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Record Nr. |
UNINA9910452251503321 |
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Autore |
BUCK MARTIN |
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Titolo |
Assessing Children's Personal And Social Development [[electronic resource] ] : Measuring The Unmeasurable? |
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Pubbl/distr/stampa |
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Hoboken, : Taylor and Francis, 1998 |
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Descrizione fisica |
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1 online resource (205 p.) |
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Altri autori (Persone) |
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Disciplina |
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Soggetti |
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Child psychology |
Personality assessment of children |
Social skills in children |
Social skills in children - Testing |
Testing |
Electronic books. |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di contenuto |
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Book Cover; Half-Title; Title; Copyright; Contents; Acknowledgments; Preface; Introduction; Chapter 1 Personal and Social Development at the Crossroads; Chapter 2 Personal and Social Development within the National Context: A Review of Recent and Current Initiatives; Chapter 3 'It's Not a Good Time for Children'-Assessment Issues within Personal and Social Development; Chapter 4 Learning Outcomes for Personal and Social Development; Chapter 5 Educating for Personal and Social Development: A Question of Discipline?; Chapter 6 A Curriculum Framework for Personal and Social Development |
A National ProjectChapter 7 Value Development in the Early Years; Approaches Through Story; Chapter 8 Conferencing: Structured Talk and PSD in the Secondary School; Chapter 9 Conferencing in the Primary School: Possibilities and Issues; Chapter 10 Researching Assessment Practice in PSE: A Secondary Case Study; Postscript: Which Way Now?; Notes on Contributors; Index |
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Sommario/riassunto |
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Social and personal development of pupils is an area of growing |
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interest. However, while much has been done in relation to provision for development, there is little available on how teachers might assess the development of pupils, be it spiritual, moral, social or cultural. The contributors also examine how we might accredit such development. With provision for development on the national agenda, this title looks at the repercussions and examines the difficult issues raised by assessment and accreditation - and the problems with which teachers will inevitably be faced. |
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2. |
Record Nr. |
UNINA9910951798703321 |
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Autore |
Sukumar N |
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Titolo |
Navigating Molecular Networks / / by N. Sukumar |
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Pubbl/distr/stampa |
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Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024 |
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ISBN |
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9783031762901 |
9783031762895 |
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Edizione |
[1st ed. 2024.] |
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Descrizione fisica |
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1 online resource (151 pages) |
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Collana |
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SpringerBriefs in Materials, , 2192-1105 |
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Disciplina |
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Soggetti |
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Statistical physics |
Biophysics |
Biomolecules |
Graph theory |
Stochastic processes |
Machine learning |
Soft condensed matter |
Statistical Physics |
Molecular Biophysics |
Graph Theory |
Stochastic Networks |
Machine Learning |
Soft Materials |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Molecular Networks -- Transformations of Chemical Space -- Spectral Graph Theory -- Universality and Random Matrix Theory -- Mapping and Navigating Chemical Space Networks -- Generative AI – Growing the Network -- Discovery and Creativity. |
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Sommario/riassunto |
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This book delves into the foundational principles governing the treatment of molecular networks and "chemical space"—the comprehensive domain encompassing all physically achievable molecules—from the perspectives of vector space, graph theory, and data science. It explores similarity kernels, network measures, spectral graph theory, and random matrix theory, weaving intriguing connections between these diverse subjects. Notably, it emphasizes the visualization of molecular networks. The exploration continues by delving into contemporary generative deep learning models, increasingly pivotal in the pursuit of new materials possessing specific properties, showcasing some of the most compelling advancements in this field. Concluding with a discussion on the meanings of discovery, creativity, and the role of artificial intelligence (AI) therein. Its primary audience comprises senior undergraduate and graduate students specializing in physics, chemistry, and materials science. Additionally, it caters to those interested in the potential transformation of material discovery through computational, network, AI, and machine learning (ML) methodologies. |
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