Teaching with Data – Block 2 reflections

Reflecting on the theme ‘teaching with data’, there are a number of ways that I can see how teaching practice could be enhanced by data-driven technologies. Digital technologies and data systems could widen the scope and the reach for teachers, allowing them greater insight and understanding into a learner’s context and learning environment. Equally, it could provide an opportunity to help inform decisions in relation to curriculum planning and organisation, provide real-time data and predictive analytics to help teachers assess the adequacy of their materials and activities, to make real-time or post hoc adjustments, and play a key part in the student feedback and assessment process. 

There is, however, an overriding assumption that these technologies and data will enhance teaching practice and learning processes. That having the knowledge and data at your finger tips and utilising these digital technologies will result in automatically improved instructional practice. Data solutionism has become accepted rhetoric; technology will ‘fix’ education, and with this, a  ‘global higher education industry’ of data solutions has emerged (Williamson et al, 2020).

The literature however points to a lack of research on the impact of these technologies, in particular examining the impact on instructors’ practices (Brown, 2020), as well as how learning analytics are rarely aligned with pedagogical theories (Raffaghellini & Stuart, 2020). This runs in parallel with the argument that there has been an ideological shift that may redefine education as a commercial enterprise rather than for the common good, promoting a new concept of learning and advancing the interests of the service providers and programme developers (Van Dijck, 2018). It is against this backdrop and potential ideological arm wrestle that concerns with the datafication of education come to the fore and were reflected in my own thoughts over the weeks. 

Williamson et al (2020) make reference to the quantification of education under the banner of ‘metric’ power. This categorisation, measurement, and classification of people and activities can lead to pedagogical reductionism, reducing a teacher’s autonomy, and potentially can render some learning invaluable as it cannot be dataified. It is almost paradoxical that the promise of increased visibility and understanding of students through data-driven technologies can lead to a potentially limiting view, where only certain data is visible and presented to the teachers. This has the potential to impact pedagogy as teachers wrestle with the fact that measurement of student performance and engagement can act as proxy measures of their own performance and teaching. Reshaping pedagogy to align with the demands and requirements of what needs to be quantified, these technologies are driving pedagogy, rather than pedagogy driving technological design. 

This leads to the importance of critical data literacy development when considering ‘teaching with data’. I have naturally framed this previously in terms of technical know-how and skills development, however, Raffaghellini and Stuart (2020) argue for an alternative approach that factors in wider issues, such as power dynamics, ethical considerations, the rise of AI, and machine learning, demanding the development of critical data literacies. It is integral that people understand the world around them, not only so they can take an active part in society, but so they can critique, question, and make informed decisions and actions. Data literacies must be developed so that educators can be part of the ongoing conversation and shape the future development and design of online education, moving beyond the ‘learn to code’ mantra. 


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, 384-400, DOI: 10.1080/13562517.2019.1698540

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

Van Dijck, J. (2018). Education. In The Platform Society. : Oxford University Press. Retrieved 1 Mar. 2021, from https://oxford-universitypressscholarship-com.ezproxy.is.ed.ac.uk/view/10.1093/oso/9780190889760.001.0001/oso-9780190889760-chapter-7.

Williamson B., Bayne S., & Shay S. (2020) The datafication of teaching in Higher Education: critical issues and perspectives, Teaching in Higher Education, 25:4, 351-365, DOI: 10.1080/13562517.2020.1748811

One Reply to “Teaching with Data – Block 2 reflections”

  1. A very thoughtful engagement with some of the key issues and readings from the course so far. Glad to see that you are identifying the strong framing discourses of datafication – with all the benefits that are promised – but tempering these with more critical perspectives. The ‘solutionist’ argument you have identified is often criticized for focusing attention on neatly defined ‘computable’ problems to which technical solutions can be applied, but obscuring the underlying causal factors. ‘Learning analytics’ may well be intended to help teachers inform their practices and optimize student outcomes as a result, but maybe the problems of student (dis)engagement, lack of retention, under-performance, low grades, etc, lie elsewhere. Perhaps the real issues are structural inequalities that hold back student achievement, financial pressures, or over-pressurized educational environments that create student performance anxiety, and so on. These are unlikely to be issues that learning analytics tracking could surface, and unlikely therefore to lead to meaningful solutions.

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