For the teaching block I chose to visualise data to illustrate the use of different platforms, highlighting habits when reading, and wellbeing. The first visualisation was closely linked to The Platform Society’s chapter on Education by van Djick et al. (2018, p. 119). The authors suggest that platformisation has implications for education as a common good as it introduces tensions ‘between two […] ideological sets of values: Bildung vis-à-vis skills, education versus learnification, teachers’ autonomy versus automated data analytics, and public institutions versus corporate platforms’. Like so many other aspects of our society, education is relying on a wide range of technologies, developed and marketed by powerful global organisations such as Google, Microsoft, and Amazon etc. This is leading to fears that the ‘adoption of commercial digital learning solutions whose design might not always be driven by best pedagogical practices but their business model that leverages user data for profit-making’ (Teräs et al. 2020, p.863). Will this development potentially reduce the teacher’s role to that of a facilitator? After all, it is pedagogical knowledge that makes teachers invaluable.
Tools such as learning analytics are often regarded as objective and neutral, yet the creation and application of technology solutions are indeed based on individuals’ behaviours, knowledge, norms and values (Lupton & Williamson 2017). Increasing use of learning analytics in teaching can be problematic as ‘the literature has pointed out how seldom learning analytics technology align with pedagogical conceptions and theories, stemming mainly from developers’ priorities rather than educational processes’ (Raffaghelli & Stewart 2020, p.439). Week after week, I am conscious of how my environment, experiences and opinions are impacting on my data tracking. Even though large-scale data collection is likely to be more representative, data will never be unbiased. This has to be taken into consideration when using data instead of teachers for assessment or monitoring of student learning.
While the data visualisations I have produced for this course are hand-drawn, the use of dashboards are becoming increasingly popular, be it for student- or teacher-facing purposes. As surfaced in Brown (2020), teachers can find it difficult to make sense of the data and often struggle to make the connection between the dashboard and their pedagogical philosophy. Perhaps this is a result of not being involved in the creation of these dashboards or not having the necessary skills to interpret the visualisations. Raffaghelli & Stewart (2020), for example, criticise the lack of faculty’s data literacy that goes beyond technical abilities.
Higher education is a competitive market and instructors are playing an important part in it. Williamson et al. 2020 (p.354) remind us that ‘[m]easures of student performance, sentiment, engagement, and satisfaction are also treated as proxy measures of the performance of staff, courses, schools, and institutions as a whole, leading to new claims that HE quality can be adduced from the analysis of large-scale student data’. What implications does this development have for teachers’ flexibility and creativity? Could the pursuit of high rankings lead to a loss of originality?
During the last three weeks I learned that data can be very helpful in giving teachers greater insight into their students’ behaviours and may help them to change their teaching in order to improve learners’ understanding as well as their wellbeing. As demonstrated above, however, the collection and analysis of data can be problematic for teachers and more questions should be asked.
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.
Lupton, D., & Williamson, B. (2017). The datafied child: The dataveillance of children and implications for their rights. New Media & Society, 19(5), 780–794.
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
Teräs, M., Suoranta, J., Teräs, H. & Curcher M. (2020). Post-Covid-19 Education and Education Technology ‘Solutionism’: a Seller’s Market. Postdigital Science and Education, 2,863–878.
van Dijck, J., Poell, T., & de Waal, M. (2018). Chapter 6: Education, In The Platform Society, Oxford University Press.
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