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Quin-Anne Hinrichs; Chelsea R. Johnston; Laura Feuerborn; Ashli Tyre – Beyond Behavior, 2025
Implementation of a culturally responsive positive behavioral interventions and supports (PBIS) framework is associated with positive outcomes for secondary students when implemented schoolwide. Yet, educators often report more implementation challenges in secondary school as compared to elementary school settings. Difficulties obtaining student…
Descriptors: Behavior Modification, Positive Behavior Supports, Student Behavior, Behavior Problems
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Boels, Lonneke – Teaching Statistics: An International Journal for Teachers, 2023
Gaze data are still uncommon in statistics education despite their promise. Gaze data provide teachers and researchers with a new window into complex cognitive processes. This article discusses how gaze data can inform and be used by teachers both for their own teaching practice and with students. With our own eye-tracking research as an example,…
Descriptors: Statistics Education, Eye Movements, Data, Cognitive Processes
Wilhelmina Van Dijk; Cynthia U. Norris; Stephanie Al Otaiba; Christopher Schatschneider; Sara A. Hart – Grantee Submission, 2022
This manuscript provides information on datasets pertaining to Project KIDS. Datasets include behavioral and achievement data for over 4,000 students between five and twelve years old participating in nine randomized control trials of reading instruction and intervention between 2005-2011, and information on home environments of a subset of 442…
Descriptors: Data, Reading Instruction, Intervention, Family Environment
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
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Krumm, Andrew; Everson, Howard T.; Neisler, Julie – Journal of Learning Analytics, 2022
This paper describes a partnership-based approach for analyzing data from a learning management system (LMS) used by students in grades 6-12. The goal of the partnership was to create indicators for the ways in which students navigated digital learning activities, referred to as playlists, that were comprised of resources, pre-assessments, and…
Descriptors: Learning Management Systems, Data Analysis, Electronic Learning, Student Behavior
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Khajonmote, Withamon; Chinsook, Kittipong; Klintawon, Sununta; Sakulthai, Chaiyan; Leamsakul, Wicha; Jansawang, Natchanok; Jantakoon, Thada – Journal of Education and Learning, 2022
The system architecture of big data in massive open online courses (BD-MOOCs System Architecture) is composed of six components. The first component was comprised of big data tools and technologies such as Hadoop, YARN, HDFS, Spark, Hive, Sqoop, and Flume. The second component was educational data science, which is composed of the following four…
Descriptors: MOOCs, Data Collection, Student Behavior, Computer Software
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Rotelli, Daniela; Monreale, Anna – Journal of Learning Analytics, 2023
The increased adoption of online learning environments has resulted in the availability of vast amounts of educational log data, which raises questions that could be answered by a thorough and accurate examination of students' online learning behaviours. Event logs describe something that occurred on a platform and provide multiple dimensions that…
Descriptors: Learning Analytics, Learning Management Systems, Time on Task, Student Behavior
Knudson, Joel – California Collaborative on District Reform, 2020
School closures in response to the COVID-19 pandemic have dramatically changed the conditions in which students learn and experience schooling. Disparities in students' access to learning and in their academic outcomes are likely to exacerbate longstanding challenges and inequities. Now more than ever, educators need information that will help…
Descriptors: Data Use, Educational Improvement, Equal Education, Data Collection
Flannery, K. Brigid; Kato, Mimi McGrath; Horner, Robert H. – Technical Assistance Center on Positive Behavioral Interventions and Supports, 2019
Using data for decision-making is critical for schoolwide leadership teams and has been shown to enhance both social and academic outcomes for students (Faria et al., 2017). Using data effectively, however, requires that teams have a clear vision about the type of data, format of data presentation, and process for using data. To avoid expending…
Descriptors: High School Students, Data Use, Outcomes of Education, Positive Behavior Supports
Tennessee Higher Education Commission, 2024
The 2024 Articulation and Transfer Report provides an update on the progress made toward full articulation between public institutions in Tennessee. This report uses data from National Student Clearinghouse Student Tracker (NSC), the Tennessee Higher Education Commission's Student Information System (THECSIS), and the Tennessee Board of Regents…
Descriptors: Articulation (Education), Higher Education, Data Analysis, Transfer Rates (College)
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Allbright, Taylor; Hough, Heather – State Education Standard, 2020
California's CORE Districts--a consortium of eight school districts serving a racially and socioeconomically diverse population of over one million students--have since 2014 led the way in deploying measures of social and emotional learning (SEL) and school climate and culture. Influenced by surging interest and research support over the past…
Descriptors: Social Development, Emotional Development, Elementary Secondary Education, Holistic Approach
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Robertson, Rachel E.; Coy, Justin N. – TEACHING Exceptional Children, 2019
When student behavior problems persist despite effective classroom management and vary in intensity depending on factors outside of teacher control, teachers are often left feeling discouraged and ineffective (Clunies-Ross, Little, & Kienhuis, 2008). Teachers may know how to handle behavior problems associated with classroom-based antecedents…
Descriptors: Student Behavior, Behavior Problems, Environmental Influences, Intervention
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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
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Danible, Kaitlin; Panizza, Chloe; Boushey, Carol; Kerr, Deborah; Zhu, Fengqing; Banna, Jinan C. – Journal of Extension, 2021
The mobile food record™ (mFR™) is a novel app that allows for the tracking of individual food waste and addresses the limitations of current methods to accurately quantify food waste. Extension educators may use data from the mFR™ to create educational initiatives for food waste prevention and education that may be implemented in settings such as…
Descriptors: Food, Measurement, Computer Oriented Programs, Extension Education
Lee Collyer – Washington Office of Superintendent of Public Instruction, 2022
The 2022 Legislature directed the Office of Superintendent of Public Instruction (OSPI) to create an advisory workgroup with specific participants to report back on topics related to student isolation and seclusion. This legislative report provides a background on the workgroup, history of isolation and restraint, current practices in the state of…
Descriptors: Discipline, Punishment, Behavior Modification, Timeout
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