Using Data To Improve Student Learning
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The Data Coach's Guide to Improving Learning for All Students
Use data as an effective tool for school change and improvement! This resource helps data team facilitators move schools away from unproductive data practices and toward examining data for systematic and continuous improvement in instruction and learning. The book, which includes a CD-ROM with slides and reproducibles, illustrates how the authors' model has proven successful in: Narrowing achievement gaps in all content areas and grade levels Achieving strong, continuous gains in local and state assessments in mathematics, science, and reading Initiating powerful conversations about race/ethnicity, class, educational status, gender, and language differences Developing a vision for a high-performing, data-informed school culture
Using Self-Assessment to Improve Student Learning
Using Self-Assessment to Improve Student Learning synthesizes research on self-assessment and translates it into actionable guidelines and principles for pre-service and in-service teachers and for school leaders, teacher educators, and researchers. Situated beyond the simple how-to frameworks currently available for teachers and graduate students, this volume illuminates self-assessment’s complexities and substantial promise to strategically move students toward self-regulated learning and internalized goals. Addressing theory, empirical evidence, and common implementation issues, the book’s developmental approach to quality self-assessment practices will help teachers, leaders, and scholars maximize their impact on student self-regulation and learning.
Using Data to Improve Student Learning
This book offers a coherent research-based overview and analysis of theories and practices in using data to improve student learning. It clarifies what 'use of data' means and differentiates the different levels of decision-making in education (relating to the system, district, school, classroom, or individual student). The relationship between data and decision-making is considered and various movements in the use of data to improve student learning are analysed, especially from the perspective of their assumptions and effects. This leads to a focus on effective educational decision-making as a social process requiring collaboration among all relevant participants. It also requires a clear understanding of educational aims, and these are seen to transcend what can be assessed by standardised tests. The consequences of this analysis for decision processes are explored and conclusions are drawn about what principles might best guide educational practice as well as what ambiguities remain. Throughout, the focus is on what existing research says about each of the issues explored.