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This book introduces Meaningful Purposive Interaction Analysis (MPIA) theory, which merges social network analysis with latent semantic analysis to help create a meaningful learning landscape out of the traces left by a learning community. It then allows us to use a learner’s digital ‘analytic traces’ to track their journey through that mapped landscape. Learning is the social co-construction of knowledge – working with yourself and with others.
The hybrid algorithm is implemented in the modeling language R and has packages which capture elements of learners’ work with ‘more knowledgeable others’ and ‘resourceful content artefacts’ via matrix algebra algorithms. The book provides comprehensive package–by-package application examples, and code samples which guide the reader through the MPIA model to show how the MPIA landscape can be constructed and the learner’s journey mapped. It is envisaged that this building block application will allow the reader to progress to use this construct to build further analytic packages to provide student guidance and support further learning decision-making.