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

Format:

Book

Author/Creator:

Wild, Fridolin, author.

Contributor:

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

Language:

English

Subjects (All):

Data mining.

Computational linguistics.

Mathematics.

Social sciences.

Educational technology.

Language and languages--Philosophy.

Language and languages.

Data Mining and Knowledge Discovery.

Computational Linguistics.

Mathematics in the Humanities and Social Sciences.

Educational Technology.

Philosophy of Language.

Local Subjects:

Data Mining and Knowledge Discovery.

Computational Linguistics.

Mathematics in the Humanities and Social Sciences.

Educational Technology.

Philosophy of Language.

Physical Description:

1 online resource (XV, 275 pages) : 106 illustrations, 59 illustrations in color

Edition:

First edition 2016.

Contained In:

Springer eBooks

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2016.

System Details:

text file PDF

Summary:

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.

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.

Other Format:

Printed edition:

ISBN:

978-3-319-28791-1

9783319287911

Access Restriction:

Restricted for use by site license.