During the “governing with data” week, I tried to reflect on some of the governing with data I experience at work especially in my data visualisations of the first two weeks working with communication and rules related data. In all sectors, including education, data are not only heavily used to measure and monitor performance but also to build a “data-driven” policy development engine endorsed by “advanced technology” and/or “scientific” approaches backed up by data. According to Williamson’s 2017 book: Big Data in Education:
“Studies of educational policy, for example, have already begun to engage with the software packages and data infrastructures that enable policy information to be collected, and that also allow policies to penetrate into institutional practices.”
From the readings and the discussions of this block, there are some issues residing in adopting data-driven polices that are impacting decisions regarding students, teachers, educational institutes with a profound implication on the educational sector as a whole (Anagnostopoulos et. al 2013). I would like to use this blog to emphasize some of them:
• Non contextual policy formation: many policies would be developed from singular data points without taking into consideration contextual data, external factors or special circumstances. What Ogza 2016 described as:” ‘thin descriptions’ stripped of contextual complexity, make statistical data a key governing device”, is what I reflected on in my second week’s visualisations. The use of a traffic light KPI performance reporting has become key in many institutions and heavily used to drive business decisions and policies that are not necessarily applicable or reflective of realities.
• Non-educational actors: predictive and AI driven decision making methods to educational governance demonstrates great dependencies on code, algorithms and digital platforms managed by commercial actors that are influencing learning and teaching policies (Williamson 2017): “Digital software allows institutions, practices and people to be in Education constantly observed and recorded as data; those data can then be utilized by learning machines to generate insights, produce ‘actionable’ intelligence, or even prescribe recommendations for active intervention”
• Educational policy colonialization: adopting a ‘global north” data driven policies in other countries / regions with the promise to improve educational systems, better student results and cost effectiveness, does not consider the local gaps and specific educational needs and requirements (Prinsloo, P. 2020). Many countries have capacity and know-how challenges to build their own educational data and platforms and depend on global players to assume the ownership with the power of data.
• Educational infrastructure accountability: according to Anagnostopoulo et. al 2013, test-based data are creating a large-scale information system dependent on data being gathered, processed and released not only to students, teachers and/or educational institutes but: “Data from these systems are made available to ever-widening audiences and used to inform decisions across and beyond the educational system”. This is an issue because it being used to drive polices and make decision without educational sector being the driver or an owner/co-owner of this infrastructures.
There are some other issues highlighted with respect to fast policy, political analytics, open data and the rising privacy and trust concerns that are impacting the educational sector and how it is being governed by data.
The question is how to build data-driven educational policies’ frameworks and platforms that are based on educational sector stakeholders, industry knowledge and ownership, inclusive and contextual to the learning and teaching needs and flexible to allow for continuous improvement and innovation underpinned by trusted and ethical infrastructures?
References
• 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.
• 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
• Williamson, B. Digital Education Governance: political analytics, performativity and accountability. Chapter 4 in Big Data in Education: The digital future of learning, policy and practice. Sage.