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Record Nr. |
UNINA9910511801503321 |
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Autore |
Tummons Jonathan |
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Titolo |
Learning architectures in higher education : beyond communities of practice / / Jonathan Tummons |
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Pubbl/distr/stampa |
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London : , : Bloomsbury Academic, , 2017 |
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ISBN |
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1-4742-6172-8 |
1-4742-6170-1 |
1-4742-6171-X |
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Descrizione fisica |
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1 online resource (vi, 171 pages) |
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Disciplina |
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Soggetti |
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Education, Higher |
Education |
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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Nota di bibliografia |
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Includes bibliographical references (pages 151-168). |
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Nota di contenuto |
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1. Communities of Practice -- 2. Communities of Practice in Higher Education -- 3. Learning and Assessment in Communities of Practice -- 4. Necessary Extensions, Part 1 - Actor- Network Theory, and Literacy Studies -- 5. Necessary Extensions, Part 2 - Threshold Concepts, and Activity Theory -- 6. Learning Architectures in Higher Education -- 7. Learning Architectures in Teacher Training -- 8. Learning Architectures in Medical Education -- 9. Two Conclusions -- References -- Index |
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Sommario/riassunto |
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"Learning Architectures in Higher Education restores criticality and rigour to the study of communities of practice as a means of understanding learning, acknowledging that this is one of the most influential and widely used theories of learning to emerge during the last 30 years but one that has been misapplied and diluted. Jonathan Tummons explores communities of practice theory, looking at how its focus on learning as apprenticeship can be understood, providing the reader with a conceptual framework for making sense of learning as a social practice as distinct from an individual, psychological process. Tummons looks at how communities of practice theory needs to be reconfigured to take account of the insights provided by other |
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theoretical models and then applies his critically and theoretically reworked perspective to two distinct higher education contexts, providing critical and powerful tools for examining learning and teaching practices."--Bloomsbury Publishing. |
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2. |
Record Nr. |
UNINA9910828170003321 |
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Autore |
Bisi Manjubala |
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Titolo |
Artificial neural network for software reliability prediction / / by Manjubala Bisi and Neeraj Kumar Goyal |
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Pubbl/distr/stampa |
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Hoboken, New Jersey ; ; Beverly, Massachusetts : , : John Wiley & Sons : , : Scrivener Publishing, , 2017 |
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©2017 |
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ISBN |
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1-119-22396-2 |
1-119-22392-X |
1-119-22393-8 |
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Edizione |
[First edition] |
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Descrizione fisica |
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1 online resource (220 pages) : illustrations, figures, tables |
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Collana |
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Performability engineering series. |
THEi Wiley ebooks. |
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Disciplina |
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Soggetti |
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Neural networks (Computer science) |
Computer software - Reliability |
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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 bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Introduction -- Software reliability modelling -- Prediction of cumulative number of software failures -- Prediction of time between successive software failures -- Identification of software fault-prone modules -- Prediction of software development efforts -- Recent trends in software reliability. |
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Sommario/riassunto |
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Artificial neural network (ANN) has proven to be a universal approximator for any non-linear continuous function with arbitrary accuracy. This book presents how to apply ANN to measure various software reliability indicators: number of failures in a given time, time between successive failures, fault-prone modules and development efforts. The application of machine learning algorithm i.e. artificial |
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neural networks application in software reliability prediction during testing phase as well as early phases of software development process is presented as well. Applications of artificial neural network for the above purposes are discussed with experimental results in this book so that practitioners can easily use ANN models for predicting software reliability indicators. |
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