harrison, M.J., Davies, C., Bell, H., Goodley, C., Fox, S & Downing, B. 2020. (Un)teaching the ‘datafied student subject’: perspectives from an education-based masters in an English university, Teaching in Higher Education, 25:4, 401-417, DOI: 10.1080/13562517.2019.1698541

Knox, J., Williamson, B. & Bayne, S., 2019, ‘Machine behaviourism: Future visions of “learnification” and “datafication” across humans and digital technologies‘, Learning, Media and Technology, 45 (1), pp. 1-15.

Noble, S. U. 2018. Algorithms of Oppression: How Search Engines Reinforce Racism by Safiya Umoja Noble. NYU Press. doi:10.15713/ins.mmj.3.

Raffaghelli, J.E. & Stewart, B. 2020. Centering complexity in ‘educators’ data literacy’ to support future practices in faculty development: a systematic review of the literature, Teaching in Higher Education, 25:4, 435-455, DOI: 10.1080/13562517.2019.1696301

Sander, I. 2020. What is critical big data literacy and how can it be implemented? Internet Policy Review. 9(2) DOI: 10.14763/2020.2.1479

Tsai, Y-S. Perrotta, C. & Gašević, D., 2020. Empowering learners with personalised learning approaches? Agency, equity and transparency in the context of learning analytics, Assessment & Evaluation in Higher Education, 45 (4), pp. 554-567, DOI: 10.1080/02602938.2019.1676396

Williamson, B. 2016. Digital Education Governance: Data Visualization, Predictive Analytics, and ‘Real-Time’ Policy Instruments. Journal of Education Policy. 31 (2), pp. 123–141. doi:10.1080/02680939.2015.1035758

Williamson, B., 2017. Conceptualising Digital Data in Big Data in Education: The digital future of learning, policy and practice. Sage.

Williamson, B. Bayne, S. Shay, S. 2020. The datafication of teaching in Higher Education: critical issues and perspectives. Teaching in Higher Education. 25(4), pp. 351-365.

Wise A. F., and Jung Y., 2019. Teaching with Analytics: Towards a Situated Model of Instructional Decision-Making. Journal of Learning Analytics. 6(2), pp. 53-69. http://dx.doi.org/10.18608/jla.2019.62.4.

2 Replies to “Block 2: Week 8 Summary”

  1. Great to see your engagement with both the set readings and additional readings for this course. Your post is a succinct account of some of the key issues related to ‘teaching with data’, which you’ve also expanded very well in each of the weekly dataviz reflections. Several of the issues you’ve raised above will be relevant to return to as we move on to the final block on ‘governing with data’. A central concern in this block is how authorities (policy centres, educational leadership and management) use data to ‘see’ and ‘know’ students, staff, faculties, and whole institutions, and to use that knowledge for some form of intervention, assessment or judgment. The kind of data required for this kind of governing increasingly requires commercial software, so we see the governing of education happening through private, for-profit technical systems being embedded in public institutions. It also involves connecting various different data sources together in complex interoperable systems. Look forward to your explorations of some of the issues related to this.

    1. Thank you for your feedback. I’m looking forward to seeing how this ties into the governance strand.

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