Lindsay's Data Visualisation Blog

9 – Spotting animals!

This week I chose to track data around animals that I spotted. The visualisation captured two broad areas that I spotted the critters, and some features about them (where I remembered) eg if they had a lead on, if they were making noise or if they were eating.

Kept the key in landscape though!

While examining this data I had been contemplating the inherent trust that the wider public seem to have in data. Or perhaps it is more of a trust in data if the option is between human perspective or data, that Anagnostopoulos et al (2013) describe as the ‘cultural authority of data’. In collecting and analysing data in the Dear Data style, it combines the two and creates data generated from a human perspective which I think displays more of the complexities of data more overtly – eg omissions, opinions, inherent agendas (although often data also features these characteristics).

For example, in viewing the distinct types of animals I saw over both areas that I sorted my data into – dogs feature heavily. This is due to a local bias where I own a dog – and like dogs better than most animals! Also, spotting dogs is important to me as a dog owner so that I can adjust my practice (eg recall my own dog, or make sure that I remove the bacon treats from the dog accessible pocket). This is true of governing with data, where practice can be influenced by local needs. So that an improvement in test scores need not reflect improved attainment but could be that teaching to the test has arisen, or the assessment instrument drew on local knowledge/perspective that allowed learners to respond positively. (Anagnostopoulos et al, 2013; Fontaine, 2016). I do not agree with the displacement notion that if we can track ‘it’ then why bother from a sociological/psychological perspective in understanding why we do ‘it’ (Anderson, 2011).

Kitchin (2014) reflects that even big data is:

  • incomplete
  • shaped by assumptions
  • always understood within a conceptual framework.

This is especially important as big data in education is increasingly used for improvement. (I have a bit of a thing with the language around improvement – what became to be known as the improvement ideology underpinned the thinking behind the Highland Clearances. The majority of those affected by the clearances wouldn’t have described the displacement as improvement but I guess that’s perspective. see Brown, MacKillop). Taking my data this week, depending on your ideology about animals and noise you could portray it as a positive or a negative where the animals were making noise. If education is described as not neutral, and therefore operating within an ideology, then assumptions and starting points must be examined (Prinsloo, 2020). This is even more important when governing with data – and actors making decisions about the educational data are often one step removed from the learners and the teachers. I feel what is described as obscured for objectivity is sometimes better described as out of context (or surface level) information.

Anagnostopoulos, D., Rutledge, S.A. & Jacobsen, R. 2013. Conclusion: The Infrastructure of Accountability: Tensions, Implications and Concluding Thoughts. In, Anagnostopoulos, D., Rutledge, S.A. & Jacobsen, R. (Eds) The Infrastructure of Accountability: Data use and the transformation of American education.

Anagnostopoulos, D., Rutledge, S.A. & Jacobsen, R. 2013. Introduction: Mapping the Information Infrastructure of Accountability. In, Anagnostopoulos, D., Rutledge, S.A. & Jacobsen, R. (Eds.) The Infrastructure of Accountability: Data use and the transformation of American education.

Anderson, T. (ed) (2011) The Theory and Practice of Online Learning (AU Press: Athabasca Uni).

Brown, D. G (2014) ‘The Highland Clearances and the politics of memory’. Unpublished PhD thesis. University of Milwaukee.  

Fontaine, C. 2016. The Myth of Accountability: How Data (Mis)Use is Reinforcing the Problems of Public Education, Data and Society Working Paper 08.08.2016.

Kitchin, R. (2014) ‘Big Data, New Epistemologies and Paradigm Shifts’ in Big Data and Society pp. 1-12. DOI: 10.1177/2053951714528481

MacKillop, A., ‘The Political Culture of the Scottish Highlands from Culloden to Waterloo’ The Historical Journal (46) pp. 511-532

Ozga, J. 2016. Trust in numbers? Digital Education Governance and the inspection process. European Educational Research Journal, 15(1) pp.69-81

Prinsloo, P. 2020. Data frontiers and frontiers of power in (higher) education: a view of/from the Global SouthTeaching in Higher Education, 25(4) pp.366-383

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