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Titlebook: Lesson Play in Mathematics Education:; A Tool for Research Rina Zazkis,Nathalie Sinclair,Peter Liljedahl Book 2013 Springer Science+Busine

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發(fā)表于 2025-3-21 19:34:01 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Lesson Play in Mathematics Education:
副標題A Tool for Research
編輯Rina Zazkis,Nathalie Sinclair,Peter Liljedahl
視頻videohttp://file.papertrans.cn/586/585222/585222.mp4
概述Introduces and elaborates upon a novel tool, lesson play, in teacher education and professional development.Provides critical analyses of numerous plays created by prospective teachers, offering insig
圖書封面Titlebook: Lesson Play in Mathematics Education:; A Tool for Research  Rina Zazkis,Nathalie Sinclair,Peter Liljedahl Book 2013 Springer Science+Busine
描述Lesson play is a novel construct in research and teachers’ professional development in mathematics education. Lesson play refers to a lesson or part of a lesson presented in dialogue form—inspired in part by Lakatos’s evocative Proofs and Refutations—featuring imagined interactions between a teacher and her/his students. We have been using and refining our use of this tool for a number of years and using it in a variety of situations involving mathematics thinking and learning. The goal of this proposed book is to offer a comprehensive survey of the affordances of the tool, the results of our studies—particularly in the area of pre-service teacher education, and the reasons that the tool offers such productive possibilities for both researchers and teacher educators.
出版日期Book 2013
關(guān)鍵詞Instructional planning tool; Lakatos proofs and refutations; Lesson play; Mathematics education; Pre-ser
版次1
doihttps://doi.org/10.1007/978-1-4614-3549-5
isbn_softcover978-1-4939-0055-8
isbn_ebook978-1-4614-3549-5
copyrightSpringer Science+Business Media New York 2013
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發(fā)表于 2025-3-21 23:39:20 | 只看該作者
port the implementation of some useful scenarios such as content query, knowledge retrieval, and security and privacy use-cases. To provide a better validation of the proposed solution, it will be explored in context of the MindMeister mind mapping tool.
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Rina Zazkis,Nathalie Sinclair,Peter Liljedahld disadvantages. Specifically we look into: 1) variation of VADER, a lexicon based method; 2) a machine learning neural network based method; and 3) a Sentiment Classifier using Word Sense Disambiguation, Maximum Entropy and Naive Bayes Classifiers. The results indicate that there is a significant c
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Rina Zazkis,Nathalie Sinclair,Peter Liljedahlons of individual modes. The users can provide input as text, click or touch, and voice commands to the system to generate corresponding results, offering a comprehensive chats or plots for LD. Furthermore, we collected an existing dataset by randomly altering the attribute values of 20% of the data
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發(fā)表于 2025-3-22 21:31:40 | 只看該作者
Rina Zazkis,Nathalie Sinclair,Peter Liljedahlsive datasets. While mobile apps provide practical deployment avenues, robust mechanisms for continuous user-driven refinement are lacking. Ultimately, it appears that context-conscious deep learning approaches balancing efficiency and representation across diverse contexts are imperative for maximi
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發(fā)表于 2025-3-23 03:46:27 | 只看該作者
Rina Zazkis,Nathalie Sinclair,Peter Liljedahlc and non-parasitic elements. Unlike traditional methods, our approach addresses previous limitations in sensitivity and specificity, leading to a notable improvement in classification performance. Our method demonstrated a groundbreaking 99.86% accuracy in parasite classification, marking a substan
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