To ‘govern education’ means to control and direct the public business of education. This implies that education is a public good (Williamson et al 2020), a stand the vast majority would agree with. Thus, governing education should lie in the hands of public institutions, which we indirectly control in democratic countries.
Big data’s impact on governing education.
Data technologies are to help the governing bodies with this process. They promise to make it more effective and cheaper. However, a close inspection reveals data interventions in education rarely achieve any of those three goals. For example, the IMPACT assessment and feedback tool gathered data on student performance and improvement for teacher job performance (O’Neil 2016), but did so with extreme levels of irregularity of error. In fact, standardisation of student performance tests to monitor broadly understood educational quality is a common manifestation of big data (Anagnostopoulos 2013; Ozga 2016). It forms basis of improvement programs, such as IMPACT, No Child Left Behind (Anagnostopoulos 2013), as well as comparison tools like college rankings (O’Neil 2016), PISA (Ozga 2016) or OECD country rankings (Williamson 2017).
This has multiple negative effects. Firstly, the legislative process is changed – numbers seem easily understood therefore decision making can and is sped up in an unprecedented way (ibid.). This leads to suboptimal legislative outcomes. Secondly, in many cases the data-driven tools create their own vicious cycles. On the small scale this manifests in preparing students to study to the test, rather than develop all-rounded knowledge. Large scale mirrors it. For example, countries introduce educational reforms to match OECD criteria, not in their own best interest (ibid.) Universities hire researchers to skew results to fit college rankings, not because they fit their pedagogical bodies (O’Neil 2016). Hence, these data driven tools become self-serving. This occurs because we aren’t discussing nearly enough where education should be directed to: whether what is being measured is actually what the society values (Biesta 2013). The last data of this blocks shows values that most would agree should be taught, but are never datafied.
In this process, even the fundament of governing is cracking, as the control passes from these public bodies, to the private entities, who gather and utilise the data (Williamson 2017). Loss of control in data technology threatens especially the Global South which already has a long colonial history of being ‘civilised’ with the ‘newest technology’ (Prinsloo 2020). Many of these countries yield to the trend in fear of being left behind (Biesta 2013)
Autistic Students in data-driven education.
For autistic students a lot of advancements have been made in terms of inclusion (Tops et al 2017). This progress is measured with numerical data, i.e. student participation. But as big data loom over schools, that participation is under threat. The first visualisation this block demonstrates how the trends described above can solidify barriers for ASD students. The second data visualisation depicts the discrepancies in numbers caused by current insufficient scientific knowledge and diagnostic tools. We simply cannot get better data at this moment, but autistic students (including the vast numbers of undiagnosed ones) are sitting in classrooms every day. Autism in education is one example why we should be extremely careful not to overly rely on data-driven solutions.
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.
Biesta, G. (2013) Good Education in an Age of Measurement, University of Ljubljana, Faculty of Education
Knox, J. Williamson, B. & Bayne, S. (2020) Machine behaviourism: future visions of ‘learnification’ and ‘datafication’ across humans and digital technologies, Learning, Media and Technology, 45:1, 31-45, DOI: 10.1080/17439884.2019.1623251
O’Neil, C. (2016) Weapons of Math Destruction. Random House Audio (Audible release date 09-06-2016)
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 South. Teaching in Higher Education, 25(4) pp.366-383
Tops, W. Van Den Bergh, A. Noens, I. Baeyens, D. (2017) A multi-method assessment of study strategies in higher education students with an autism spectrum disorder; Learning and Individual Differences 59, pp. 141-148
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.
Williamson, B. Bayne, S. Shay, S. (2020) The datafication of teaching in Higher Education: critical issues and perspectives, Teaching in Higher Education, Vol 25:4, pp. 351-365