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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
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
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Pistilli, Matthew D. – New Directions for Higher Education, 2017
The implementation of analytics in support of student success requires effective use of feedback and interventions, as well as a system by which the use of feedback and institutional supports can be tracked and evaluated.
Descriptors: Educational Research, Data Analysis, Academic Achievement, Intervention
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Ashenafi, Michael Mogessie; Ronchetti, Marco; Riccardi, Giuseppe – International Educational Data Mining Society, 2016
Predicting overall student performance and monitoring progress have attracted more attention in the past five years than before. Demographic data, high school grades and test result constitute much of the data used for building prediction models. This study demonstrates how data from a peer-assessment environment can be used to build student…
Descriptors: Peer Evaluation, Progress Monitoring, Performance, Undergraduate Students
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
Tomlinson, Carol Ann – Educational Leadership, 2015
This article describes a language arts teacher's two-tiered way of using assessment data, which uses both standardized assessment data and teacher-created "skills" inventories. Standardized test data may help see where a learner performed in a previous year on items that often do not line up too well with future content. Standardized…
Descriptors: Standardized Tests, Skill Analysis, Data Analysis, Progress Monitoring
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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
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
Data Quality Campaign, 2014
Regular attendance is essential to succeeding in school, and chronic absence--missing excessive amounts of school for any reason--can cause students to be off track academically. Developed in partnership with Attendance Works, this fact sheet analyzes data from the "Data for Action 2013" survey to discuss how states use data to monitor…
Descriptors: Attendance, Success, Academic Achievement, State Action
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Gallagher, Kerry – State Education Standard, 2016
Digital tools are making it easier than ever for teachers to gather and analyze formative data. Paper exit slips can take a classroom teacher upward of an hour to sort and graph after just one day of classes. But now, that same teacher can pose a question out loud to the class and ask students to type answers on their mobile phones and hit send.…
Descriptors: Data Collection, Formative Evaluation, Technology Uses in Education, Handheld Devices
Swail, Watson Scott; Fung-Angarita, Maly – Educational Policy Institute, 2018
The issue of student retention and graduation from postsecondary institutions has grown in stature over the past decade. While the last 40 years of federal and state policies have focused largely on access to college, there is now a very real interest in not only getting students into college but also helping them earn baccalaureate and other…
Descriptors: Data Collection, Postsecondary Education, College Students, School Holding Power
Fergus, Edward – Corwin, 2017
According to federal data, African American students are more than three times as likely as their white peers to be suspended or expelled. As a school leader, what do you do when your heart is in the right place, but your data show otherwise? In "Solving Disproportionality and Achieving Equity", Edward Fergus takes us on a journey into…
Descriptors: Disproportionate Representation, Equal Education, Data Analysis, Research Utilization
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