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Zhi Li; Wenxiang Zhang – Education and Information Technologies, 2025
In the swiftly changing realm of education, technology serves as a key instrument in transforming the methods of teaching, learning experiences, and educational outcomes. Legal and governance issues, integral to maintaining order and justice in societies, are equally pertinent in the realm of education. The digital age introduces concerns like…
Descriptors: Educational Technology, Technology Uses in Education, Technological Advancement, Legal Responsibility
Tsai, Tiffany; Tosh, Katie – RAND Corporation, 2020
Teachers' use of student data to inform instruction is commonly accepted as sound educational practice, and this data use is only likely to grow as more data, as well as more-complex data, become increasingly available to educators. However, numerous studies reveal inconsistent data use among teachers and an overall lack of the preparation and…
Descriptors: Data Analysis, Data Use, Database Management Systems, Personnel Data
Shaw, Mairead; Flake, Jessica K. – Educational Measurement: Issues and Practice, 2023
Clustered data structures are common in many areas of educational and psychological research (e.g., students clustered in schools, patients clustered by clinician). In the course of conducting research, questions are often administered to obtain scores reflecting latent constructs. Multilevel measurement models (MLMMs) allow for modeling…
Descriptors: Hierarchical Linear Modeling, Research Methodology, Data Analysis, Structural Equation Models
Jane Kalista – UNESCO International Institute for Educational Planning, 2023
This conceptual framework aims to build a shared and comprehensive understanding of what constitute EiE data and of the concepts and processes that underpin and guide work on education in emergencies data across a range of contexts, including acute emergencies, protracted crises, and displacement. The framework also presents a number of strategic…
Descriptors: Emergency Programs, Resilience (Psychology), Social Values, Prevention
Hilbert, Sven; Coors, Stefan; Kraus, Elisabeth; Bischl, Bernd; Lindl, Alfred; Frei, Mario; Wild, Johannes; Krauss, Stefan; Goretzko, David; Stachl, Clemens – Review of Education, 2021
Machine learning (ML) provides a powerful framework for the analysis of high-dimensional datasets by modelling complex relationships, often encountered in modern data with many variables, cases and potentially non-linear effects. The impact of ML methods on research and practical applications in the educational sciences is still limited, but…
Descriptors: Artificial Intelligence, Online Courses, Educational Research, Data Analysis
Swist, Teresa; Humphry, Justine; Gulson, Kalervo N. – Learning, Media and Technology, 2023
There is a broad impetus across policy and institutional domains to expand public engagement and involvement with emerging technology research and innovation. Yet innovative theory, methods, and practices to critically explore algorithmic system controversies and democratic possibilities are still in nascent form. In this paper, we bring together…
Descriptors: Algorithms, Data Analysis, Democracy, Design
Cornman, Stephen Q.; Reynolds, David; Zhou, Lei; Ampadu, Osei; D'Antonio, Laura; Gromos, David; Howell, Malia; Wheeler, Stephen – National Center for Education Statistics, 2019
High demand exists for data to analyze the equitable distribution of school funding within and across school districts. In response to this growing demand, the National Center for Education Statistics (NCES) developed a new collection of finance data at the school level--the School-Level Finance Survey (SLFS). The SLFS collects at the school level…
Descriptors: Educational Finance, Data Collection, Feasibility Studies, Elementary Secondary Education
Sosa, Giovanni – RP Group, 2022
The first step to addressing equity gaps is to identify them. How can community colleges determine, with some degree of certainty, whether one or more student groups on a campus is in need of assistance in order to succeed? This paper tackles this question by delving into the three methods typically used to identify equity gaps, comparing and…
Descriptors: Equal Education, Community College Students, Disproportionate Representation, Data Analysis
Marjorie Cohen; Steve Klein; Cherise Moore – Career and Technical Education Research Network, 2020
By partnering with researchers, state CTE administrators have the opportunity to better understand CTE programming and practices across their states. This is the fourth in a series of six practitioner training modules developed as part of the Career & Technical Education (CTE) Research Network Lead. Designed for CTE practitioners and state…
Descriptors: Vocational Education, Educational Research, Research Utilization, Data Use
Cornman, Stephen Q.; Zhou, Lei; Ampadu, Osei; D'Antonio, Laura; Gromos, David; Wheeler, Stephen – National Center for Education Statistics, 2018
This report presents school-level finance data on expenditures by function from the School-Level Finance Survey (SLFS). The SLFS is an extension of two existing collections being conducted by the National Center for Education Statistics (NCES) in collaboration with the Census Bureau: the School District Finance Survey (F-33) and the state-level…
Descriptors: Educational Finance, Data Collection, Feasibility Studies, Elementary Secondary Education
Kulkarni, Tara; Weeks, Mollie R.; Sullivan, Amanda L. – Communique, 2020
As frequent consumers and disseminators of research, school psychologists have an ethical obligation to critically evaluate the findings of studies (National Association of School Psychologists, 2010); however, this can feel burdensome when studies are behind paywalls and require hours to properly scrutinize. Particularly when studies utilizing…
Descriptors: Data Analysis, School Psychology, Criticism, Psychological Studies
Pazzaglia, Angela M.; Stafford, Erin T.; Rodriguez, Sheila M. – Regional Educational Laboratory Northeast & Islands, 2016
This guide describes a five-step collaborative process that educators can use with other educators, researchers, and content experts to write or adapt questions and develop surveys for education contexts. This process allows educators to leverage the expertise of individuals within and outside of their organization to ensure a high-quality survey…
Descriptors: Surveys, Data Analysis, Response Rates (Questionnaires), Statistics
Mathematical Tools for Real-World Applications: A Gentle Introduction for Students and Practitioners
Draganov, Alexandr – MIT Press, 2022
Techniques for applying mathematical concepts in the real world: six rarely taught but crucial tools for analysis, research, and problem-solving. Many young graduates leave school with a solid knowledge of mathematical concepts but struggle to apply these concepts in practice. Real scientific and engineering problems are different from those found…
Descriptors: Mathematical Concepts, Relevance (Education), College Mathematics, Engineering
Bruhn, Allison L.; McDaniel, Sara C.; Rila, Ashley; Estrapala, Sara – Beyond Behavior, 2018
Students who are at risk for or show low-intensity behavioral problems may need targeted, Tier 2 interventions. Often, Tier 2 problem-solving teams are charged with monitoring student responsiveness to intervention. This process may be difficult for those who are not trained in data collection and analysis procedures. To aid practitioners in these…
Descriptors: Progress Monitoring, Behavior Problems, Student Behavior, At Risk Students
Wang, Yinying – TechTrends: Linking Research and Practice to Improve Learning, 2016
Against the backdrop of the ever-increasing influx of big data, this article examines the opportunities and concerns over big data in education. Specifically, this article first introduces big data, followed by delineating the potential opportunities of using big data in education in two areas: learning analytics and educational policy. Then, the…
Descriptors: Educational Research, Data Collection, Data Analysis, Educational Policy