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Ruedel, Kristin; Nelson, Gena; Bailey, Tessie – National Center for Systemic Improvement at WestEd, 2018
The Tennessee Department of Education (TDOE) discovered that infants and toddlers who were referred for Part C services and initially found ineligible for a full evaluation through screening were being re-referred and later found eligible for services at a high rate. This report presents the systematic evaluation process that TDOE's Tennessee…
Descriptors: Educational Legislation, Federal Legislation, Equal Education, Disabilities
Center on Standards and Assessments Implementation, 2018
The recommendations in this brief create a framework for using data effectively to make instructional decisions. The availability of student-level data for educators has pushed forward the movement to strengthen the role of data to guide instruction and improve student learning. While improvements in technology and assessments, as well as recent…
Descriptors: Student Evaluation, Information Utilization, Data Collection, Data Analysis
Giacumo, Lisa A.; Breman, Jeroen – Quarterly Review of Distance Education, 2016
This article provides a systematic literature review about nonprofit and for-profit organizations using "big data" to inform performance improvement initiatives. The review of literature resulted in 4 peer-reviewed articles and an additional 33 studies covering the topic for these contexts. The review found that big data and analytics…
Descriptors: Workplace Learning, Literature Reviews, Job Performance, Job Simplification
Jimerson, Jo Beth; Reames, Ellen – Journal of Educational Change, 2015
In this conceptual paper, we map the research terrain on what we term "student-involved data use" (SIDU)--that is, the practice of having students track, chart, and analyze their own data in formal and structured ways. Drawing on peer-reviewed research as well as practitioner-oriented literature, social media, and district websites, we…
Descriptors: Evidence, Data Analysis, Student Projects, Student Records
Seager, Andrew; Madura, John P.; Cox, Joshua; Carey, Rebecca – Regional Educational Laboratory Northeast & Islands, 2015
This descriptive study of a change initiative by the Syracuse (NY) City School District informs the question, "How do school boards and districts better use research and data to inform policy decisions?" Researchers used interviews and artifacts to describe how the district developed and implemented a new discipline policy, the Syracuse…
Descriptors: School Districts, Boards of Education, Data Analysis, Information Utilization
Algozzine, Bob; Horner, Robert H.; Todd, Anne W.; Newton, J. Stephen; Algozzine, Kate; Cusumano, Dale – Journal of Psychoeducational Assessment, 2016
Although there is a strong legislative base and perceived efficacy for multidisciplinary team decision making, limited evidence supports its effectiveness or consistency of implementation in practice. In recent research, we used the Decision Observation, Recording, and Analysis (DORA) tool to document activities and adult behaviors during positive…
Descriptors: Problem Solving, Participative Decision Making, Positive Behavior Supports, Meetings
Cresswell, John; Schwantner, Ursula; Waters, Charlotte – OECD Publishing, 2015
This report reviews the major international and regional large-scale educational assessments, including international surveys, school-based surveys and household-based surveys. The report compares and contrasts the cognitive and contextual data collection instruments and implementation methods used by the different assessments in order to identify…
Descriptors: International Assessment, Educational Assessment, Data Collection, Comparative Analysis
Corsello, Maryann; Sharma, Anu; Jerabek, Angela – Grantee Submission, 2015
Building Assets Reducing Risks (BARR) is a social emotional model that achieves academic outcomes through combining use of real-time student data with proven relationship-building strategies and intensive teacher collaboration to prevent course failure. BARR is a recipient of US Department of Education "Investing in Innovation (i3)"…
Descriptors: Teacher Collaboration, Student Improvement, Improvement Programs, Information Utilization
Boudett, Kathryn Parker, Ed.; City, Elizabeth A., Ed.; Murnane, Richard J., Ed. – Harvard Education Press, 2013
"Data Wise: A Step-by-Step Guide to Using Assessment Results to Improve Teaching and Learning" presents a clear and carefully tested blueprint for school leaders. It shows how examining test scores and other classroom data can become a catalyst for important schoolwide conversations that will enhance schools' abilities to capture…
Descriptors: Guides, Data Collection, Data Analysis, Information Utilization
Jimenez, Bree A.; Mims, Pamela J.; Browder, Diane M. – Education and Training in Autism and Developmental Disabilities, 2012
Effective practices in student data collection and implementation of data-based instructional decisions are needed for all educators, but are especially important when students have severe intellectual and developmental disabilities. Although research in the area of data-based instructional decisions for students with severe disabilities shows…
Descriptors: Guidelines, Developmental Disabilities, Mental Retardation, Severe Mental Retardation
Ma, Xuan; And Others – 1995
This guide presents an introduction to the basics about data collection and discusses the uses of data in daily work. The emphasis is on youth programs in Indiana, but the information is useful for programs in other states. Ways in which program planners and advocacy activists can use data are described, and local, state, and national resources…
Descriptors: Advocacy, Data Analysis, Data Collection, Databases
Stevens, David W.; And Others – 1992
This report presents findings of a demonstration project that tested the concepts and applications of linking student records using social security numbers with state unemployment insurance (UI) wage and other data sources. It also contains information of value to policymakers, state and local administrators, practitioners, and consumers of…
Descriptors: Data Analysis, Data Collection, Data Interpretation, Data Processing

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