What if there was a better way to collect and interpret assessment data that could strengthen the link between teaching and learning? "Student Assessment: Better Evidence, Better Decisions, Better Learning" is the innovative guide to show you how it is done and done right. This unique book offers a new assessment model focused on…

Classification, a fundamental data analytics task, has widespread applications across various academic disciplines, such as marketing, finance, sociology, psychology, education, and public health. Its versatility enables researchers to explore diverse research questions and extract valuable insights from data. Therefore, it is crucial to extend…

A staple of many spreadsheet-based management science courses is the use of Excel for activities such as model building, sensitivity analysis, goal seeking, and Monte-Carlo simulation. What might those things look like if carried out using Python? We describe a teaching module in which Python is used to do typical Excel-based modeling and…

The purpose of this study is to develop a valid and reliable scale to determine and evaluate the different dimensions of data literacy at school. The study is a quantitative descriptive survey model. The sampling for exploratory factor analysis was formed of 307 and confirmatory factor analysis 338 teachers and school administrators who are on…

Existing toolkits for analyzing movement dynamics in animal ecology primarily focus on individual or group behavior in habitats without predefined boundaries, while methods for studying human activity often cater to bounded environments, such as team sports played on defined fields. This leaves a gap in tools for modeling and analyzing human group…

Native nations have long proven their resilience against the odds, consistently paving a path forward and exercising their sovereign rights as autonomous, self-governing peoples. As the world embarks upon an increasingly digital age, Indigenous peoples face a new threat: datafication. Datafication is "turning nearly every aspect of human life…

The increasing availability of digital tools in science classrooms can provide students with more frequent and easier access to large amounts of data. Large data sets have considerable epistemological potential, as they enable, for instance, the observation of otherwise unobservable phenomena, but it must be assumed that handling them places…

While research on Learning Analytics (LA) is plentiful, it often prioritises perspectives on LA systems over the practical ways instructors use data to analyse and refine the learning process per se. The present study addresses this inadequacy by investigating how student data is employed by educators in UK Higher Education Institutions (HEIs) and…

The Education-to-Workforce (E-W) Indicator Framework offers guidance for using data to promote education outcomes and economic security for all. Developed with input from leading experts from over 20 national and community organizations, it highlights key connections needed among systems to support students as they progress from early education…

This paper deeply discusses the transformation potential of integrating Internet big data into the pre-school education model in colleges and universities. Through in-depth analysis, we studied the challenges and opportunities faced by preschool education in colleges and universities, and discussed the innovative influence of big data technology…

In teacher education, there is a growing need for teachers to become data literate by collecting a variety of data on student learning to assess student progress and inform instruction. Research on pedagogical documentation in education, in particular early childhood education, has been undertaken to make students' learning visible by documenting…

This guide provides information and best practices that will help state education agencies (SEAs) and local education agencies (LEAs) manage data related to students who frequently change schools, with a focus on actions to minimize student learning disruptions and allow agencies to provide support for those particular students. This document…

The robustness of qualitative comparative analysis (QCA) results features high on the agenda of methodologists and practitioners. This article aims at advancing this debate on several fronts. First, in line with the extant literature, we take a comprehensive view on robustness arguing that decisions on calibration, consistency, and frequency…

The significance of ethics in data science research has attracted considerable attention in recent years. While there is widespread agreement on the importance of teaching ethics within computing contexts, there is no clear method for its implementation and assessment. Studies focusing on methods for integrating ethics into data science courses…

As data-informed decision-making continues to evolve across multiple disciplines in higher education institutions, and as the role of research administration continues to expand from proposal submissions, compliance, and managing research and development expenditures to a profession with an active partnership with investigators to support…