9781041134084 - burnout intervention mechanisms for online learning processes enabled by predictive learning analytics di xia, xiaona; qi, wanxue (16 risultati)
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Da: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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EUR 228,52
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Da: Majestic Books, Hounslow, Regno UnitoMajestic Books
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Da: GreatBookPricesUK, Woodford Green, Regno UnitoGreatBookPricesUK
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Da: Books Puddle, New York, NY, U.S.A.Books Puddle
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EUR 239,18
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Da: GreatBookPricesUK, Woodford Green, Regno UnitoGreatBookPricesUK
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Da: PBShop.store UK, Fairford, GLOS, Regno UnitoPBShop.store UK
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Da: California Books, Miami, FL, U.S.A.California Books
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Da: Biblios, frankfurt am main, HESSE, GermaniaBiblios
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Da: Kennys Bookstore, Olney, MD, U.S.A.Kennys Bookstore
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Da: Revaluation Books, Exeter, Regno UnitoRevaluation Books
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Hardcover. Condizione: Brand New. 204 pages. 9.18x6.12x9.45 inches. In Stock.
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Da: Grand Eagle Retail, Bensenville, IL, U.S.A.Grand Eagle Retail
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EUR 230,85
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Hardcover. Condizione: new. Hardcover. This book aims to fully demonstrate the burnout of learners in online learning processes. The authors propose a series of feasible and reliable solutions to sufficiently obtain and analyze massive instances of online learning behavior.In order to flexibly perceive and intervene in the "burn…out state" and improve online learning processes and learning effectiveness, the authors design and construct various novel data analysis models and decision prediction methods using technological means and data-driven learning strategies. Their innovative methods, techniques, and decisions would benefit autonomous learning behavior tracking and stimulate the learning interest of online learning processes enabled by predictive learning analytics. By employing behavioral science research strategies, they build adaptive prediction and optimization measures for positive online learning patterns, improve learning behaviors, optimize learning states, and establish dynamic and sustainable knowledge tracing paths and behavior scheduling methods, enabling users to achieve self-organization and self-mobilization in their overall learning processes.The book will appeal to scholars and learners in Europe, North America, and Asia, especially those majoring in educational statistics and measurement, educational big data, learning analytics, educational psychology, artificial intelligence in education, computer science, and online collaborative learning. This title aims to fully demonstrate the burnout of students in online learning processes. The authors propose a series of feasible and reliable solutions to sufficiently obtain and analyze massive instances of online learning behavior. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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- Print on Demand
Da: CitiRetail, Stevenage, Regno UnitoCitiRetail
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 260,01
EUR 43,24 spedizioneSpedito da Regno Unito a U.S.A.Quantità: 1 disponibili
Hardcover. Condizione: new. Hardcover. This book aims to fully demonstrate the burnout of learners in online learning processes. The authors propose a series of feasible and reliable solutions to sufficiently obtain and analyze massive instances of online learning behavior.In order to flexibly perceive and intervene in the "burn…out state" and improve online learning processes and learning effectiveness, the authors design and construct various novel data analysis models and decision prediction methods using technological means and data-driven learning strategies. Their innovative methods, techniques, and decisions would benefit autonomous learning behavior tracking and stimulate the learning interest of online learning processes enabled by predictive learning analytics. By employing behavioral science research strategies, they build adaptive prediction and optimization measures for positive online learning patterns, improve learning behaviors, optimize learning states, and establish dynamic and sustainable knowledge tracing paths and behavior scheduling methods, enabling users to achieve self-organization and self-mobilization in their overall learning processes.The book will appeal to scholars and learners in Europe, North America, and Asia, especially those majoring in educational statistics and measurement, educational big data, learning analytics, educational psychology, artificial intelligence in education, computer science, and online collaborative learning. This title aims to fully demonstrate the burnout of students in online learning processes. The authors propose a series of feasible and reliable solutions to sufficiently obtain and analyze massive instances of online learning behavior. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
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- Print on Demand
Da: AussieBookSeller, Truganina, VIC, AustraliaAussieBookSeller
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 287,65
EUR 31,67 spedizioneSpedito da Australia a U.S.A.Quantità: 1 disponibili
Hardcover. Condizione: new. Hardcover. This book aims to fully demonstrate the burnout of learners in online learning processes. The authors propose a series of feasible and reliable solutions to sufficiently obtain and analyze massive instances of online learning behavior.In order to flexibly perceive and intervene in the "burn…out state" and improve online learning processes and learning effectiveness, the authors design and construct various novel data analysis models and decision prediction methods using technological means and data-driven learning strategies. Their innovative methods, techniques, and decisions would benefit autonomous learning behavior tracking and stimulate the learning interest of online learning processes enabled by predictive learning analytics. By employing behavioral science research strategies, they build adaptive prediction and optimization measures for positive online learning patterns, improve learning behaviors, optimize learning states, and establish dynamic and sustainable knowledge tracing paths and behavior scheduling methods, enabling users to achieve self-organization and self-mobilization in their overall learning processes.The book will appeal to scholars and learners in Europe, North America, and Asia, especially those majoring in educational statistics and measurement, educational big data, learning analytics, educational psychology, artificial intelligence in education, computer science, and online collaborative learning. This title aims to fully demonstrate the burnout of students in online learning processes. The authors propose a series of feasible and reliable solutions to sufficiently obtain and analyze massive instances of online learning behavior. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
