Publication Date
| In 2026 | 0 |
| Since 2025 | 642 |
| Since 2022 (last 5 years) | 4081 |
| Since 2017 (last 10 years) | 10338 |
| Since 2007 (last 20 years) | 25816 |
Descriptor
Source
Author
Publication Type
Education Level
Audience
| Practitioners | 2921 |
| Researchers | 1809 |
| Teachers | 1760 |
| Policymakers | 1611 |
| Administrators | 1189 |
| Students | 321 |
| Media Staff | 307 |
| Community | 264 |
| Parents | 114 |
| Counselors | 79 |
| Support Staff | 36 |
| More ▼ | |
Location
| United States | 1496 |
| Canada | 1404 |
| Australia | 1402 |
| California | 1310 |
| Texas | 916 |
| New York | 819 |
| United Kingdom | 782 |
| Florida | 715 |
| Illinois | 618 |
| North Carolina | 556 |
| Pennsylvania | 528 |
| More ▼ | |
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
| Meets WWC Standards without Reservations | 13 |
| Meets WWC Standards with or without Reservations | 21 |
| Does not meet standards | 24 |
Region 8 Comprehensive Center, 2022
Most school and district leaders have a wealth of information available to them before, during, and after the hiring process, but they might not analyze it regularly or use it to inform their recruitment and retention plans. However, using data strategically is key to positively impacting teacher recruitment and retention. This brief discusses…
Descriptors: Data Use, Teacher Recruitment, Teacher Persistence, Elementary Secondary Education
Keeanna Jessica Marie Warren – ProQuest LLC, 2022
Teacher turnover continues to be a significant problem in the United States. Teacher turnover is expensive because it costs money to continue recruiting, hiring, and training new teachers to replace those leaving (Carver-Thomas & Darling-Hammond, 2017). Most important though, teacher turnover hurts student achievement and success (Sorensen…
Descriptors: Data Analysis, Prediction, Teacher Persistence, Faculty Mobility
Amelia Parnell – Journal of Postsecondary Student Success, 2022
Data-informed decision-making is no longer an optional or occasional practice, as higher education professionals now routinely respond to calls for accountability by providing data to show how their work impacts students. Institutions are operating with a culture that, at a minimum, includes the use of descriptive and diagnostic analyses to assess…
Descriptors: Student Needs, Data Use, Prediction, Data Analysis
Mary F. Jones; Julie Dallavis – Journal of Educational Administration, 2024
Purpose: Research shows data-informed leadership matters for school improvement and student achievement, but less is known about what motivates leaders' data use toward such outcomes, particularly in the Catholic school context. Design/methodology/approach: This qualitative interview study uses interview (n = 23) data from a sample of Catholic…
Descriptors: Data Use, Educational Improvement, Catholic Schools, Instructional Leadership
Jane Watson; Noleine Fitzallen; Ben Kelly – Mathematics Education Research Journal, 2024
Incorporating an evidence-based approach in STEM education using data collection and analysis strategies when learning about science concepts enhances primary students' discipline knowledge and cognitive development. This paper reports on learning activities that use the nature of viscosity and the power of informal statistical inference to build…
Descriptors: Elementary School Students, Grade 5, STEM Education, Statistics
Yu-Jie Wang; Chang-Lei Gao; Xin-Dong Ye – Education and Information Technologies, 2024
The continuous development of Educational Data Mining (EDM) and Learning Analytics (LA) technologies has provided more effective technical support for accurate early warning and interventions for student academic performance. However, the existing body of research on EDM and LA needs more empirical studies that provide feedback interventions, and…
Descriptors: Precision Teaching, Data Use, Intervention, Educational Improvement
Jelena Mihajlovic-Milicevic; Miloš Radenkovic; Aleksandra Labus; Danijela Stojanovic; Zorica Bogdanovic – Interactive Learning Environments, 2024
This paper studies the problem of coordination and supervision of virtual teams and their capabilities. The goal is to develop a model suitable for managing virtual student teams specialized in the development of smart environments. The developed model is based on SAFe and DevOps, which when combined provide us with a framework for the evaluation…
Descriptors: Educational Environment, Virtual Classrooms, Group Instruction, Active Learning
Katherine A. Shields; Bryan C. Hutchins; Kelly Reese; Edward C. Fletcher; Katherine Hughes – Career and Technical Education Research Network, 2024
In recent years, career and technical education (CTE) programs that include quality work-based learning (WBL) opportunities for students have gained significant traction among educators, policymakers, and stakeholders as an effective way to prepare students for the labor market. However, a lack of data on WBL--which many CTE students participate…
Descriptors: Vocational Education, Experiential Learning, Data Collection, Educational Research
Denise Nadasen – Association of Public and Land-grant Universities, 2024
The Data Culture Framework is a high-level guide designed for institutional leaders who want to create and sustain an effective data culture on campus. The Framework offers a set of practices designed to help institutions of higher education create and maintain an effective data-informed community among institutional leaders, faculty, and staff.
Descriptors: Land Grant Universities, Data Collection, Data Use, College Faculty
Phyllis Young Watson – ProQuest LLC, 2024
Performance-based funding policies for postsecondary institutions have been enacted throughout the United States as an answer to reductions in public funding and increased accountability mandates. Institutional effectiveness, as demonstrated through metrics such as graduation and retention rates, dictates the level of funding received. Educational…
Descriptors: Black Colleges, Graduation Rate, Expenditure per Student, Performance Based Assessment
John Stamper; Steven Moore; Carolyn P. Rosé; Philip I. Pavlik Jr.; Kenneth Koedinger – Journal of Educational Data Mining, 2024
LearnSphere is a web-based data infrastructure designed to transform scientific discovery and innovation in education. It supports learning researchers in addressing a broad range of issues including cognitive, social, and motivational factors in learning, educational content analysis, and educational technology innovation. LearnSphere integrates…
Descriptors: Learning Analytics, Web Sites, Data Use, Educational Technology
Marah Sutherland; David Fainstein; Taylor Lesner; Georgia L. Kimmel; Ben Clarke; Christian T. Doabler – Grantee Submission, 2024
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…
Descriptors: Statistics Education, Mathematics Skills, Skill Development, Data Analysis
Jason Delisle; Jason Cohn – Urban Institute, 2024
Data showing what students earn after attending higher education institutions have become increasingly available, bolstering calls from policymakers and advocates that government financial aid programs should be tied to those outcomes. Often overlooked, however, is that these data and policies usually reflect the earnings of only students who…
Descriptors: College Graduates, College Attendance, Dropouts, Data Collection
Peter K. Dunn – International Journal of Mathematical Education in Science and Technology, 2024
The use of group work projects is common in introductory statistics courses, including projects where students collect their own data. However, the COVID-induced lockdown at the start of 2020 meant that data collection was compromised. In this study, we examine a situation where students were permitted to use artificial (made-up) data for their…
Descriptors: Student Projects, Undergraduate Students, Statistics Education, COVID-19
Claire Abbott; Michael DeArmond; Dan Goldhaber; Hannah Putman – National Center for Analysis of Longitudinal Data in Education Research (CALDER), 2025
This report examines how states can use their Statewide Longitudinal Data Systems (SLDS) to better understand their teacher supply problems and assess initiatives designed to address them. It emphasizes two key actions: connecting existing data and collecting new data on the prospective workforce. Using research and real-world examples from four…
Descriptors: Teacher Supply and Demand, Data Use, Longitudinal Studies, Data Collection

Direct link
Peer reviewed
