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Learning analytics in R with SNA, LS...

Wild, Fridolin.

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Learning analytics in R with SNA, LSA, and MPIA

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Learning analytics in R with SNA, LSA, and MPIA/ by Fridolin Wild.
作者:	Wild, Fridolin.
出版者:	Cham :Springer International Publishing : : 2016.,
面頁冊數:	xiii, 275 p. :ill. (some col.), digital ;24 cm.
內容註:	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.
Contained By:	Springer eBooks
標題:	Data mining. -
電子資源:	http://dx.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. - Cham :Springer International Publishing :2016. - xiii, 275 p. :ill. (some col.), digital ;24 cm.

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:

562972
Data mining.

LC Class. No.: QA76.9.D343

Dewey Class. No.: 006.312

Learning analytics in R with SNA, LSA, and MPIA
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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.
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