This week I chose to track when I had screen time on my mobile, and to a certain extent why. I had three broad categories; social media, searching for information and other.
What interested me in this was what inferences could be made about my accessing the phone in that way – and taking another step how governing organisations in particular (one step removed from the learner) make inferences about learner data in the same way through quantification and classification techniques (Williamson, 2017). This is important in an educational context, and the data can bring the matter it describes into being, and further it can influence behaviour of those trying to meet the measurements (Williamson, 2017).
I decided to reflect on the layout of the visualisation more this week. The more I read, I realised that the accessibility (or perceived accessibility) of the visualisation – and the extent to which it is explanatory and perhaps persuasive it integral to it being used as tool for political intervention (Williamson, 2017). The bar chart is a common and familiar method of communication, and so can be encoded and decoded easily by most audiences to make some interpretation of the information (Hall, 1973). This also links to the visualisation’s potential in which to ‘sell’ it’s message (Sobe, 2013) – for example if this was used by a governing organisation to display engagement it could be a quick engagement tool for a multitude of messages. I have been thinking what Ozga (2016) describes as ‘trust in numbers’ ie the power of data. I wonder what makes it so persuasive, and perhaps official in a way that is not quite the case with language, where people might be more critical of the message.
I also reflected on the interconnectedness of the data in this visualisation, ie the tallys relate to specific space on the bar chart. But without the key and an element of deciphering that relationship between the data is not easily displayed.
Therefore, as a policy tool for a governing organisation the potential for visualisations of learner data could be influential on different areas of society. Since so much of our present and future is invested in the education process. And often produced or analysed or sold by actors in the process who are removed from the key players ie the learners and the teachers. (Williamson, 2017).
Hall, S. (1973) Encoding and Decoding in Television Discourse.
Ozga, J. 2016. Trust in numbers? Digital Education Governance and the inspection process. European Educational Research Journal, 15(1) pp.69-81
Sobe, N. (2013) ‘Educational data at late nineteenth- and early twentieth-century international expositions: ‘accomplisehd results’ and ‘instruments and apparatuses’ in Lawn, M. (ed) The Rise of Data in Education Systems: collection, visualization and use. Oxford: Symposium. Pp. 41-56.
Williamson, B. (2017) Digital Education Governance: political analytics, performativity and accountability. Chapter 4 in Big Data in Education: The digital future of learning, policy and practice. Sage.