NotesFAQContact Us
Collection
Advanced
Search Tips
Audience
Teachers1
What Works Clearinghouse Rating
Showing 1 to 15 of 22 results Save | Export
Peer reviewed Peer reviewed
Direct linkDirect link
Soyoung Park; Pamela M. Stecker; Sarah R. Powell – Intervention in School and Clinic, 2024
This article provides teachers with a toolkit for assessing students in the context of data-based individualization (DBI) in mathematics. Assessing students is a critical component of DBI because it provides teachers with information about what they may need to modify in their instructional programs. In this article, we provide teachers with…
Descriptors: Student Evaluation, Individualized Instruction, Mathematics Instruction, Progress Monitoring
Peer reviewed Peer reviewed
Direct linkDirect link
Duncan Culbreth; Rebekah Davis; Cigdem Meral; Florence Martin; Weichao Wang; Sejal Foxx – TechTrends: Linking Research and Practice to Improve Learning, 2025
Monitoring applications (MAs) use digital and online tools to collect and track data on student behavior, and they have become increasingly popular among schools. Empirical research on these complex surveillance platforms is scant, and little is known about the efficacy or impact that they have on students. This study used a multi-method…
Descriptors: High School Students, COVID-19, Pandemics, Progress Monitoring
Peer reviewed Peer reviewed
Direct linkDirect link
Ian Hardy – Professional Development in Education, 2024
Schooling in Australia has become subject to increased processes of data-based governance. This article draws upon the insights of an experienced teacher, 'Meriam', who, having taught more than 34-years over almost a 50-year span, reflected upon the nature of such changes. Utilising theorising in relation to datafication processes and…
Descriptors: Foreign Countries, Experienced Teachers, Teacher Attitudes, Educational Change
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
Peer reviewed Peer reviewed
Direct linkDirect link
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
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
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
Peer reviewed Peer reviewed
Direct linkDirect link
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
Peer reviewed Peer reviewed
Direct linkDirect link
Verlenden, Jorge; Naser, Shereen; Brown, Jeffrey – Journal of Applied School Psychology, 2021
Behavioral and social-emotional challenges experienced in childhood are risk factors for negative educational and health outcomes. Universal social-emotional screening in schools has been identified as an effective approach to identifying children at risk for mental health and behavioral challenges and is congruent with tiered frameworks for…
Descriptors: Screening Tests, Behavior Problems, Emotional Problems, At Risk Persons
Peer reviewed Peer reviewed
Direct linkDirect link
Hartong, Sigrid – Critical Studies in Education, 2021
This contribution takes a critical perspective on digital school performance platforms (SPP), which today play a key role in US state education monitoring and accountability. Using examples from two different US state education agencies, I provide an analytical disentanglement of some key dimensions of such platforms' enactment and materiality. I…
Descriptors: State Departments of Education, Technology Uses in Education, Performance, Accountability
Diana Louise Nestico-Arnold – ProQuest LLC, 2023
Elementary school teachers struggle to collect and use reading progress data to make instructional decisions. Collecting and interpreting reading progress data when making instructional decisions allows districts to identify and support students who need early reading intervention and may protect districts from Free Appropriate Public Education…
Descriptors: Progress Monitoring, Reading Achievement, Data Use, Decision Making
Conley, Kathleen M.; Horner, Robert H.; McIntosh, Kent – Technical Assistance Center on Positive Behavioral Interventions and Supports, 2019
A core feature of Positive Behavioral Interventions and Supports (PBIS) is the collection, summary, and use of data for iterative decision-making. The initial design of support and the adaptations that make behavior support match cultural, organizational, and personal needs require that a support team have functional information to guide…
Descriptors: Positive Behavior Supports, Elementary School Students, Decision Making, Progress Monitoring
Ruedel, Kristin; Nelson, Gena; Bailey, Tessie – National Center for Systemic Improvement at WestEd, 2017
Many states struggle with collecting reliable and valid data that can be easily analyzed at the state level. This can be particularly challenging in states where schools and districts have local control over the selection of their assessment tools. This state spotlight presents the strategies that the Iowa Department of Education (IDE) used to…
Descriptors: Data Use, Data Collection, State Policy, Educational Policy
Center on Positive Behavioral Interventions and Supports, 2022
This practice guide is an updated version of "Supporting and Responding to Behavior: Evidence-based Classroom Strategies for Teachers" (see ED619696) that replaces, rather than supplements, the first version. This guide summarizes evidence-based, positive, and proactive practices that support and respond to students' social, emotional,…
Descriptors: Evidence Based Practice, Student Behavior, Intervention, Classroom Techniques
Ruedel, Kristin; Nelson, Gena; Bailey, Tessie – National Center for Systemic Improvement at WestEd, 2017
State tests lack the sensitivity and frequency to reflect ongoing academic improvement of students who are well below grade-level proficiency standards. As a result, some states are using screening and progress-monitoring data, collected as part of a multi-tiered system of supports (MTSS) model, to evaluate early student-level progress toward SiMR…
Descriptors: Data Use, Student Evaluation, Screening Tests, Progress Monitoring
Previous Page | Next Page »
Pages: 1  |  2