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001			397978
003			ES-MaUEC
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007			cr nn 008mamaa
008			230429s2023 si | o |1|| 0|eng d
020			_a9789819900268
024	7		_a10.1007/978-981-99-0026-8 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ336 _b2023 EB
245	0	0	_aEducational Data Science : _bEssentials, Approaches, and Tendencies : Proactive Education based on Empirical Big Data Evidence _cedited by Alejandro Peña-Ayala
250			_a1st ed 2023
264		1	_aSingapore _bSpringer Nature _c2023
300			_a1 recurso en línea
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	0		_aBig Data Management _x2522-0187
505	0		_a1. Engaging in Student-Centered Educational Data Science through Learning Engineering -- 2. A review of clustering models in educational data science towards fairness-aware learning -- 3. Educational Data Science: Is an "Umbrella Term" or an Emergent Domain? -- 4. Educational Data Science Approach for End-to-End Quality Assurance Process for Building Credit-Worthy Online Courses -- 5. Understanding the Effect of Cohesion in Academic Writing Clarity Using Education Data Science -- 6. Sequential pattern mining in educational data: the application context, potential, strengths, and limitations -- 7. Sync Ratio and Cluster Heat Map for Visualizing Student Engagement.
520			_aThis book describes theoretical elements, practical approaches, and specialized tools that systematically organize, characterize, and analyze big data gathered from educational affairs and settings. Moreover, the book shows several inference criteria to leverage and produce descriptive, explanatory, and predictive closures to study and understand education phenomena at in classroom and online environments. This is why diverse researchers and scholars contribute with valuable chapters to ground with well--sounded theoretical and methodological constructs in the novel field of Educational Data Science (EDS), which examines academic big data repositories, as well as to introduces systematic reviews, reveals valuable insights, and promotes its application to extend its practice. EDS as a transdisciplinary field relies on statistics, probability, machine learning, data mining, and analytics, in addition to biological, psychological, and neurological knowledge about learning science. With this in mind, the book is devoted to those that are in charge of educational management, educators, pedagogues, academics, computer technologists, researchers, and postgraduate students, who pursue to acquire a conceptual, formal, and practical landscape of how to deploy EDS to build proactive, real- time, and reactive applications that personalize education, enhance teaching, and improve learning!
988			_aSpringer_Computer_2023
650		7	_2embne _9413115 _aInteligencia artificial _xProceso de datos _vCongresos y asambleas
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-99-0026-8 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b01/2024 _dz _eb _zSI