Smart Learning

dc.contributor.authorGarcía-Peñalvo, F. J.
dc.contributor.authorCasado-Lumbreras, C.
dc.contributor.authorColomo-Palacios, R.
dc.contributor.authorYadav, A.
dc.date.accessioned2020-10-06T11:33:54Z
dc.date.available2020-10-06T11:33:54Z
dc.date.issued2020-10-06
dc.description.abstractArtificial 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 learningen
dc.identifier.citationGarcía-Peñalvo, F. J., Casado-Lumbreras, C., Colomo-Palacios, R., & Yadav, A. (2020). Smart Learning. Applied Sciences, 10(9), 6964. doi:10.3390/app10196964en
dc.identifier.issn2076-3417
dc.identifier.urihttp://repositorio.grial.eu/handle/grial/2136
dc.language.isoenen
dc.publisherMDPIen
dc.subjectartificial intelligenceen
dc.subjectsmart systemsen
dc.subjectmachine learningen
dc.subjectdeep learningen
dc.subjecteducationen
dc.subjectlearning technologiesen
dc.subjectextraction information from educational environmentsen
dc.subjectInternet of Things applied to educationen
dc.subjecteducational data miningen
dc.subjectcloud computing in educationen
dc.subjectdata mining and big data analysisen
dc.subjectintelligent systems for educationen
dc.titleSmart Learningen
dc.typeArticleen

Files

Original bundle

Now showing 1 - 1 of 1
Thumbnail Image
Name:
applsci-10-06964.pdf
Size:
223.92 KB
Format:
Adobe Portable Document Format
Description:
Article

License bundle

Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:

Collections