An Infrastructure for Workplace Learning Analytics: Tracing Knowledge Creation with the Social Semantic Server

Authors

  • Adolfo Ruiz Calleja Tallinn University
  • Sebastian Dennerlein Graz University of Technology
  • Dominik Kowald Graz University of Technology
  • Dieter Theiler Graz University of Technology
  • Elisabeth Lex Graz University of Technology
  • Tobias Ley Tallinn University

DOI:

https://doi.org/10.18608/jla.2019.62.9

Keywords:

Learning Analytics, informal learning, workplace learning, Artifact-Actor Network, data infrastructure

Abstract

In this paper, we propose the Social Semantic Server (SSS) as a service-based infrastructure for workplace and professional learning analytics (LA). The design and development of the SSS have evolved over eight years, starting with an analysis of workplace learning inspired by knowledge creation theories and their application in different contexts. The SSS collects data from workplace learning tools, integrates it into a common data model based on a semantically enriched artifact-actor network, and offers it back for LA applications to exploit the data. Further, the SSS design’s flexibility enables it to be adapted to different workplace learning situations. This paper contributes by systematically deriving requirements for the SSS according to knowledge creation theories, and by offering support across a number of different learning tools and LA applications integrated into the SSS. We also show evidence for the usefulness of the SSS extracted from 4 authentic workplace learning situations involving 57 participants. The evaluation results indicate that the SSS satisfactorily supports decision making in diverse workplace learning situations and allow us to reflect on the importance of knowledge creation theories for this analysis.

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Published

2019-07-23

How to Cite

Ruiz Calleja, A., Dennerlein, S., Kowald, D., Theiler, D., Lex, E., & Ley, T. (2019). An Infrastructure for Workplace Learning Analytics: Tracing Knowledge Creation with the Social Semantic Server. Journal of Learning Analytics, 6(2), 120–139. https://doi.org/10.18608/jla.2019.62.9