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Hardy, Lisa; Dixon, Colin; Van Doren, Seth; Hsi, Sherry – Science Teacher, 2022
In science classrooms, students usually see and work with data that's intended to tell them right away about the natural world. Students then often treat the data we provide to them as factual, rather than as a source of evidence (Duschl 2008; Sandoval and Millwood 2005; Berland and Reiser 2009; McNeill and Berland 2017; Hancock, Kaput, and…
Descriptors: Data Collection, Data Analysis, Science Experiments, High School Students
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Lee, Victor R.; Delaney, Victoria – Journal of Science Education and Technology, 2022
As data become more available and integrated into daily life, there has been growing interest in developing data science curricula for youth in conjunction with scientific practices and classroom technologies. However, the "what" and "how" of data science in pre-collegiate education have not yet reached consensus. This paper…
Descriptors: Data, Data Analysis, Curriculum Development, Educational Practices
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Shen, Jian; Luo, Qiang – Best Evidence in Chinese Education, 2022
The development of school education depends on the quality of the education provided, and it is a key metric for assessing the effectiveness of schools in developing talent. Building specialized, intelligent education quality monitoring (EQM) databases is crucial for speeding EQM progress in the big data era. This article examines the development…
Descriptors: Educational Quality, Quality Assurance, Databases, Foreign Countries
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
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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
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Ann Marie Cotman; F. Chris Curran; Katharine Harris-Walls – Education Policy Analysis Archives, 2024
Choices made on data visualizations guide how users make meaning of the information presented. This research investigates design decisions made on 115 state-level dashboards reporting school safety data. Using pre-determined codes drawn from a framework of visualization rhetoric, dashboard characteristics were described and analyzed. Analysis…
Descriptors: School Safety, Data, Visual Aids, State Agencies
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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
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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
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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
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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
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
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