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Toward A Theoretical Basis for Dynamically Driven Content: Computer-Mediated Environments and Personalised eLearning - Tapa blanda
Sinopsis
The intent of dynamic learning with data driven content (DDC) in computer-mediated learning environments is to interactively adapt the flow of content so that each student receives personalised learning materials and interventions more suited to their needs than in traditional one-size-fits-all applications. Measurement technologies similar to some models underlying computer-adaptive testing approaches (CAT) are used here to create personalisation by mapping knowledge spaces and driving computer-mediated learning environments. Methods explore extensions to CAT with item response models and construct mapping, which may direct the flow and difficulty not only of assessments but also of other e- learning materials and feedback to tailor the learning experience to student needs. A measurement model, the iota model, is introduced and tested as a multifacet Rasch model to estimate "pathway" parameters through BEAR CAT testlets. Testlets are small bundles of items that act as questions and follow-up probes to interactively measure and assign scores to students. The function of the measurement models applied is mathematically equivalent to the semi-linear neural net model.
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Reseña del editor
The intent of dynamic learning with data driven content (DDC) in computer-mediated learning environments is to interactively adapt the flow of content so that each student receives personalised learning materials and interventions more suited to their needs than in traditional one-size-fits-all applications. Measurement technologies similar to some models underlying computer-adaptive testing approaches (CAT) are used here to create personalisation by mapping knowledge spaces and driving computer-mediated learning environments. Methods explore extensions to CAT with item response models and construct mapping, which may direct the flow and difficulty not only of assessments but also of other e- learning materials and feedback to tailor the learning experience to student needs. A measurement model, the iota model, is introduced and tested as a multifacet Rasch model to estimate "pathway" parameters through BEAR CAT testlets. Testlets are small bundles of items that act as questions and follow-up probes to interactively measure and assign scores to students. The function of the measurement models applied is mathematically equivalent to the semi-linear neural net model.
Biografía del autor
Kathleen Scalise received her Ph.D. in quantitative measurement at the University of California, Berkeley. An assistant professor at the University of Oregon, she served as a writer of California's K-12 Science Framework, as UC Berkeley Chancellor's speechwriter, and works on dynamically delivered content in eLearning with item response models.
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Toward A Theoretical Basis for Dynamically Driven Content
Publicado por
LAP LAMBERT Academic Publishing, 2010
ISBN 10: 3838310373
ISBN 13: 9783838310374
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Tapa blanda
Librería: moluna, Greven, Alemania
Calificación del vendedor: 5 de 5 estrellas
Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Scalise KathleenKathleen Scalise received her Ph.D. in quantitative measurementat the University of California, Berkeley. An assistant professor at the University ofOregon, she served as a writer of California s K-12 Science Framewor. Nº de ref. del artículo: 5411744
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Toward A Theoretical Basis for Dynamically Driven Content
Publicado por
LAP LAMBERT Academic Publishing Mai 2010, 2010
ISBN 10: 3838310373
ISBN 13: 9783838310374
Nuevo
Taschenbuch
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The intent of dynamic learning with data driven content (DDC) in computer-mediated learning environments is to interactively adapt the flow of content so that each student receives personalised learning materials and interventions more suited to their needs than in traditional one-size-fits-all applications. Measurement technologies similar to some models underlying computer-adaptive testing approaches (CAT) are used here to create personalisation by mapping knowledge spaces and driving computer-mediated learning environments. Methods explore extensions to CAT with item response models and construct mapping, which may direct the flow and difficulty not only of assessments but also of other e- learning materials and feedback to tailor the learning experience to student needs. A measurement model, the iota model, is introduced and tested as a multifacet Rasch model to estimate 'pathway' parameters through BEAR CAT testlets. Testlets are small bundles of items that act as questions and follow-up probes to interactively measure and assign scores to students. The function of the measurement models applied is mathematically equivalent to the semi-linear neural net model. 332 pp. Englisch. Nº de ref. del artículo: 9783838310374
Cantidad disponible: 2 disponibles
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Toward A Theoretical Basis for Dynamically Driven Content : Computer-Mediated Environments and Personalised eLearning
Publicado por
LAP LAMBERT Academic Publishing, 2010
ISBN 10: 3838310373
ISBN 13: 9783838310374
Nuevo
Taschenbuch
Librería: AHA-BUCH GmbH, Einbeck, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The intent of dynamic learning with data driven content (DDC) in computer-mediated learning environments is to interactively adapt the flow of content so that each student receives personalised learning materials and interventions more suited to their needs than in traditional one-size-fits-all applications. Measurement technologies similar to some models underlying computer-adaptive testing approaches (CAT) are used here to create personalisation by mapping knowledge spaces and driving computer-mediated learning environments. Methods explore extensions to CAT with item response models and construct mapping, which may direct the flow and difficulty not only of assessments but also of other e- learning materials and feedback to tailor the learning experience to student needs. A measurement model, the iota model, is introduced and tested as a multifacet Rasch model to estimate 'pathway' parameters through BEAR CAT testlets. Testlets are small bundles of items that act as questions and follow-up probes to interactively measure and assign scores to students. The function of the measurement models applied is mathematically equivalent to the semi-linear neural net model. Nº de ref. del artículo: 9783838310374
Cantidad disponible: 1 disponibles
Imagen del vendedor
Toward A Theoretical Basis for Dynamically Driven Content
Publicado por
LAP LAMBERT Academic Publishing Mai 2010, 2010
ISBN 10: 3838310373
ISBN 13: 9783838310374
Nuevo
Taschenbuch
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. Neuware -The intent of dynamic learning with data driven content (DDC) in computer-mediated learning environments is to interactively adapt the flow of content so that each student receives personalised learning materials and interventions more suited to their needs than in traditional one-size-fits-all applications. Measurement technologies similar to some models underlying computer-adaptive testing approaches (CAT) are used here to create personalisation by mapping knowledge spaces and driving computer-mediated learning environments. Methods explore extensions to CAT with item response models and construct mapping, which may direct the flow and difficulty not only of assessments but also of other e- learning materials and feedback to tailor the learning experience to student needs. A measurement model, the iota model, is introduced and tested as a multifacet Rasch model to estimate 'pathway' parameters through BEAR CAT testlets. Testlets are small bundles of items that act as questions and follow-up probes to interactively measure and assign scores to students. The function of the measurement models applied is mathematically equivalent to the semi-linear neural net model.Books on Demand GmbH, Überseering 33, 22297 Hamburg 332 pp. Englisch. Nº de ref. del artículo: 9783838310374
Cantidad disponible: 2 disponibles