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Titlebook: Making the Moment Matter; Care Theory for Teac Muffet Trout Book 2012 SensePublishers 2012 ethics.teacher education.care

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发表于 2025-3-21 17:55:05 | 显示全部楼层 |阅读模式
书目名称Making the Moment Matter
副标题Care Theory for Teac
编辑Muffet Trout
视频video
概述Written by experts, Gives a modern approach, Comprehensive in Scope
丛书名称Transgressions
图书封面Titlebook: Making the Moment Matter; Care Theory for Teac Muffet Trout Book 2012 SensePublishers 2012 ethics.teacher education.care
描述“Making the Moment Matter is a wonderful contribution to the literature on meaningful teacher education. Grounded in Nel Noddings’ relational ethic of care, this finely written book explores both the moral foundations and the lived realities of facilitating teacher development through pedagogical caring. Trout’s theoretically rich, carefully designed, and engaging inquiry could not be more timely. In an era when reform talk in teaching and teacher education is increasingly characterized by reference to outcomes, accountability and “value added” measures, this book reminds us that better teaching is a process centered on caring relationships. Her work has a great deal to offer many different readers—educational researchers interested in models of well-crafted studies of practice, teacher educators looking for insights into the complex work of teacher development, and others who wish to learn more about the manner of relationships that stand at the heart of education.”
出版日期Book 2012
关键词ethics; teacher education; care
版次1
doihttps://doi.org/10.1007/978-94-6209-110-8
isbn_ebook978-94-6209-110-8Series ISSN 2214-9732 Series E-ISSN 2214-9740
issn_series 2214-9732
copyrightSensePublishers 2012
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Muffet Trout by any suitable algorithm, such as kernel .-means. We show empirically that kernels found by our algorithm yield clusterings of higher quality than existing approaches that either use ML/CL constraints or a different means to implement the supervision using relative comparisons.
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Muffet Trout ensemble-based global model is guaranteed to be at least as good as the average performance of local models. However, a big challenge lies in the explosion of model size since each round of ensemble can give rise to multiple times size increment. Thus, we carry out model compression after each ense
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Muffet Troutng the learning process, which is based on the neighborhood structure in the original space. Experimental results on standard datasets (CIFAR-10, NUS-WIDE, and Oxford-17) demonstrate that our DDH significantly outperforms existing hashing methods by large margin in terms of mAP for image retrieval a
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Muffet Troutrchitecture. Our proposed method finds within 12 GPU hours neural network architectures that can achieve a classification error of about 4% and 24% with only 5.5 and 6.5 million parameters on CIFAR-10 and CIFAR-100, respectively. In comparison to competitor approaches, our method provides similar co
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