000			03255nam a22005415i 4500
001			978-3-319-28791-1
003			DE-He213
005			20200421112222.0
007			cr nn 008mamaa
008			160404s2016 gw | s |||| 0|eng d
020			_a9783319287911 _9978-3-319-28791-1
024	7		_a10.1007/978-3-319-28791-1 _2doi
050		4	_aQA76.9.D343
072		7	_aUNF _2bicssc
072		7	_aUYQE _2bicssc
072		7	_aCOM021030 _2bisacsh
082	0	4	_a006.312 _223
100	1		_aWild, Fridolin. _eauthor.
245	1	0	_aLearning Analytics in R with SNA, LSA, and MPIA _h[electronic resource] / _cby Fridolin Wild.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2016.
300			_aXV, 275 p. 106 illus., 59 illus. in color. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
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			_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' 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'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.
650		0	_aComputer science.
650		0	_aLanguage and languages _xPhilosophy.
650		0	_aData mining.
650		0	_aMathematics.
650		0	_aSocial sciences.
650		0	_aComputational linguistics.
650		0	_aEducational technology.
650	1	4	_aComputer Science.
650	2	4	_aData Mining and Knowledge Discovery.
650	2	4	_aComputational Linguistics.
650	2	4	_aMathematics in the Humanities and Social Sciences.
650	2	4	_aEducational Technology.
650	2	4	_aPhilosophy of Language.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783319287898
856	4	0	_uhttp://dx.doi.org/10.1007/978-3-319-28791-1
912			_aZDB-2-SCS
942			_cEBK
999			_c57464 _d57464