00819nam0-22002771i-450-99000502043040332119990530000502043FED01000502043(Aleph)000502043FED0100050204319990530g19809999km-y0itay50------baitaaf------00---Cultura contadina in LiguriaLa Val GravegliaHugo Plomteuxfotografie di Franco VergineGenovaSagep Ed.1980.252 p., [36] c. di tav.ill.29 cmPlomteux,Hugo195597Vergine,FrancoITUNINARICAUNIMARCBK990005020430403321ALPHA 4197Fil.Mod. 34493FLFBCFLFBCCultura contadina in Liguria133774UNINA03392nam 22005535 450 991029987300332120200707010533.03-319-67928-710.1007/978-3-319-67928-0(CKB)4100000000881537(DE-He213)978-3-319-67928-0(MiAaPQ)EBC6299003(MiAaPQ)EBC5577612(Au-PeEL)EBL5577612(OCoLC)1066197282(PPN)22012504X(EXLCZ)99410000000088153720171027d2018 u| 0engurnn|008mamaatxtrdacontentcrdamediacrrdacarrierAI Injected e-Learning The Future of Online Education /by Matthew Montebello1st ed. 2018.Cham :Springer International Publishing :Imprint: Springer,2018.1 online resource (XIX, 86 p. 6 illus.) Studies in Computational Intelligence,1860-949X ;7453-319-67927-9 Introduction -- e-Learning so far -- MOOCs, Crowdsourcing and Social Networks -- User Profiling and Personalisation -- Personal Learning Networks, Portfolios and Environments -- Customised e-Learning -- Looking Ahead.This book reviews a blend of artificial intelligence (AI) approaches that can take e-learning to the next level by adding value through customization. It investigates three methods: crowdsourcing via social networks; user profiling through machine learning techniques, and personal learning portfolios using learning analytics. Technology and education have drawn closer together over the years as they complement each other within the domain of e-learning, and different generations of online education reflect the evolution of new technologies as researcher and developers continuously seek to optimize the electronic medium to enhance the effectiveness of e-learning. Artificial intelligence (AI) for e-learning promises personalized online education through a combination of different intelligent techniques that are grounded in established learning theories while at the same time addressing a number of common e-learning issues. This book is intended for education technologists and e-learning researchers as well as for a general readership interested in the evolution of online education based on techniques like machine learning, crowdsourcing, and learner profiling that can be merged to characterize the future of personalized e-learning.Studies in Computational Intelligence,1860-949X ;745Computational intelligenceArtificial intelligenceComputational Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/T11014Artificial Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/I21000Computational intelligence.Artificial intelligence.Computational Intelligence.Artificial Intelligence.371.3344678Montebello Matthewauthttp://id.loc.gov/vocabulary/relators/aut1063951MiAaPQMiAaPQMiAaPQBOOK9910299873003321AI Injected e-Learning2535452UNINA