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Titlebook: Ludic, Co-design and Tools Supporting Smart Learning Ecosystems and Smart Education; Proceedings of the 5 óscar Mealha,Matthias Rehm,Traian

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發(fā)表于 2025-3-21 18:22:03 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Ludic, Co-design and Tools Supporting Smart Learning Ecosystems and Smart Education
副標(biāo)題Proceedings of the 5
編輯óscar Mealha,Matthias Rehm,Traian Rebedea
視頻videohttp://file.papertrans.cn/589/588955/588955.mp4
概述Presents papers from the 5th International Conference on Smart Learning Ecosystems and Regional Development.Provides an overview of major research applications in smart learning ecosystems.Is a valuab
叢書名稱Smart Innovation, Systems and Technologies
圖書封面Titlebook: Ludic, Co-design and Tools Supporting Smart Learning Ecosystems and Smart Education; Proceedings of the 5 óscar Mealha,Matthias Rehm,Traian
描述..This book presents papers from the 5th International Conference on Smart Learning Ecosystems and Regional Development, which promotes discussions on R&D work, policies, case studies, entrepreneur experiences, with a particular focus on understanding the relevance of smart learning ecosystems for regional development and social innovation, and how the effectiveness of the relation of citizens and smart ecosystems can be boosted. The book explores how technology-mediated instruments can foster citizens’ engagement with learning ecosystems and territories, providing insights into innovative human-centric design and development models/techniques,?education/training practices, informal social learning, innovative citizen-driven policies, and technology-mediated experiences and their impact. As such, it will inspire the social innovation sectors and ICT, as well as economic development and deployment strategies and new policies for smarter proactive citizens.?.
出版日期Conference proceedings 2021
關(guān)鍵詞Smart Learning Ecosystems; Human-Centric Design; Info-Communication; ICT Mediation; Ecosystem Planning; B
版次1
doihttps://doi.org/10.1007/978-981-15-7383-5
isbn_softcover978-981-15-7385-9
isbn_ebook978-981-15-7383-5Series ISSN 2190-3018 Series E-ISSN 2190-3026
issn_series 2190-3018
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
The information of publication is updating

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An Analysis of Alternation Schemes to Increase Student Employability and the Smartness of Secondary owed to identify commonalities and criticalities and to extract a set of guidelines and recommendations on how to improve the alternation schemes and satisfy the expectations of all actors (students, companies, family, schools) and, thus, to increase the smartness of the educational processes and, as a consequence, that of the schools.
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Conceptualization of Hypersituation as Result of IoT in Educationowever, despite several references to this fact, this term still lacks further conceptualization and the drawing of guidelines to achieve it. Thus, this paper aims to present an interpretation and definition of the term hypersituation indicating potentials, challenges, and ways to achieve it.
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Conference proceedings 2021 R&D work, policies, case studies, entrepreneur experiences, with a particular focus on understanding the relevance of smart learning ecosystems for regional development and social innovation, and how the effectiveness of the relation of citizens and smart ecosystems can be boosted. The book explore
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Effect Induced by the Covid-19 Pandemic on Students’ Perception About Technologies and Distance Learng an historical documentation, question the nowadays organization of the physical learning ecosystems, that date to few centuries ago and suggest to re-think both their organization and functionalities.
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Cohesion Network Analysis for Predicting User Ranks in Reddit Communitiese for textual complexity, reflective of writing style specificities. Various regression models were trained and evaluated in order to predict users’ rankings, which are reflected in the number of votes they receive from their peers. The results show that the user ranks are predicted with a precision of 15 votes by using MLP neural networks.
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