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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
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Shepley, Collin; Grisham-Brown, Jennifer; Lane, Justin D.; Ault, Melinda J. – Topics in Early Childhood Special Education, 2022
Progress-monitoring data collection is an essential skill for teachers serving children for whom the general curriculum is insufficient. As the field of early childhood education moves toward tiered service provision models, the importance of routine data collection is heightened. Therefore, we evaluated the effects of a training package on…
Descriptors: Early Childhood Education, Preschool Teachers, Teacher Behavior, Data Collection
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Silva, Meghan R.; Collier-Meek, Melissa A.; Codding, Robin S.; Kleinert, Whitney L.; Feinberg, Adam – Contemporary School Psychology, 2021
Response-to-intervention (RtI) is a multi-tiered framework designed to prevent academic difficulties by facilitating robust, research-based instruction and providing targeted or individualized short-term interventions for students at-risk per periodic screening and progress monitoring data. The cornerstone of RtI is data-based decision-making to…
Descriptors: Data Collection, Data Analysis, Response to Intervention, Decision Making
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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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
Ben Bryant; Simon Day – UK Department for Education, 2023
This research was commissioned as part of Inclusive Britain, the government response to the Commission on Race and Ethnic Disparities (CRED). The research sought to answer three main questions: (1) Where schools and trusts have closed attainment gaps between pupils from different ethnic groups, has this been the result of a deliberate strategy…
Descriptors: School Role, Achievement Gap, Racial Differences, Ethnicity
Golden, Cindy – Brookes Publishing Company, 2018
Collecting data on behavior, academic skills, and Individualized Education Plan (IEP) goals is an essential step in showing student progress--but it can also be a complicated, time-consuming process. Take the worry and stress out of data collection with this ultra-practical resource, packed with the tools you need to organize, manage, and monitor…
Descriptors: Data Collection, Information Management, Student Records, Student Behavior
Ruedel, Kristin; Nelson, Gena; Bailey, Tessie – National Center for Systemic Improvement at WestEd, 2018
To evaluate interim progress toward the State-identified Measurable Result (SIMR), states require access to high-quality data from local education agencies (LEAs) and early intervention service providers. In a review of 2017 Phase III State Systemic Improvement Plans (SSIP), 43 Part C states noted limitations or concerns related to data and…
Descriptors: Fidelity, Data Collection, State Standards, Barriers
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Reinders, Hayo – JALT CALL Journal, 2018
If only we could know what our students were up to at any given moment in class. Who is paying attention, and who is falling asleep? Who understands the past perfect and who thinks it is about something wonderful that happened yesterday? And wouldn't it be great if we knew who is motivated and who is ready to drop out of the course? Language…
Descriptors: Data Collection, Data Analysis, Language Teachers, Learner Engagement
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Bruhn, Allison L.; McDaniel, Sara C.; Rila, Ashley; Estrapala, Sara – Beyond Behavior, 2018
Students who are at risk for or show low-intensity behavioral problems may need targeted, Tier 2 interventions. Often, Tier 2 problem-solving teams are charged with monitoring student responsiveness to intervention. This process may be difficult for those who are not trained in data collection and analysis procedures. To aid practitioners in these…
Descriptors: Progress Monitoring, Behavior Problems, Student Behavior, At Risk Students
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
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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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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
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
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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