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Cited by 717

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Learning to improve: how America's schools can get better at getting better

·2015·Choice Reviews Online

Abstract

As a field, education has largely failed to learn from experience. Time after time, promising education reforms fall short of their goals and are abandoned as other promising ideas take their place. In Learning to Improve , the authors argue for a new approach. Rather than oimplementing fast and learning slow,o they believe educators should adopt a more rigorous approach to improvement that allows the field to olearn fast to implement well.o Using ideas borrowed from improvement science, the authors show how a process of disciplined inquiry can be combined with the use of networks to identify, adapt, and successfully scale up promising interventions in education. Organized around six core principles, the book shows how onetworked improvement communitieso can bring together researchers and practitioners to accelerate learning in key areas of education. Examples include efforts to address the high rate of failure among students in community college remedial math courses and strategies forimproving feedback to novice teachers. Learning to Improve offers a new paradigm for research and development in education that promises to be a powerful driver of improvement for the nationAEs schools and colleges.

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Metadata

Venue
Choice Reviews Online
Published
2015-08-18
Volume
53
Issue
01
Pages
53-0354-53-0354
ISSN
  • 0009-4978 (ISSN-L)
  • 1523-8253
OpenAlex
W2468472082
Cited by
717
References
0
Type
book review
Language
en
Open access
closed

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CoChat. (2026). Citation check: Learning to improve: how America's schools can get better at getting better. https://app.cochat.ai/papers/10.5860/choice.191214
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Learning to improve: how America's schools can get better at getting better. (2015). Choice Reviews Online. https://doi.org/10.5860/choice.191214

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.