02276nam 2200469 450 991055501550332120200110022124.91-119-56357-71-119-56358-51-119-51372-3(CKB)4100000007111280(MiAaPQ)EBC5566701(CaSebORM)9781786303165(PPN)23739104X(EXLCZ)99410000000711128020181116d2018 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierModeling of next generation digital learning environments complex systems theory /Marc Trestini1st editionLondon, UK :ISTE ;Hoboken, NJ :Wiley,2018.1 online resource (274 pages)1-78630-316-7 The emergence of social networks, OpenCourseWare, Massive Open Online Courses, informal remote learning and connectivist approaches to learning has made the analysis and evaluation of Digital Learning Environments more complex. Modeling these complex systems makes it possible to transcribe the phenomena observed and facilitates the study of these processes with the aid of specific tools. Once this essential step is taken, it then becomes possible to develop plausible scenarios from the observation of emerging phenomena and dominant trends. This book highlights the contribution of complex systems theory in the study of next generation Digital Learning Environments. It describes a realistic approach and proposes a range of effective management tools to achieve it.MOOCs (Web-based instruction)Simulated environment (Teaching method)NumeracyStudy and teachingTechnological innovationsMOOCs (Web-based instruction)Simulated environment (Teaching method)NumeracyStudy and teachingTechnological innovations.371.3344678Trestini Marc1220007MiAaPQMiAaPQMiAaPQBOOK9910555015503321Modeling of next generation digital learning environments2820907UNINA01542nam0 22003613i 450 VAN0024504420251113091502.242N978303068310820220422d2021 |0itac50 baengCH|||| |||||i e bcrArtificial Intelligence for Materials Scienceeditors Yuan Cheng, Tian Wang, Gang ZhangChamSpringer2021VII, 228 p.ill.24 cm001VAN000239902001 Springer series in materials science210 BerlinSpringer1986-312CHChamVANL001889620.11Materiali dell'ingegneria22620.1Scienze dei materiali22ChengYuanVANV087661ZhangGangVANV200144Springer <editore>VANV108073650ITSOL20251114RICAhttps://link.springer.com/book/10.1007/978-3-030-68310-8E-book - Accesso al full-text attraverso riconoscimento IP di Ateneo, proxy e/o ShibbolethBIBLIOTECA DEL DIPARTIMENTO DI SCIENZE E TECNOLOGIE AMBIENTALI BIOLOGICHE E FARMACEUTICHEIT-CE0101VAN17NVAN00245044BIBLIOTECA DEL DIPARTIMENTO DI SCIENZE E TECNOLOGIE AMBIENTALI BIOLOGICHE E FARMACEUTICHE17CONS e-book 2222 17BIB2222/134 134 20220422 Artificial intelligence for materials science1905207UNICAMPANIA