Special educator shortages have threatened the provision of services U.S. to students with disabilities since the passage of the Education for All Handicapped Children Act in 1975, since reauthorized as the Individuals with Disabilities Education Act. We conducted a systematic review of studies that used administrative data to study the special…

Across the country, district-community teams are tackling pressing and complex educational challenges with Inclusive Innovation, an education R&D model that starts with centering the needs of those most impacted by these challenges. This paper shares the stories, solutions, outcomes, and learnings from years of deep collaboration in the words…

As countries make continued investments in early childhood care and education (ECCE) systems, data and measurement play a critical role in outlining strengths and areas for improvement. To promote data-driven decision-making across all levels of early childhood systems, measurement must be valid, reliable, and feasible, especially when used at…

We tested 10 data-based strategies to better identify student homelessness in Camden City School District, which has a student body from minoritized backgrounds. We operationalized strategies through a research-practice partnership, following the federal homelessness definition. Data span 5 years (2014-15 through 2018-19), including integrated…

Our research aims to predict students' academic performance by considering the variables affecting academic performance in science courses using the deep learning method from machine learning algorithms and to determine the importance of independent variables affecting students' academic performance in science courses. 445 students from 5th, 6th,…

Purpose: This paper defines and assesses student learning patterns under the influence of problem-based learning (PBL) and their classification into a reasonable minimum number of classes. Study utilizes PBL implemented in an undergraduate Statistics and Operations Research course for techno-management students at a private university in India.…

This study builds on the long-identified gap between the marketing industry and marketing academia. The research investigates the relationship between resource-based factors and marketing faculty's intention to use marketing analytics technology. The theoretical framework for this study is the concerns-based adoption model (CBAM). Resource-based…

The European Education Area aims to support Member States' efforts in enhancing the educational attainment of younger generations. In this policy context, there is a need for an objective tool to assess the educational outcomes of EU countries. The present report addresses this need by pursuing two objectives: (1) providing a comprehensive method…

Support vector classifiers are one of the most popular linear classification techniques for binary classification. Different from some commonly seen model fitting criteria in statistics, such as the ordinary least squares criterion and the maximum likelihood method, its algorithm depends on an optimization problem under constraints, which is…

Purpose: This study aims to explore children's cognitions about data flows online and their understandings of algorithms, often referred to as algorithmic literacy or algorithmic folk theories, in their everyday uses of social media and YouTube. The authors focused on children ages 8 to 11, as these are the ages when most youth acquire their own…

Data-driven decision-making (DDDM) requires educational leaders and teachers to monitor and evaluate programs and processes and link results to individual and organizational outcomes. A classroom teacher's ability to collect and review data, analyze data to identify trends and strategies for student achievement, and use data to determine and…

Institutions of higher education are presented as vital pathways to economic mobility, particularly for working class, first generation, and students of color. Yet the inequitable outcomes produced by post-secondary institutions suggest structures and systems of exclusion and enclosure. In this critical mixed methods case study, we explore the…

Data-based individualization (DBI) is a framework that allows educators to make timely and informed decisions about student progress in academics or behavior. In this article, we focus on the DBI framework as applied to math intervention within a tiered support model for students experiencing math difficulty. We review how DBI starts with an…

Being able to understand, interpret, and critically evaluate data is necessary for all individuals in our society. Using the PreK-12 Guidelines for Assessment and Instruction in Statistics Education-II (GAISE-II; Bargagliotti et al., 2020) curriculum framework, the current paper outlines five evidence-based recommendations that teachers can use to…

This study examines the levels of Media and Information Literacy (MIL) among underprivileged communities, shedding light on their digital literacy and online behaviour. Rooted in the UNESCO Media and Information Literacy framework, focusing on specific dimensions of MIL, with an emphasis on assessing the ability to retrieve, critically evaluate,…