Learning Analytics in R with SNA, LSA, and MPIA

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.

Table of Contents

Chapter 1: Introduction
Chapter 2: Learning Theory and Algorithmic Quality Characteristics
Chapter 3: Representing and Analysing Purposiveness with SNA
Chapter 4: Representing and Analysing Meaning with LSA
Chapter 5: Meaningful, Purposive Interaction Analysis
Chapter 6: Visual Analytics Using Vector Maps as Projection Surfaces
Chapter 7: Calibrating for Specific Domains
Chapter 8: Implementation: The MPIA Package
Chapter 9: MPIA in Action: Example Learning Analytics
Chapter 10: Evaluation
Chapter 11: Conclusion and Outlook
Annex A: Classes and Methods of the mpia Package