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Editorial

Smart Learning

1
GRIAL Research Group, Computer Science Department, University of Salamanca, 37008 Salamanca, Spain
2
Research and Psychology in Education Department, Universidad Complutense de Madrid, Pozuelo de Alarcón, 28223 Madrid, Spain
3
Department of Computer Science, Østfold University College, 1783 Halden, Norway
4
Department of Counseling, Educational Psychology and Special Education, Michigan State University, East Lansing, MI 48824, USA
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(19), 6964; https://doi.org/10.3390/app10196964
Received: 22 September 2020 / Accepted: 1 October 2020 / Published: 5 October 2020
(This article belongs to the Special Issue Smart Learning)
Artificial intelligence applied to the educational field has a vast potential, especially after the effects worldwide of the COVID-19 pandemic. Online or blended educational modes are needed to respond to the health situation we are living in. The tutorial effort is higher than in the traditional face-to-face approach. Thus, educational systems are claiming smarter learning technologies that do not pretend to substitute the faculty but make their teaching activities easy. This Special Issue is oriented to present a collection of papers of original advances in educational applications and services propelled by artificial intelligence, big data, machine learning, and deep learning. View Full-Text
Keywords: artificial intelligence; smart systems; machine learning; deep learning; education; learning technologies; extraction information from educational environments; Internet of Things applied to education; educational data mining; cloud computing in education; data mining and big data analysis; intelligent systems for education; machine and deep learning in education; diagnostic and predictive analytics in educational processes; intelligent process applied in specific educational domains; activity recognition in education; data authentication and security in educational environments; privacy-preserving systems for education; computational models in education artificial intelligence; smart systems; machine learning; deep learning; education; learning technologies; extraction information from educational environments; Internet of Things applied to education; educational data mining; cloud computing in education; data mining and big data analysis; intelligent systems for education; machine and deep learning in education; diagnostic and predictive analytics in educational processes; intelligent process applied in specific educational domains; activity recognition in education; data authentication and security in educational environments; privacy-preserving systems for education; computational models in education
MDPI and ACS Style

García-Peñalvo, F.J.; Casado-Lumbreras, C.; Colomo-Palacios, R.; Yadav, A. Smart Learning. Appl. Sci. 2020, 10, 6964. https://doi.org/10.3390/app10196964

AMA Style

García-Peñalvo FJ, Casado-Lumbreras C, Colomo-Palacios R, Yadav A. Smart Learning. Applied Sciences. 2020; 10(19):6964. https://doi.org/10.3390/app10196964

Chicago/Turabian Style

García-Peñalvo, Francisco J., Cristina Casado-Lumbreras, Ricardo Colomo-Palacios, and Aman Yadav. 2020. "Smart Learning" Applied Sciences 10, no. 19: 6964. https://doi.org/10.3390/app10196964

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