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Chad J. Coleman – ProQuest LLC, 2021
Determining which students are at-risk of poorer outcomes -- such as dropping out, failing classes, or decreasing standardized examination scores -- has become an important area of both research and practice in K-12 education. The models produced from this type of predictive modeling research are increasingly used by high schools in Early Warning…
Descriptors: Artificial Intelligence, Educational Technology, Technology Uses in Education, Elementary Secondary Education
Nancy Montes; Fernanda Luna – UNESCO International Institute for Educational Planning, 2024
This article characterizes and reflects on the possible uses of early warning systems (hereafter, EWS) in the region as effective tools to support educational pathways, whenever they identify risks of dropout, difficulties for the achievement of substantive learning, and the possibility of organizing specific actions. This article was developed in…
Descriptors: Data Collection, Data Use, At Risk Students, Foreign Countries
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Allen, Ray; Ferkel, Rick; Fisher, Kevin; Wawersik, Andrew – Journal of Physical Education, Recreation & Dance, 2022
The purpose of this paper is to present an assessment system that enables physical education programs to collect data capable of meeting their assessment. Assessment at the elementary level is a daunting task. Practitioners are charged to teach multiple objectives across all learning domains to hundreds of students in multiple grades in a limited…
Descriptors: Physical Education, Educational Assessment, Data Collection, Elementary School Teachers
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Gray, Cameron C.; Perkins, Dave; Ritsos, Panagiotis D. – Assessment & Evaluation in Higher Education, 2020
The field of learning analytics is progressing at a rapid rate. New tools, with ever-increasing number of features and a plethora of datasets that are increasingly utilized demonstrate the evolution and multifaceted nature of the field. In particular, the depth and scope of insight that can be gleaned from analysing related datasets can have a…
Descriptors: Educational Research, Data Collection, Data Analysis, Visual Aids
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Ethan R. Van Norman; Emily R. Forcht – Journal of Education for Students Placed at Risk, 2024
This study evaluated the forecasting accuracy of trend estimation methods applied to time-series data from computer adaptive tests (CATs). Data were collected roughly once a month over the course of a school year. We evaluated the forecasting accuracy of two regression-based growth estimation methods (ordinary least squares and Theil-Sen). The…
Descriptors: Data Collection, Predictive Measurement, Predictive Validity, Predictor Variables
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Flanagan, Matthew F.; Kutscher, Elisabeth L. – TEACHING Exceptional Children, 2021
Community-based instruction (CBI) is one type of community experience in which students with disabilities work toward instructional goals while engaged in activities occurring in a natural environment outside of a typical school setting (Hoover, 2016; Rowe et al., 2015). Educators who implement CBI capitalize on their students' time in the…
Descriptors: Community Based Instruction (Disabilities), Progress Monitoring, Students with Disabilities, High School Students
Advance CTE: State Leaders Connecting Learning to Work, 2021
With the recent reauthorization of the Strengthening Career and Technical Education for the 21st Century Act (Perkins V), unprecedented philanthropic investment in career pathways, and the urgent economic needs of the COVID-19 (coronavirus) pandemic, the career readiness field is at a critical moment in time. To meet this moment, states,…
Descriptors: Career Readiness, Career Development, Educational Legislation, Federal Legislation
Juan D’Brot; W. Chris Brandt – Region 5 Comprehensive Center, 2024
In today's educational landscape, state and local educational agencies (SEAs and LEAs) often experience challenges connecting large-scale accountability data with actual school improvement initiatives. These challenges tend to be rooted in incoherent design and use of data systems for continuous improvement. As we aim to support SEAs in…
Descriptors: Educational Improvement, Data Collection, State Departments of Education, School Districts
Marx, Teri; Peterson, Amy; Arden, Sarah – National Center on Intensive Intervention, 2020
During spring 2020, educators quickly adapted to providing interventions and collecting data virtually despite the challenges of the COVID-19 pandemic. Parents were critical partners in supporting opportunities for students with intensive needs to data-based individualization (DBI) Process practice and receive feedback and sharing what was working…
Descriptors: COVID-19, Pandemics, Individualized Instruction, Data Use
Moore, Colleen; Bracco, Kathy Reeves; Nodine, Thad; Esch, Camille; Grubb, Brock – Education Insights Center, 2019
California does not have a statewide data system that tracks student progress through K-12 and higher education and into the workforce. As a result, educators and policymakers cannot answer critical questions about student progress, which limits their ability to make evidence-based changes to support better and more equitable opportunities for…
Descriptors: Progress Monitoring, Data Collection, Elementary Secondary Education, Higher Education
Jonathan L. Loveall – ProQuest LLC, 2022
In the 1995 legislation establishing charter schools in the state of Louisiana, the Louisiana Legislature indicated their intention "that the best interests of at-risk pupils be the overriding consideration in implementing the provisions of this Chapter" (Charter School Demonstration Programs, 1997). Furthermore, the legislation required…
Descriptors: Students with Disabilities, Charter Schools, State Legislation, Progress Monitoring
Education Commission of the States, 2020
The population of English learners (ELs) in K-12 schools continues to grow. Between the 2009-10 and 2014-15 school years, the percentage of English learners increased in over half of the states, and in 2017, English learners made up 10.1% of the total student population. Research suggests that in their transition to English, non-native speakers…
Descriptors: Elementary School Students, English Language Learners, English (Second Language), Second Language Learning
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Kishida, Yuriko; Carter, Mark; Kemp, Coral – Australasian Journal of Special and Inclusive Education, 2021
Although the use of data is important for informing inclusive practice, research into Australian early childhood educators' data practice is limited. Types of data collected in early childhood settings and the use of these data were investigated. Surveys completed by 105 early childhood educators across Australia indicated that anecdotal written…
Descriptors: Data Use, Data Collection, Early Childhood Teachers, Early Childhood Education
Zarate, Kary – ProQuest LLC, 2021
When a paraeducator accompanies students receiving special education support across settings, students are at increased risks of decreased engagement and interaction with certified teachers (Giangreco et al., 2001). When paraeducators are tasked with implementing, it is critical they be prepared to monitor students' progress accurately and…
Descriptors: Accuracy, Generalizability Theory, Data Collection, Paraprofessional School Personnel
Office of Inspector General, US Department of Education, 2023
The objective of this inspection was to determine what steps the Office of Special Education and Rehabilitative Services (OSERS) has taken to implement its final regulations on significant disproportionality in special education. The inspection found that OSERS provided general guidance and technical assistance for State educational agencies…
Descriptors: Students with Disabilities, Federal Legislation, Equal Education, Educational Legislation
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