I found much inspiration for my data collections in my personal and professional life, and I enjoyed the tasks of gathering information and visualising each collection.
While handling specific data for my visualisations, I became more aware of how this data could be used by me, my workplace, school boards or governmental institutions with opposed interests in mind, depending on how you analyse and interpret data! I noticed, especially with my data visualisation from week 10 on languages, to be conscious of which data are available to whom.
Therefore one of the keywords should be transparency when it comes to governing with data. It should be understandable for all involved which data is collected and why. My personal impression and experience is that companies are often far ahead in data security compared with educational institutions.
In the field of education, collecting and interpreting data is often seen “as a solution to problems of schooling” (Ozga, 2016, p.70). Yet by looking back at the data collections of week 10 and 11, this has to be questioned.
Data gathering can be part of the problem and lead to false conclusions, as explored in week 10, if not collected, stored and analysed correctly. Anagnostopoulos (2013) describes this with data accuracy, which is vital since discussions and decisions that influence education policy are based on data presentations. However, educational data provided by schools “are often turned into simplified presentations” (Willisamson, 2017, p.82).
According to Young (2017), data can contribute to accountability and is “about checking that the school meets aims set elsewhere” (Young, 2017, p.53). Furthermore, the author criticises that the importance of educational knowledge is often underestimated in the school’s governing body. Fontaine (2016) adds to this thought that “accountability processes shift authority and control to policymakers, bureaucrats, and test makers over professional educators” (Fontaine, 2016, p.1). Technology is now part of every lesson in my workplace (week 11). However, the mere use of technology in the classroom should not be synonymous with high-quality teaching and learning. Due to a lack of educational knowledge, this is often to be mixed up by school boards!
Data is not objective (Gitelman et al., 2013; Fontaine, 2016); the awareness for this and data literacy in more general should lead the governing with data. My visualisations of the past weeks, even from the first two blocks, could be used by the governance body of my school. On the one hand, they could be interpreted in my favour because they show, for example, how often I use technology in my classroom. On the other hand, the data could disclose gaps in the frequent use, and questions could be asked by my headteacher why I haven’t used IT tools in a specific lesson. In evaluation meetings after inspections, it is established practice that we as teachers have to justify approaches and methods. We often know our students best, or at least we feel like we do. This could get increasingly difficult with standardised tests of student performances. Comparing students’ achievements by only looking at the data in numbers without acknowledging socio-economic circumstances is highly questionable.
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.
Gitelman, Lisa et al. 82013) “Raw Data” Is an Oxymoron, Cambridge: MIT Press.
Ozga, J. (2016). Trust in numbers? Digital Education Governance and the inspection process. European educational research journal EERJ, 15(1), pp.69–81.
Williamson, B. (2017). Digital education governance: Political analytics, performativity and accountability. In Big Data in Education: The digital future of learning, policy and practice. 55 City Road: SAGE Publications Ltd
Young, H. (2017). Knowledge, Experts and Accountability in School Governing Bodies. Educational management, administration & leadership, 45(1), pp.40–56.