1. bookVolume 8 (2016): Issue 2 (December 2016)
Journal Details
License
Format
Journal
eISSN
2037-0849
First Published
16 Apr 2015
Publication timeframe
2 times per year
Languages
English
access type Open Access

Think big: learning contexts, algorithms and data science

Published Online: 20 Jan 2017
Volume & Issue: Volume 8 (2016) - Issue 2 (December 2016)
Page range: 69 - 83
Journal Details
License
Format
Journal
eISSN
2037-0849
First Published
16 Apr 2015
Publication timeframe
2 times per year
Languages
English
Abstract

Due to the increasing growth in available data in recent years, all areas of research and the managements of institutions and organisations, specifically schools and universities, feel the need to give meaning to this availability of data. This article, after a brief reference to the definition of big data, intends to focus attention and reflection on their type to proceed to an extension of their characterisation. One of the hubs to make feasible the use of Big Data in operational contexts is to give a theoretical basis to which to refer. The Data, Information, Knowledge and Wisdom (DIKW) model correlates these four aspects, concluding in Data Science, which in many ways could revolutionise the established pattern of scientific investigation. The Learning Analytics applications on online learning platforms can be tools for evaluating the quality of teaching. And that is where some problems arise. It becomes necessary to handle with care the available data. Finally, a criterion for deciding whether it makes sense to think of an analysis based on Big Data can be to think about the interpretability and relevance in relation to both institutional and personal processes.

Keywords

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