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Titlebook: Machine Learning Paradigms; Advances in Learning Maria Virvou,Efthimios Alepis,Lakhmi C. Jain Book 2020 Springer Nature Switzerland AG 2020

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發(fā)表于 2025-3-21 17:11:46 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱Machine Learning Paradigms
副標(biāo)題Advances in Learning
編輯Maria Virvou,Efthimios Alepis,Lakhmi C. Jain
視頻videohttp://file.papertrans.cn/621/620416/620416.mp4
概述Presents recent machine learning paradigms and advances in learning analytics.Provides concise coverage from the vantage point of a newcomer, but will also appeal to experts/researchers in learning an
叢書(shū)名稱Intelligent Systems Reference Library
圖書(shū)封面Titlebook: Machine Learning Paradigms; Advances in Learning Maria Virvou,Efthimios Alepis,Lakhmi C. Jain Book 2020 Springer Nature Switzerland AG 2020
描述.This book presents recent machine learning paradigms and advances in learning analytics, an emerging research discipline concerned with the collection, advanced processing, and extraction of useful information from both educators’ and learners’ data with the goal of improving education and learning systems. In this context, internationally respected researchers present various aspects of learning analytics and selected application areas, including:.? Using learning analytics to measure student engagement, to quantify the learning experience and to facilitate self-regulation;.? Using learning analytics to predict student performance;.? Using learning analytics to create learning materials and educational courses; and.? Using learning analytics as a tool to support learners and educators in synchronous and asynchronous eLearning.. .The book offers a valuable asset for professors, researchers, scientists, engineers and students of all disciplines. Extensive bibliographies at the end of each chapter guide readers to probe further into their application areas of interest..
出版日期Book 2020
關(guān)鍵詞Learning Analytics; Mobile Learning; Educational Tools; Social Network Learning; Big Learning Data; Analy
版次1
doihttps://doi.org/10.1007/978-3-030-13743-4
isbn_ebook978-3-030-13743-4Series ISSN 1868-4394 Series E-ISSN 1868-4408
issn_series 1868-4394
copyrightSpringer Nature Switzerland AG 2020
The information of publication is updating

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Jede Hauptgruppe basiert auf spezifischen Merkmalen. Im Falle einer ?nderung der Form wird der Stoffzusammenhalt beibehalten, vermindert oder vermehrt (vgl. Tab.?7.1). .Die in folgenden Abschnitten beschriebenen Fertigungsverfahren finden sich zum Beispiel in den Hauptgruppen.beziehungsweise.Andere
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David Martín Santos Melgozan des konstruktiven Leichtbaus im Fahrzeug- und Maschinenbau. Dabei wurde besonderer Wert auf eine praxisorientierte Darstellung gelegt, um der Ingenieurausbildung an Hochschulen passgenau gerecht zu werden. Es führt methodisch in die Arbeitstechniken und konstruktiven Fragestellungen ein. Ziel des
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Machine Learning Paradigms,ment of more engaging and human-like computer-based learning, personalization and incorporation of artificial intelligence techniques. A new research discipline, termed ., is emerging and examines the collection and intelligent analysis of learner and instructor data with the goal to extract informa
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Using a Multi Module Model for Learning Analytics to Predict Learners’ Cognitive States and Provide m learning analytics in order to support the digital education. The way learning analytics is used, can vary. It can be used to provide learners with information to reflect on their achievements and patterns of behavior in relation to others, or to identify students requiring extra support and atten
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發(fā)表于 2025-3-22 21:51:58 | 只看該作者
Analytics for Student Engagement studies. This significantly elevates opportunities to better understand how students learn. The learning analytics community is exploring these data to describe learning processes [.] and ground recommendations for improved learning environments [., ., .]. One challenge in this work is need for mor
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Learning Feedback Based on Dispositional Learning Analyticsd on self-report surveys, offers a very rich context for learning analytics applications. In previous research, we have demonstrated how such Dispositional Learning Analytics applications not only have great potential regarding predictive power, e.g. with the aim to promptly signal students at risk,
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