This contribution deals with effect measures for covariates in ordinal data models to address the interpretation of the results on the extreme categories of the scales, evaluate possible response styles, and motivate collapsing of extreme categories. It provides a simpler interpretation of the influence of the covariates on the probability of the…

Higher education has faced increased pressure to prove its quality through 'economic efficiency' and 'value for money', thrusting institutions into what researchers call an 'audit culture'. This study explores whether and how the audit culture has impacted educational developers in Canadian post-secondary institutions. Results include a summary of…

Biomedical data science education faces the challenge of preparing students for conducting rigorous research with increasingly complex and large datasets. At the same time, philosophers of science face the challenge of making their expertise accessible for scientists in such a way that it can improve everyday research practice. Here, we…

Statistical literacy has a large and important role in the teaching of statistics. Most mathematics and statistics courses are hierarchical, and the earlier material forms the foundation for later material. We construct a hierarchical structure for an introductory statistics course using Rasch analysis of the student scripts for the final…

In 2009, John Hattie's book Visible Learning: A Synthesis of over 800 Meta-Analyses Relating to Achievement brought big data to education. In the decade and a half since Visible Learning was originally published it has been aggressively marketed and has now grown into a large suite of branded books, tools, and products. Visible Learning continues…

Research based on educational data mining conducted at academic institutions is often limited by the institutional policy with regard to the type of learning management system and the detail level of its activity reports. Often, researchers deal with only raw data. Such data normally contain numerous fictitious user activities that can create a…

With the growing attention on large-scale educational testing and assessment, the ability to process substantial volumes of response data becomes crucial. Current estimation methods within item response theory (IRT), despite their high precision, often pose considerable computational burdens with large-scale data, leading to reduced computational…

This paper adds an algorithmic epistemology perspective to previous works that examine the datafication of subjective social and emotional characteristics, perceptions, and behaviours. The paper employs a comparative epistemological approach to explore two behavioural educational platforms: RedCritter Teacher and Panorama Education. We unpack…

Purpose: As one of the buzzwords in the present age with considerable impacts in tertiary institutions, social media use in online teaching, learning and information dissemination have been extensively discussed in extant literature. This paper aims to explore the existing campus sustainability appraisal (CSA) tools to identify the length at which…

Adaptive teachers use student data to guide instruction. Learn about using an anecdotal record form to support adaptive teaching. In this article, the authors describe how teachers can use learning trajectories to make adaptive decisions to meet the needs of their students. They provide an example using the U .S. Math Recovery Council's learning…

In today's data-driven and data-informed educational landscape, leaders face increasing pressure to make decisions and present results based on what appear to be comprehensive statistical analyses. However, the ethical implications of these responsibilities can be complex, particularly when statistical results carry the potential to be…

Educator labor markets vary considerably across the country and can change quickly during recessions. We use data from the Quality Workforce Indicators (QWI) on educators in Elementary and Secondary Schools from 2000-01 to 2022-23. We demonstrate how to transform the quarter-level data in the QWI to construct valid educator labor market measures.…

High-volume, high-quality and diverse datasets are crucial for advancing research in the education field. However, such datasets often contain sensitive information that poses significant privacy challenges. Traditional anonymisation techniques fail to meet the privacy standards required by regulations like GDPR, prompting the need for more robust…

In the context of digital transformation processes in schools, the introduction of technology is often discussed in terms of the individualization and optimization of teaching, learning and school management. However, it is often neglected that technologies are preconfigured and provide socio-material offers that invite certain actions and thus…

Early Warning Systems (EWS) are research-based analytics that use statistical models to assess dropout risk. School leaders use this analytic to consolidate data about a student and provide actionable data to craft an intervention. Little is currently known about the processes involved in school implementation or data use. By analyzing Montana EWS…