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1. |
Record Nr. |
UNINA9910462332503321 |
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Autore |
Moore Alex <1947-, > |
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Titolo |
Teaching and learning : pedagogy, curriculum and culture / / Alex Moore |
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Pubbl/distr/stampa |
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London ; ; New York : , : Routledge, , 2012 |
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ISBN |
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0-415-66365-2 |
1-280-68270-1 |
9786613659644 |
1-136-48053-6 |
1-136-48054-4 |
0-203-13406-0 |
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Edizione |
[2nd ed.] |
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Descrizione fisica |
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1 online resource (207 p.) |
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Disciplina |
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Soggetti |
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Teaching |
Learning |
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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Bibliographic Level Mode of Issuance: Monograph |
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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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1. Theories of teaching and learning -- 2. Teaching, learning and education -- 3. Teaching, learning and language -- 4. Teaching, learning and culture -- 5. What makes a 'good teacher'? -- 6. Teaching, learning and the curriculum : pedagogic and curricular alternatives. |
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Sommario/riassunto |
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"Teaching and Learning: Pedagogy, Curriculum and Culture is designed to share important theory with readers in an accessible but sophisticated way. It offers an overview of the key issues and dominant theories of teaching and learning as they impact upon the practice of education professionals in the classroom. This second edition has been updated to take account of significant changes in the field; young people's use of digital technologies, the increasing involvement of world of business in state education, and ongoing high-profile debates about assessment, to name but a few. It examines the global move from traditional subject-and-knowledge based curricula towards skills and problem-solving and discusses how the emphasis on education for citizenship has forced us to reconsider the social functions of |
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education. Central topics also covered include: - An assessment of the most influential theorists of learning and teaching - The ways in which public educational policy impinges on local practice - The nature and role of language and culture in formal educational settings - An assessment of different models of 'good teaching' - Alternative models of curriculum and pedagogy. With questions, points for consideration and ideas for further reading and research throughout, this book delivers discussion and analysis designed to support understanding of classroom interactions and to contribute to improved practice. It will be essential reading for all student teachers, those engaged in professional development and Education Studies students"-- |
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2. |
Record Nr. |
UNISA996418261603316 |
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Titolo |
Network Algorithms, Data Mining, and Applications [[electronic resource] ] : NET, Moscow, Russia, May 2018 / / edited by Ilya Bychkov, Valery A. Kalyagin, Panos M. Pardalos, Oleg Prokopyev |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
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ISBN |
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Edizione |
[1st ed. 2020.] |
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Descrizione fisica |
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1 online resource (XIII, 244 p. 65 illus., 43 illus. in color.) |
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Collana |
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Springer Proceedings in Mathematics & Statistics, , 2194-1009 ; ; 315 |
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Disciplina |
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Soggetti |
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Mathematical optimization |
Neural networks (Computer science) |
Combinatorics |
Optimization |
Mathematical Models of Cognitive Processes and Neural Networks |
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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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Part I: Network algorithms -- Obaid, H. B. and Trafalis, T: Fairness in Resource Allocation: Foundation and Applications -- Ignatov, D., Ivanova, P., Zamaletdinova, A. and Prokopyev, O: Searching for Maximum Quasi-Bicliques with Mixed Integer Programming -- Miasnikof, P., Pitsoulis, L., Bonner, A. J., Lawryshyn, Y. and Pardalos, P. |
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M: Graph Clustering Via Intra-Cluster Density Maximization -- Shvydun, S.: Computational Complexity of SRIC and LRIC indices -- Sifaleras, A. and Konstantaras, I: A survey on variable neighborhood search methods for supply network inventory -- Part II: Network Data Mining -- Ananyeva, M. and Makarov, I: GSM: Inductive Learning on Dynamic Graph Embeddings -- Averchenkova, A., Akhmetzyanova, A., Sudarikov, K., Sulimov, P., Makarov I. and Zhukov, L. E: Collaborator Recommender System based on Co-authorship Network Analysis -- Demochkin, K. and Savchenko, A: User Preference Prediction in a Set of Photos based on Neural Aggregation Network -- Makrushin , S.: Network structure and scheme analysis of the Russian language segment of Wikipedia -- Meshcheryakova, N., Shvydun, S. and Aleskerov, F: Indirect Influence Assessment in the Context of Retail Food Network -- Sokolova, A. D. and Savchenko, A. V: Facial clustering in video data using deep convolutional neural networks -- Part III: Network Applications -- Egamov, A.: The existence and uniqueness theorem for initial-boundary value problem of the same class of integro-differential PDEs -- Gradoselskaya, G., Karpov, I. and Shcheglova, T: Mapping of politically active groups on social networks of Russian regions (on the example of Karachay-Cherkessia Republic) -- Mikhailova, O., Gradoselskaya, G. and Kharlamov, A: Social Mechanisms of the Subject Area Formation. The Case of “Digital Economy -- Shcheglova, T., Gradoselskaya, G. and Karpov, I: Methodology for measuring polarization of political discourse: case of comparing oppositional and patriotic discourse in online social networks -- Zaytsev, D., Khvatsky, G., Talovsky, N. and Kuskova, V: Network Analysis Methodology of Policy Actors Identification and Power Evaluation (the case of the Unified State Exam introduction in Russia). |
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Sommario/riassunto |
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This proceedings presents the result of the 8th International Conference in Network Analysis, held at the Higher School of Economics, Moscow, in May 2018. The conference brought together scientists, engineers, and researchers from academia, industry, and government. Contributions in this book focus on the development of network algorithms for data mining and its applications. Researchers and students in mathematics, economics, statistics, computer science, and engineering find this collection a valuable resource filled with the latest research in network analysis. Computational aspects and applications of large-scale networks in market models, neural networks, social networks, power transmission grids, maximum clique problem, telecommunication networks, and complexity graphs are included with new tools for efficient network analysis of large-scale networks. Machine learning techniques in network settings including community detection, clustering, and biclustering algorithms are presented with applications to social network analysis. |
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