Learning analytics for professional and workplace learning: A literature review

Adolfo Ruiz-Calleja*, Luis P. Prieto, Tobias Ley, María Jesús Rodríguez-Triana, Sebastian Dennerlein

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Despite the ubiquity of learning in the everyday life of most workplaces, the learning analytics community only has paid attention to such settings very recently. One probable reason for this oversight is the fact that learning in the workplace is often informal, hard to grasp and not univocally defined. This paper summarizes the state of the art of Workplace Learning Analytics (WPLA), extracted from a systematic literature review of five academic databases as well as other known sources in the WPLA community. Our analysis of existing proposals discusses particularly on the role of different conceptions of learning and their influence on the LA proposals’ design and technology choices. We end the paper by discussing opportunities for future work in this emergent field.

Original languageEnglish
Title of host publicationData Driven Approaches in Digital Education - 12th European Conference on Technology Enhanced Learning, EC-TEL 2017, Proceedings
PublisherSpringer Verlag Heidelberg
Pages164-178
Number of pages15
Volume10474 LNCS
ISBN (Print)9783319666099
DOIs
Publication statusPublished - 2017
Event12th European Conference on Technology Enhanced Learning, EC-TEL 2017 - Tallinn, Estonia
Duration: 12 Sep 201715 Sep 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10474 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference12th European Conference on Technology Enhanced Learning, EC-TEL 2017
CountryEstonia
CityTallinn
Period12/09/1715/09/17

Keywords

  • Learning analytics
  • Learning metaphors
  • Professional development
  • Workplace learning

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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