Understanding and Using Big Data for Educational Management

Lars Nellemann, Peter Nordestgaard, Lone Dirckinck-Holmfeld

Research output: Contribution to book/anthology/report/conference proceedingBook chapterResearchpeer-review

1 Citation (Scopus)
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Abstract

This chapter explores how elements of Big Data can be used to develop pedagogical and didactical management and to establish practices for creating shared understanding and meaning in educational organizations through constructive use of such data. The chapter builds on three sources: Heidegger’s ontology of data as created and interpreted by humans; Wenger’s conception of how humans participate and create meaning and identity through communities of practice; and Nonaka’s idea of dialogue and openness in a free and constructive form of Ba. Based on a data report developed by the authors, the study describes a workshop for a university educational management group informed by the principles of Ba. As well as highlighting some of the challenges of using Big Data, the analysis documents how this approach can illuminate the “black box” of pedagogical management within a systematic and dialogical workshop setting. In line with the Heideggerian perspective, the data signified learning platform actions to be discussed, interpreted, and assigned meaning in a collaborative process with teachers and stewards of IT pedagogy. The chapter sets out guiding principles for the use of Big Data for pedagogical development and management through systematic dialogue within this boundary-crossing setup.

Original languageEnglish
Title of host publicationRadical Solutions and Learning Analytics
EditorsDaniel Burgos
Number of pages19
Place of PublicationSpringer, Singapore
PublisherSpringer
Publication dateMay 2020
Pages193-211
Chapter12
ISBN (Print)978-981-15-4525-2
ISBN (Electronic)978-981-15-4526-9
DOIs
Publication statusPublished - May 2020
SeriesLecture Notes in Educational Technology
ISSN2196-4963

Keywords

  • Ba
  • Communities of practice
  • Educational management
  • Big Data
  • Phenomenology

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