Despite increasing calls to build equitable data infrastructures, the education field has yet to have a shared guideline around equitable education data management and stewardship. To address this gap, we propose one framework from the data governance literature: the FAIR (Findable, Accessible, Interoperable, Reusable) data management principles…

This paper examines ways in which the ethics of data-driven technologies might be (re)politicised, particularly where educational institutions are involved. The recent proliferation of principles, guidelines, and frameworks for ethical 'AI' (artificial intelligence) have emerged from a plethora of organisations in recent years, and seem poised to…

For the last decade or more, circulation numbers of physical materials have declined in academic libraries across the United States. In the spring of 2020, the COVID-19 pandemic drastically altered society and daily life, not to mention library functions. In particular, fears of contagion via physical surfaces and transmission by contact led many…

In the recent Australian 2021 census, the socio-technical construct of algorithmically driven decision-making processes made LGBTQI+ data as a category of diversity, inclusion and belonging an absent presence. In this paper, we position the notion of 'data justice' in relation to the entrenchment of inequalities and exclusion of LGBTQI+ lives and…

Higher education is awash with data that, when refined, facilitates data-informed decisions. Such decision-making is much more prevalent in support of undergraduate education given the much larger number of undergraduates pursuing higher education in contrast to the much smaller proportion of graduate students. A simple extension of current…

The data sets used in statistics education have changed over time, from mathematically "well-behaved" ones that facilitated computation, to more context-rich sources and now, with the increasing influence of data science practices, to "found" data, often from open data sites. As data sources change, it is important for…

The General Data Protection Regulation (GDPR) in the European Union contains directions on how user data may be collected, stored, and when it must be deleted. As similar legislation is developed around the globe, there is the potential for repercussions across multiple fields of research, including educational data mining (EDM). Over the past two…

Determining whether a state's young people are on track for a life of opportunity is a difficult task for governors and state leaders. States can be both awash in data and unable to easily access and use that data to inform policy. State longitudinal data systems that meaningfully connect workforce, higher education, K-12, and early childhood…

This article proposes a pragmatic approach to data justice in education that draws upon Nancy Fraser's theory. The main argument is premised on the theoretical and practical superiority of a deontological framework for addressing algorithmic bias and harms, compared to ethical guidelines. The purpose of a deontological framework is to enable the…

Exploratory data analysis (EDA) is an iterative, open-ended data analysis procedure that allows practitioners to examine data without pre-conceived notions to advise improvement processes and make informed decisions. Education is a data-rich field that is primed for a transition into a deeper, more purposeful use of data. This article introduces…

While the use of numbers in governance has a long history, the kinds of numbers we now produce enable a range of new possibilities for monitoring, regulation and policy decision-making. Global policy actors are now calling for a steep increase in investment in education data. The growing trust in numbers has been critiqued by education policy…

The COVID-19 pandemic negatively affected the quality of data from educational testing programs. These data were previously used for many important purposes ranging from placing students in instructional programs to school accountability. In this article, we draw from the research design literature to point out the limitations inherent in…

Preparing teacher candidates to analyze achievement data is necessary to develop the understanding of how academic data is collected and used to inform instruction. Collecting achievement data in the area of literacy includes a variety of assessments focused on reading and writing. A critical component of analyzing data involves working in…

Special education and related professionals often understand the importance of adhering to intervention procedures (i.e., fidelity), but may be less familiar with concepts of intervention intensity. In some cases, professionals may correctly apply an intervention, but with insufficient intensity. This can result in an intervention failing to…

This paper explores co-design in Japanese education for deploying data-driven educational technology and practice. Although there is a growing emphasis on data to inform educational decision-making and personalize learning experiences, challenges such as data interoperability and inconsistency with teaching goals prevent practitioners from…