Block 2 reflections

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.

References

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

Week 8: a week of wellbeing

This week was Moray House’s Health and Wellbeing week which prompted me to track my own wellbeing for my data visualisation. Given the uncertainties, isolation and move to online learning during the COVID-19 pandemic, mental health and general wellbeing are now more important than ever (Grubic et al. 2020).

Week 8 visualisation
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I tried to include as many factors as possible which I thought are contributing to my wellbeing. Some of the data, for example the data for screen time, may not be accurate as my daughters sometimes use my phone to watch programmes or phone their grandparents on WhatsApp. Nonetheless, I still thought that the time I spend in front of a screen (which didn’t include screen time for work) was very high yet I couldn’t find the time to exercise.

While there are many offerings from schools and universities to support wellbeing, I was thinking about how teachers would use data in order to address the wellbeing of their students.

Teachers can play an active part in student wellbeing by considering ‘changes to syllabus, curriculum, and university culture itself’ (Bail et al. 2019, p.676). Bail et al. also point out that being approachable and presenting learning materials clearly, were important contributors for students’ wellbeing. There is therefore a direct link between teachers and wellbeing and having access to data may help to improve students’ happiness.

Being aware of privacy and surveillance issues can also be a factor in wellbeing. Students who feel that they are constantly being monitored, may feel anxious or more under pressure. Of course, data like this may include sensitive information and students might not want to share such personal details with their teachers. Teachers, on the other hand, need to understand how to handle and interpret data in order to make meaningful decisions. As surfaced in Raffaghelli & Stewart (2020), teachers should be equipped with data literacy skills that look beyond technical skills and address datafication in education.

References

Baik, C., Larcombe, W. & Brooker, A. (2019). How universities can enhance student mental wellbeing: the student perspective. Higher Education Research & Development, 38(4), 674-687, DOI: 10.1080/07294360.2019.1576596

Grubic, N., Badovinac, S. & Johri, A.M. (2020). Student mental health in the midst of the COVID-19 pandemic: A call for further research and immediate solutions. International Journal of Social Psychiatry, 66(5), 517-518. doi:10.1177/0020764020925108

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

Week 7: a week of highlights

No doubt, this week’s highlight has been the return of my girls to nursery/school. For this week’s data visualisation, however, I decided to visualise my highlighting of one of the core readings (Williamson, Bayne & Shay, 2020). Each rectangle represents a page. I always print out my reading material but it would also be possible to use one of the many highlighting tools for marking text online or in PDFs for example.

Week 7 visualisation

I normally don’t use different colours but have done for this exercise and I think it’s something that I will keep up in particular highlighting sections that I may want to refer to in my summaries or assignment.

I can see the potential benefits of analysing how highlighting is used for learners. The data may tell me if I have understood everything or whether I need to go away and do further research. It may also be useful in identifying which texts are particularly relevant for assignments if highlighting has been done with this in mind. Highlighting text is highly individual though and representing data in a dashboard, for example, would most likely not be valid.

In terms of teaching, however, I’m not sure how useful this data would be. Is it more of a box-ticking exercise to indicate that students have read the text similar to measuring attendance described by Brown (2020)? Could teachers actually see how students have engaged with the text? I’m not sure data like this would make an impact on teachers’ pedagogic response due to a lack of quality. It is also questionable whether teachers would have time to look at this data given their already-stretched workload.

Aside from issues regarding quality and validity, instructors may also lack the required data literacy in order to interpret results. As surfaced in Raffaghelli & Stewart (2020, p.435), ‘most approaches to educators’ data literacy address management and technical abilities, with less emphasis on critical, ethical and personal approaches to datafication in education.’  In a world where data are becoming increasingly important, there should be an emphasis on debates around privacy, ethics and equality (Raffaghelli & Stewart, 2020) for teachers, institutions and students.

References

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 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

Week 6: a week of platforms

After seeing The Platform Society chapter I decided to track what platforms I am using this week. I have only tracked when I was at my desk and only recorded the platforms I engaged with most. Nonetheless, my visualisation shows how often I rely on using various platforms on a daily basis.

Week 6 visualisation
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Van Dijck et al. (2018) highlight the potential implications of platformisation on education. With increasingly powerful tech companies, there are fears that education will be more and more governed by big corporations, ‘propelled by algorithmic architectures and business models’.

Platformisation and increased use of technologies in education also raises issues of privacy and surveillance. Being constantly monitored can have an impact on both students and teachers. As surfaced in Brown (2020), dashboards, for example, may have an impact on instructors’ pedagogical strategies.

COVID-19 is likely to have exacerbated the issue of platformisation as the educational technology sector is one of the few industries to profit from the pandemic. Dominated by powerful technical platforms, public education could see long-term consequences as state governance becomes less significant (Williamson et al. 2020). Adopting technologies without challenging the motives of big corporations could see global commercial platforms being incorporated into public education which, in turn, may be a risk to education as a public good (ibid. 2020).

Technologies and practices that were introduced during the pandemic are often regarded as emergency or temporary measures, however, some researchers point out that ‘[a]s these tools become rooted in teaching practice, it will become difficult to go back’ (Teräs et al. 2020, p.870).

My visualisation only shows a fraction of what a machine could have recorded but it nonetheless gives an insight into how entwined various platforms and our daily lives are.

References

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.

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. Eynon, R. & Potter, J. (2020). Pandemic politics, pedagogies and practices: digital technologies and distance education during the coronavirus emergency. Learning, Media and Technology, 45:2, 107-114.