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020			_a9783030391300
024	7		_a10.1007/978-3-030-39130-0 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
041	0		_aeng
050		4	_aLB1028.43 _b2020 EB
100	1		_aTroussas, Christos _eautor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
245	0	0	_aAdvances in Social Networking-based Learning _bMachine Learning-based User Modelling and Sentiment Analysis / _cby Christos Troussas, Maria Virvou.
250			_a1st ed. 2020.
264		1	_aCham _bSpringer International Publishing : _bImprint: Springer _c2020.
300			_a1 recurso en línea (XII, 176 páginas)
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF
490	0		_aIntelligent Systems Reference Library _x1868-4394 _v181
490	0		_aIntelligent Technologies and Robotics (Springer-42732)
505	0		_aIntroduction -- Related Work -- Intelligent, Adaptive and social e-learning in POLYGLOT. .
520	3		_aThis book discusses three important, hot research issues: social networking-based learning, machine learning-based user modeling and sentiment analysis. Although these three technologies have been widely used by researchers around the globe by academic disciplines and by R&D departments in the IT industry, they have not yet been used extensively for the purposes of education. The authors present a novel approach that uses adaptive hypermedia in e-learning models to personalize educational content and learning resources based on the needs and preferences of individual learners. According to reports, in 2018 the vast majority of internet users worldwide are active on social networks, and the global average social network penetration rate as of 2018 is close to half the population. Employing social networking technologies in the field of education allows the latest technological advances to be used to create interactive educational environments where students can learn, collaborate with peers and communicate with tutors while benefiting from a social and pedagogical structure similar to a real class. The book first discusses in detail the current trend of social networking-based learning. It then provides a novel framework that moves further away from digital learning technologies while incorporating a wide range of recent advances to provide solutions to future challenges. This approach incorporates machine learning to the student-modeling component, which also uses conceptual frameworks and pedagogical theories in order to further promote individualization and adaptivity in e-learning environments. Moreover, it examines error diagnosis, misconceptions, tailored testing and collaboration between students are examined and proposes new approaches for these modules. Sentiment analysis is also incorporated into the general framework, supporting personalized learning by considering the user's emotional state, and creating a user-friendly learning environment tailored to students' needs. Support for students, in the form of motivation, completes the framework. This book helps researchers in the field of knowledge-based software engineering to build more sophisticated personalized educational software, while retaining a high level of adaptivity and user-friendliness within human-computer interactions. Furthermore, it is a valuable resource for educators and software developers designing and implementing intelligent tutoring systems and adaptive educational hypermedia systems. .
988			_aPrimersemestre_2020_Robotics
650		7	_2embne _aInnovaciones educativas _9158536
650		7	_2embne _aProceso de datos _9141180
700	1		_aVirvou, Maria _eautor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _9100284
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783030391294
776	0	8	_iPrinted edition: _z9783030391317
776	0	8	_iPrinted edition: _z9783030391324
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-39130-0 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE _n0
998			_b03/2020 _dz _eb _zSI