This explanatory case study investigated why educators (teachers, administrators and support teachers) in a northeast charter school found it difficult to implement a data-driven decision-making (D3M) process. To comprehend where the breakdown in the process occurred, the researcher examined the cultural, technological and political barriers that…

Credit recovery in public education has become an intervention used widely in the United States to address the rising number of students who are not meeting with success in the completion of required courses in the traditional education setting. With the passing of the Every Student Succeeds Act in 2015, public schools sought a method to motivate…

With the adoption of Learning Management Systems (LMSs) in educational institutions, a lot of data has become available describing students' online behavior. Many researchers have used these data to predict student performance. This has led to a rather diverse set of findings, possibly related to the diversity in courses and predictor variables…

This paper examines the development of data infrastructure in Australian schooling with a specific focus on interoperability standards that help to make new markets for education data. The conceptual framework combines insights from studies of infrastructure, economic markets and digital data. The case of the Australian National Schools…

This study evaluated the effectiveness of a family-centered feeding intervention, Easing Anxiety Together with Understanding and Perseverance (EAT-UP™), for promoting food acceptance of children with autism spectrum disorder at home. A concurrent multiple-baseline design was used with systematic replication across three families. Baseline was…

Each edition of the IDRA Newsletter strives to provide many different perspectives on the issues in education topics discussed and to define its significance in the state and national dialogue. This issue focuses on Using Data for Action and includes: (1) Community and School Use of Data for College Readiness and Postsecondary Success (Karmen…

The State Family Outcomes Measurement System Framework (S-FOMS) is a framework originally developed by the Early Childhood Outcomes (ECO) Center that identifies seven key components of a high-quality family outcomes measurement system at the state level. This document contains background information about the framework's seven components, 15…

The U.S. Department of Education's Office of English Language Acquisition (OELA) helps to ensure that ELs attain English proficiency and achieve academic success. OELA accomplishes these goals by administering discretionary grant programs to prepare professionals for teaching and disseminating information about educational research, practices, and…

Data science is emerging as a field that is revolutionizing science and industries alike. Work across nearly all domains is becoming more data driven, affecting both the jobs that are available and the skills that are required. As more data and ways of analyzing them become available, more aspects of the economy, society, and daily life will…

The Office of Inspector General (OIG) works to promote efficiency, effectiveness, and integrity in the programs and operations of the U.S. Department of Education (Department). Through OIG's audits, inspections, investigations, and other reviews, they continue to identify areas of concern within the Department's programs and operations and…

Purpose: We aimed to compare the speech of parents and examiners as they elicited language samples from minimally and low-verbal (MLV) children and adolescents with autism spectrum disorder (ASD), while following the same semi-structured elicitation protocol, Eliciting Language Samples for Analysis-Adolescents (ELSA-A). We also compared the speech…

A prominent goal of colleges and universities today is to enact data-driven teaching and learning. Faculty clearly play a key role, and yet they tend to have limited time, a lack of training in assessment or education research, and few incentives for engaging in this work. We describe a framework designed to address the practical and cultural…

The growing use of machine learning for the data-driven study of social issues and the implementation of data-driven decision processes has required researchers to re-examine the often implicit assumption that datadriven models are neutral and free of biases. The careful examination of machine-learned models has identified examples of how existing…

This quantitative research analyzes the impact of the Web Application for the Educational Process on Compound Interest (WAEPCI) considering the machine learning and data science. The sample is composed of 46 students who studied the Financial Mathematics course in a Mexican university during the 2017 school year. WAEPCI presents the calculation of…

A driving factor in designing interactive museum exhibits to support simultaneous users is that visitors learn from one another via observation and conversation. Researchers typically analyze such collaborative interactions among museumgoers through manual coding of live- or video-recorded exhibit use. We sought to determine how log data from an…