This paper offers a methodological framework to research data practices in education critically. Data practices are understood in the generic sense of the word here, i.e., as the actions, performances, and the resulting consequences, of introducing data-producing technologies in everyday educational situations. The paper first distinguishes…

This study investigates current approaches to learning analytics (LA) dashboarding while highlighting challenges faced by education providers in their operationalization. We analyze recent dashboards for their ability to provide actionable insights which promote informed responses by learners in making adjustments to their learning habits. Our…

The digitalisation and datafication of education has raised profound questions about the changing role of teachers' educational expertise and agency, as automated processes, data-driven analytics and accountability regimes produce new forms of knowledge and governance. Increasingly, research is paying greater attention to the significant role of…

The changing landscape of science and science education has provided an impetus to re-imagine how science can be taught in schools. The dawn of the Big Data era, especially in astronomy, and the notion of "Science as Practice" are some of the developments driving this need for a re-imagination of science education. This current work…

There is a significant amount of data available about students and their learning activities in many educational systems today. However, these datasets are frequently spread across several different digital services, making it challenging to use them strategically. In addition, there are no established standards for collecting, processing,…

For many justifications, the collection, analysis, and use of educational data are central to the evaluation and improvement of students' progress and learning outcomes. The use of data in educational evaluation and decision making are expected to span all layers--from the institution, teachers, students, and classroom levels, providing a…

Here we demonstrate how more highly interoperable computable knowledge enables systems to generate large quantities of evidence-based advice for health. We first provide a thorough analysis of advice. Then, because advice derives from knowledge, we turn our focus to computable, i.e., machine-interpretable, forms for knowledge. We consider how…

Identifying at-risk students as soon as possible is a challenge in educational institutions. Decreasing the time lag between identification and real at-risk state may significantly reduce the risk of failure or disengage. In small courses, their identification is relatively easy, but it is impractical on larger ones. Current Learning Management…

Data-driven instruction is still a huge scope and has many shades. One promising way of adding learning analytics to traditional teaching is to offer teachers accessible, data-driven information, either in a dashboard style or with a UI with which they could perform their own analysis on student data (e.g., Ben-Naim, Bain, & Marcus, 2009;…