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Parnell, Amelia; Jones, Darlena; Wesaw, Alexis; Brooks, D. Christopher – EDUCAUSE, 2018
As higher education institutions in the United States strive to maximize their use of resources to better support students, it is critical for professionals to make data-informed decisions. Most institutions are currently gathering an abundance of data from multiple sources, which provides a good opportunity for functional units, divisions, and…
Descriptors: College Students, Colleges, Data Analysis, Data Collection
Woods, Julie; Rafa, Alyssa – Education Commission of the States, 2018
This special report -- informed by the Education Commission of the States' March 2018 School Improvement Thinkers Meeting -- guides state leaders toward a better understanding of how to improve lower-performing schools. This brief contains questions in four key areas to provide a road map for digging into school improvement systems and orienting…
Descriptors: Educational Improvement, Change Strategies, Educational Objectives, Public Education
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
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Barret, Mandy; Branson, Lisa; Carter, Sheryl; DeLeon, Frank; Ellis, Justin; Gundlach, Cirrus; Lee, Dale – Inquiry, 2019
Artificial intelligence (AI) technology is becoming the basis for business. Most businesses use it to improve the customer experience. The education community is just beginning to find ways to successfully implement AI for staff and students. Artificial Intelligence should be leveraged to create a better student experience. For example, Elon…
Descriptors: Artificial Intelligence, Technology Uses in Education, Higher Education, Educational Opportunities
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Filderman, Marissa J.; Toste, Jessica R. – TEACHING Exceptional Children, 2018
Reading proficiency is fundamental to school success. However, up to 50% of students with reading disabilities are not making adequate progress. Students who demonstrate persistent and severe reading difficulties require increasingly intensive instruction individualized to meet their instructional needs Individualizing instruction with…
Descriptors: Reading Difficulties, Reading Skills, Individualized Instruction, Decision Making
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Hager, Karen D. – TEACHING Exceptional Children, 2018
Delivering high-quality instruction grounded in evidence-based practices is one of the most important responsibilities of special education teachers. However, simply implementing effective evidence-based practices is not enough to ensure positive results; the practices must be implemented with fidelity (Cook & Odom, 2013). Although there are…
Descriptors: Video Technology, Self Evaluation (Individuals), Teacher Evaluation, Educational Technology
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January, Stacy-Ann A.; Van Norman, Ethan R.; Christ, Theodore J.; Ardoin, Scott P.; Eckert, Tanya L.; White, Mary Jane – School Psychology Review, 2018
The present study examined the utility of two progress monitoring assessment schedules (bimonthly and monthly) as alternatives to monitoring once weekly with curriculum-based measurement in reading (CBM-R). General education students (N = 93) in Grades 2-4 who were at risk for reading difficulties but not yet receiving special education services…
Descriptors: Progress Monitoring, Reading Improvement, Reading Tests, Student Evaluation
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Wagner, Dana L.; Hammerschmidt-Snidarich, Stephanie M.; Espin, Christine A.; Seifert, Kathleen; McMaster, Kristen L. – Learning Disabilities Research & Practice, 2017
Teachers must be proficient at using data to evaluate the effects of instructional strategies and interventions, and must be able to make, describe, justify, and validate their data-based instructional decisions to parents, students, and educational colleagues. An important related skill is the ability to accurately read and interpret…
Descriptors: Preservice Teachers, Progress Monitoring, Curriculum Based Assessment, Teacher Competencies
Yoder, N.; Darling-Churchill, K.; Colombi, G. D.; Ruddy, S.; Neiman, S.; Chagnon, E.; Mayo, R. – National Center on Safe Supportive Learning Environments, 2017
This reference manual identifies five overarching sets of activities for improving school climate, with the goal of improving student outcomes (e.g., achievement, attendance, behaviors, and skills). These sets of activities help to initiate, implement, and sustain school climate improvements. For each activity set, the manual presents a clear…
Descriptors: Educational Environment, Educational Improvement, Educational Strategies, Program Implementation
Huebner, Richard A. – ProQuest LLC, 2017
The ubiquity of data in various forms has fueled the need for advanced data-mining techniques within organizations. The advent of data mining methods used to uncover hidden nuggets of information buried within large data sets has also fueled the need for determining how these unique projects can be successful. There are many challenges associated…
Descriptors: Data Analysis, Data Collection, Information Retrieval, Surveys
Moore, Colleen; Bracco, Kathy Reeves; Nodine, Thad – Education Insights Center, 2017
California collects expansive sets of data about students in its public K-12 and higher education systems--data that, collectively, have great potential to meet the information needs of state policymakers, local educators, and other stakeholders. But the data are collected and maintained in systems that are not connected, were designed for…
Descriptors: Student Records, Data Collection, Progress Monitoring, College Students
Dervarics, Chuck; O'Brien, Eileen – Center for Public Education, 2019
In this research brief, the National School Boards Association's Center for Public Education looks at indicators of school board effectiveness. From this research, it is clear that school boards in high-achieving districts exhibit habits and characteristics that are markedly different from boards in low-achieving districts. In the most dramatic…
Descriptors: Public Schools, Boards of Education, High Achievement, Low Achievement
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
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Cumming, Therese M.; O'Neill, Sue C. – Intervention in School and Clinic, 2019
Students receiving behavioral supports in the third tier of the schoolwide positive behavioral interventions and supports (SWPBIS) framework are often identified as having emotional and behavior disabilities. Although educators implement evidence-based practices with fidelity, these practices are not always effective in supporting students with…
Descriptors: Data Use, Behavior Disorders, Emotional Disturbances, Intervention
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Nelson, Peter M.; Van Norman, Ethan R.; Klingbeil, Dave A.; Parker, David C. – Psychology in the Schools, 2017
Although extensive research exists on the use of curriculum-based measures for progress monitoring, little is known about using computer adaptive tests (CATs) for progress-monitoring purposes. The purpose of this study was to evaluate the impact of the frequency of data collection on individual and group growth estimates using a CAT. Data were…
Descriptors: Progress Monitoring, Computer Assisted Testing, Data Collection, Scheduling
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