000			03022nam a22003735i 4500
001			85502
003			ES-MaUEC
005			20230207040514.0
007			cr nn 008mamaa
008			160404s2016 gw | s |||| 0|eng d
020			_a9783319287911
040			_aES-MaUEC
050		4	_aQA76.9.D343 _bW553 2016 EB
082	0	4	_a006.312
100	1		_aWild, Fridolin _998510 _0Local
245	1	0	_aLearning Analytics in R with SNA, LSA, and MPIA _cby Fridolin Wild
260			_aCham _bSpringer International Publishing _c2016
300			_a1 recurso en línea (XV, 275 p.) _b106 ilustraciones, 59 ilustraciones en color
336			_aTexto (visual) _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
505	0		_aPreface -- 1.Introduction -- 2.Learning Theory and Algorithmic Quality Characteristics -- 3.Representing and Analysing Purposiveness with SNA -- 4.Representing and Analysing Meaning with LSA -- 5.Meaningful, Purposive Interaction Analysis -- 6.Visual Analytics Using Vector Maps as Projection Surfaces -- 7.Calibrating for Specific Domains -- 8.Implementation: The MPIA Package -- 9.MPIA in Action: Example Learning Analytics -- 10.Evaluation -- 11.Conclusion and Outlook -- Annex A: Classes and Methods of the MPIA Package.
520	3		_aThis book introduces Meaningful Purposive Interaction Analysis (MPIA) theory, which combines social network analysis (SNA) with latent semantic analysis (LSA) to help create and analyse a meaningful learning landscape from the digital traces left by a learning community in the co-construction of knowledge. The hybrid algorithm is implemented in the statistical programming language and environment R, introducing packages which capture - through matrix algebra - elements of learners{u2019} work with more knowledgeable others and resourceful content artefacts. The book provides comprehensive package-by-package application examples, and code samples that guide the reader through the MPIA model to show how the MPIA landscape can be constructed and the learner{u2019}s journey mapped and analysed. This building block application will allow the reader to progress to using and building analytics to guide students and support decision-making in learning
710	2		_aSpringerLink (Online service) _0Local _9106996
942			_2lcc _cLE
988			_aEBOOK, EBSPRINGER
650		7	_aData mining _0comprobar BNE20033218554 _2embne _9162648
650		7	_aR (Lenguaje de programación) _9164496 _0comprobar BNE20050339195 _2embne
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://link.springer.com/book/10.1007/978-3-319-28791-1 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
901			_ai9783319287911
907			_a.b12947805 _b10-10-17 _c21-11-16
998			_am _a_alco _a_vill _b11-07-17 _cm _dz _ek _feng _ggw _h0
945			_aQA76.9.D343 W553 2016 EB _g1 _ieBOOK _j0 _lmae _o- _pEUR0.00 _q- _r- _sb _t15 _u0 _v0 _w0 _x0 _y.i11591328 _z06-04-17
999			_c85502 _d85502 _x1