Learning Analytics in R with SNA, LSA, and MPIA
Wild, Fridolin
Learning Analytics in R with SNA, LSA, and MPIA by Fridolin Wild - Cham Springer International Publishing 2016 - 1 recurso en línea (XV, 275 p.) 106 ilustraciones, 59 ilustraciones en color
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 learners 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
9783319287911
Data mining
R (Lenguaje de programación)
QA76.9.D343 / W553 2016 EB
006.312
Learning Analytics in R with SNA, LSA, and MPIA by Fridolin Wild - Cham Springer International Publishing 2016 - 1 recurso en línea (XV, 275 p.) 106 ilustraciones, 59 ilustraciones en color
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 learners 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
9783319287911
Data mining
R (Lenguaje de programación)
QA76.9.D343 / W553 2016 EB
006.312