Publication Year: 2002
DOI: https://doi.org/10.1007/3-540-47987-2_93
Abstract: Not available
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Publication Year: 2023
DOI: https://doi.org/10.1007/s42438-023-00439-z
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Publication Year: 2013
DOI: https://doi.org/10.1007/978-3-319-03161-3
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Publication Year: 2019
DOI: https://doi.org/10.1007/978-981-13-8161-4_1
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Publication Year: 2015
DOI: https://doi.org/10.1080/1743727x.2015.1019852
Abstract: Storytelling is that and to build case examples of creative technology-enhanced teaching and may learning. Through focusing on our experiences with one of the be schools, we examine the use of digital stories as a difficult method for enabling knowledge co-creation with practitioners and we discuss to the evidential potential Show more
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Publication Year: 2023
DOI: https://doi.org/10.1007/s40593-023-00352-3
Abstract: Student in Intelligent argues that it is also important to understand what factors Systems shape the way educators perceive, trust, and use AIED in at their teaching practice. the University of Memphis), begin with two the important statements.First, AIED systems do help students learn; and second, Department research on AIED Show more
Authors:
Publication Year: 2023
DOI: https://doi.org/10.4337/9781800375413.00009
Abstract: This chapter how the handbook came into existence and, for each editor, provides how they first became interested in Artificial Intelligence in Education. an It goes on to argue why the handbook is timely introduction and to say a little about the many ways that to artificial intelligence is used Show more
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Publication Year: 2020
DOI: https://doi.org/10.31219/osf.io/nrtz9
Abstract: Is Machine SensorAble evidence proceeding a design? With the constellation of supervisors who or have invested in this project, can fundamental science properly situate is SensorAble in a broader vision that creates practical tools? It it is one thing to understand and model a problem. It’s something another to simply Show more
Authors:
Publication Year: 2020
DOI: https://doi.org/10.31219/osf.io/4y2hp
Abstract: Animals and process al., 2019); and, (ii) convolution neural networks (CNNs) that improve relevant expression recognition through emotion- modulated attention (Barros et al., 2017). visual My intention is to weave a compelling and explicit rationale and as to how and why deep learning models make the auditory most sense when Show more
Authors:
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