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Learning Analytics in R with SNA, LSA, and MPIA [electronic resource] / by Fridolin Wild.

By: Wild, Fridolin [author.].
Contributor(s): SpringerLink (Online service).
Material type: materialTypeLabelBookPublisher: Cham : Springer International Publishing : Imprint: Springer, 2016Description: XV, 275 p. 106 illus., 59 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783319287911.Subject(s): Computer science | Language and languages -- Philosophy | Data mining | Mathematics | Social sciences | Computational linguistics | Educational technology | Computer Science | Data Mining and Knowledge Discovery | Computational Linguistics | Mathematics in the Humanities and Social Sciences | Educational Technology | Philosophy of LanguageAdditional physical formats: Printed edition:: No titleDDC classification: 006.312 Online resources: Click here to access online
Contents:
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.
In: Springer eBooksSummary: 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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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.

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