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Introduction

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Learning Analytics in R with SNA, LSA, and MPIA

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Abstract

This book presents new ways to automatically represent conceptual development evident from interaction of learners with content. It introduces novel instruments for further analysis and visualization, therewith supporting guidance and decision-making about and during learning. Within this introduction, the scope of the book is further refined. An introduction to the learning technology software market is followed by a characterization of the research challenge faced and of the wider research area in which the solution presented in this book is situated. Three roots of the work are introduced in this chapter to set the stage: epistemic theory, algorithms for representation/visualization/analysis, and, finally, the application area of Learning Analytics. The chapter is rounded up by an overview over the book.

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Notes

  1. 1.

    http://teleurope.eu and http://www.stellarnet.eu/

  2. 2.

    The Grand Challenge descriptions extracted from the STELLAR Alpine Rendezvous.

  3. 3.

    For the appearance in print in Fischer et al. (2014), the Grand Challenge problem descriptions had to be shortened in order to fit the book into the SpringerBriefs in Education format: this means that Whitelock (2014a) is a shortened version of Whitelock (2012a); and Whitelock (2014b) is a shortened version of Whitelock (2012b).

  4. 4.

    See also Chaps. 3–7 for details and Chap. 2 for conceptual clarification.

  5. 5.

    http://bit.ly/R4V6KN

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  1. Performance Augmentation Lab Department of Computing and Communication Technologies, Oxford Brookes University, Oxford, UK

    Fridolin Wild

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Wild, F. (2016). Introduction. In: Learning Analytics in R with SNA, LSA, and MPIA. Springer, Cham. https://doi.org/10.1007/978-3-319-28791-1_1

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