國立虎尾科技大學 |

Learning Analytics in R with SNA, LSA, and MPIA

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Learning Analytics in R with SNA, LSA, and MPIA/ by Fridolin Wild.
Author:	Wild, Fridolin.
Description:	XV, 275 p. 106 illus., 59 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Data mining. -
Online resource:	https://doi.org/10.1007/978-3-319-28791-1
ISBN:	9783319287911

Learning Analytics in R with SNA, LSA, and MPIA
Wild, Fridolin.

Learning Analytics in R with SNA, LSA, and MPIA[electronic resource] /by Fridolin Wild. - 1st ed. 2016. - XV, 275 p. 106 illus., 59 illus. in color.online resource.

Preface -- 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.

This 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.

ISBN: 9783319287911

Standard No.: 10.1007/978-3-319-28791-1doiSubjects--Topical Terms:

528622
Data mining.

LC Class. No.: QA76.9.D343

Dewey Class. No.: 006.312

Learning Analytics in R with SNA, LSA, and MPIA
LDR:02844nam a22004095i 4500 001 975303
003 DE-He213
005 20200629114845.0
007 cr nn 008mamaa
008 201211s2016 gw | s |||| 0|eng d
020 $a 9783319287911 $9 978-3-319-28791-1
024 7 $a 10.1007/978-3-319-28791-1 $2 doi
035 $a 978-3-319-28791-1
050 4 $a QA76.9.D343
072 7 $a UNF $2 bicssc
072 7 $a COM021030 $2 bisacsh
072 7 $a UNF $2 thema
072 7 $a UYQE $2 thema
082 0 4 $a 006.312 $2 23
100 1 $a Wild, Fridolin. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1107547
245 1 0 $a Learning Analytics in R with SNA, LSA, and MPIA $h [electronic resource] / $c by Fridolin Wild.
250 $a 1st ed. 2016.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2016.
300 $a XV, 275 p. 106 illus., 59 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
505 0 $a Preface -- 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 $a This 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 $a Data mining. $3 528622
650 0 $a Computational linguistics. $3 555811
650 0 $a Mathematics. $3 527692
650 0 $a Social sciences. $3 572679
650 0 $a Educational technology. $3 556755
650 0 $a Language and languages—Philosophy. $3 1254282
650 1 4 $a Data Mining and Knowledge Discovery. $3 677765
650 2 4 $a Computational Linguistics. $3 670080
650 2 4 $a Mathematics in the Humanities and Social Sciences. $3 885667
650 2 4 $a Educational Technology. $3 670011
650 2 4 $a Philosophy of Language. $3 671588
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783319287898
776 0 8 $i Printed edition: $z 9783319287904
776 0 8 $i Printed edition: $z 9783319804255
856 4 0 $u https://doi.org/10.1007/978-3-319-28791-1
912 $a ZDB-2-SCS
912 $a ZDB-2-SXCS
950 $a Computer Science (SpringerNature-11645)
950 $a Computer Science (R0) (SpringerNature-43710)