NotesFAQContact Us
Collection
Advanced
Search Tips
What Works Clearinghouse Rating
Showing 1 to 15 of 54 results Save | Export
Digital Promise, 2021
The Powerful Learning with Computational Thinking report explains how the Digital Promise team works with districts, schools, and teachers to make computational thinking ideas more concrete to practitioners for teaching, design, and assessment. We describe three powerful ways of using computers that integrate well with academic subject matter and…
Descriptors: Computation, Thinking Skills, Computer Uses in Education, Data Collection
Chad J. Coleman – ProQuest LLC, 2021
Determining which students are at-risk of poorer outcomes -- such as dropping out, failing classes, or decreasing standardized examination scores -- has become an important area of both research and practice in K-12 education. The models produced from this type of predictive modeling research are increasingly used by high schools in Early Warning…
Descriptors: Artificial Intelligence, Educational Technology, Technology Uses in Education, Elementary Secondary Education
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
Peer reviewed Peer reviewed
Direct linkDirect link
Rwitajit Majumdar; Huiyong Li; Yuanyuan Yang; Hiroaki Ogata – Educational Technology & Society, 2024
Self-direction skill (SDS) is an essential 21st-century skill that can help learners be independent and organized in their quest for knowledge acquisition. While some studies considered learners from higher education levels as the target audience, providing opportunities to start the SDS practice by K12 learners is still rare. Further, practicing…
Descriptors: 21st Century Skills, Skill Development, Electronic Learning, Physical Activity Level
Peer reviewed Peer reviewed
PDF on ERIC Download full text
Faubert, Brenton Cyriel; Le, Anh Thi Hoai; Wakim, Georges; Swapp, Donna – International Journal of Education Policy and Leadership, 2019
This article reports on a rigorous approach developed for calibrating the Evidence-Based Adequacy Model to suit the Ontario K-12 public education context, and the actual calibrations made. The four-step calibration methodology draws from expert consultations and a review of the academic literature. Specific attention is given to the technical…
Descriptors: Elementary Secondary Education, Public Education, Models, Foreign Countries
Peer reviewed Peer reviewed
Direct linkDirect link
Ukpokodu, Omiunota N. – Urban Review: Issues and Ideas in Public Education, 2018
African immigrants in the U.S. have been headlined as America's "new model minority." The purpose of this paper is to examine if evidence exists to support the claim of African immigrant students' (AIS) educational achievement and excellence (a core indicator of the "model minority" theory) in U.S. k-12 schools. Using a…
Descriptors: Immigrants, Blacks, Models, Minority Group Students
Peer reviewed Peer reviewed
PDF on ERIC Download full text
Coleman, Chad; Baker, Ryan S.; Stephenson, Shonte – International Educational Data Mining Society, 2019
Determining which students are at risk of poorer outcomes -- such as dropping out, failing classes, or decreasing standardized examination scores -- has become an important area of research and practice in both K-12 and higher education. The detectors produced from this type of predictive modeling research are increasingly used in early warning…
Descriptors: Prediction, At Risk Students, Predictor Variables, Elementary Secondary Education
Peer reviewed Peer reviewed
Direct linkDirect link
Traub, Michele R.; Joslyn, P. Raymond; Kronfli, Faris R.; Peters, Kerri P.; Vollmer, Timothy R. – Rural Special Education Quarterly, 2017
The demand for behavioral services is currently outpacing the availability of trained and certified behavior analysts. This need might be felt most acutely in rural communities, where educational funds often do not allow for hiring full-time behavioral staff. This article outlines a model for behavioral consultation that has been successful in…
Descriptors: Models, Consultation Programs, School Districts, Rural Areas
Peer reviewed Peer reviewed
Direct linkDirect link
Piety, Philip J. – Review of Research in Education, 2019
This chapter reviews actionable data use--both as an umbrella term and as a specific concept--developed in three different traditions that data/information can inform and guide P-20 educational practice toward better outcomes. The literatures reviewed are known as data-driven decision making (DDDM), education data mining (EDM), and learning…
Descriptors: Educational Practices, Data Use, Outcomes of Education, Learning Analytics
Peer reviewed Peer reviewed
Direct linkDirect link
Selekman, Janice; Wolfe, Linda C.; Cole, Marjorie – Journal of School Nursing, 2016
School nurses collect data to report to their school district and state agencies. However, there is no national requirement or standard to collect specific data, and each state determines its own set of questions. This study resulted from a joint resolution between the National Association of State School Nurse Consultants and the National…
Descriptors: School Nurses, Data Collection, School Health Services, Questionnaires
Marker, Kathryn Christner – ProQuest LLC, 2016
Because data access may be perceived by principals as overwhelming or irrelevant rather than helpful (Wayman, Spikes, & Volonnino, 2013), data access does not guarantee effective data use. The data-based decision making literature has largely focused on teacher use of data, considering less often data-based organizational improvements for the…
Descriptors: Principals, Data Collection, Evaluation Methods, Decision Making
Atkinson, Linton – ProQuest LLC, 2015
This paper is a research dissertation based on a qualitative case study conducted on Teachers' Experiences within a Data-Driven Decision Making (DDDM) process. The study site was a Title I elementary school in a large school district in Central Florida. Background information is given in relation to the need for research that was conducted on the…
Descriptors: Teaching Experience, Data, Decision Making, Reading Achievement
Peer reviewed Peer reviewed
Direct linkDirect link
Crawford, Lindy – Preventing School Failure, 2014
This article discusses the role of assessment in a response-to-intervention model. Although assessment represents only 1 component in a response-to-intervention model, a well-articulated assessment system is critical in providing teachers with reliable data that are easily interpreted and used to make instructional decisions. Three components of…
Descriptors: Intervention, Models, Response to Intervention, Student Evaluation
Peer reviewed Peer reviewed
Direct linkDirect link
Perrotta, Carlo – Technology, Pedagogy and Education, 2014
This paper uses methods derived from the field of futures studies to explore the future of technology-enhanced assessment. Drawing on interviews and consultation activities with experts, the paper aims to discuss the conditions that can impede or foster "innovation" in assessment and education more broadly. Through a review of relevant…
Descriptors: Foreign Countries, Educational Assessment, Educational Technology, Innovation
Slater, Liz – CfBT Education Trust, 2013
Monitoring, evaluation, and quality assurance in their various forms are seen as being one of the foundation stones of high-quality education systems. De Grauwe, writing about "school supervision" in four African countries in 2001, linked the decline in the quality of basic education to the cut in resources for supervision and support.…
Descriptors: Educational Improvement, Accountability, Quality Assurance, Educational Quality
Previous Page | Next Page »
Pages: 1  |  2  |  3  |  4