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    Innovative Learning Environments in STEM Higher Education

    Opportunities, Challenges, and Looking Forward

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    Contributor(s)
    Ryoo, Jungwoo (editor)
    Winkelmann, Kurt (editor)
    Language
    English
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    Abstract
    As explored in this open access book, higher education in STEM fields is influenced by many factors, including education research, government and school policies, financial considerations, technology limitations, and acceptance of innovations by faculty and students. In 2018, Drs. Ryoo and Winkelmann explored the opportunities, challenges, and future research initiatives of innovative learning environments (ILEs) in higher education STEM disciplines in their pioneering project: eXploring the Future of Innovative Learning Environments (X-FILEs). Workshop participants evaluated four main ILE categories: personalized and adaptive learning, multimodal learning formats, cross/extended reality (XR), and artificial intelligence (AI) and machine learning (ML). This open access book gathers the perspectives expressed during the X-FILEs workshop and its follow-up activities. It is designed to help inform education policy makers, researchers, developers, and practitioners about the adoption and implementation of ILEs in higher education.
    URI
    https://library.oapen.org/handle/20.500.12657/47325
    Keywords
    Statistics for Social Sciences, Humanities, Law; Machine Learning; Statistics and Computing/Statistics Programs; Learning & Instruction; Knowledge based Systems; Statistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy; Statistics and Computing; Education; Innovative Learning Environments; ILEs; Science, Technology, Engineering, and Math; STEM; virtual reality; VR; augmented reality; mixed reality; cross reality; extended reality; artificial intelligence; AI; adaptive learning; personalized learning; higher education; multimodal learning; mobile learning; Open Access; Social research & statistics; Mathematical & statistical software; Teaching skills & techniques; Cognition & cognitive psychology; Expert systems / knowledge-based systems
    DOI
    10.1007/978-3-030-58948-6
    ISBN
    9783030589486, 9783030589486
    Publisher
    Springer Nature
    Publisher website
    https://www.springernature.com/gp/products/books
    Publication date and place
    2021
    Grantor
    • Florida Institute of Technology - [grantnumber unknown]
    Series
    SpringerBriefs in Statistics,
    Classification
    Social research and statistics
    Machine learning
    Mathematical and statistical software
    Teaching skills and techniques
    Expert systems / knowledge-based systems
    Pages
    137
    Rights
    https://creativecommons.org/licenses/by/4.0/
    • Imported or submitted locally

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    License

    • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

    Credits

    • logo EU
    • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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