Approximation of statistical implicative analysis to learning analytics: a systematic review

Abstract

The learning Analytics has been and is still an emerging technology, the amount of research on learning analysis are increasing every day. The integration of new tools, methods and theories is necessary. The aim of this paper is to study the approximation of Statistical Implicative Analysis theory (SIA) to Learning Analytics (LA). To this end, we have created an approximation framework based on the definition, stages, and methods used in LA. In total, three criteria approach and thirty-six sub-themes were compared. We use systematic review in the literature published in the last 66 months in bibliographic database ACM, EBSCO, Google Scholar, IEEE, ProQuest, Scopus and WOS. We started with 319 papers and finally 24 met all quality criteria. This document provides the themes by which SIA approximates to LA, also provides the percentages by category approach and identifies a number of future researches.

DOI: 10.1145/3012430.3012540

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Cite this paper

@inproceedings{PazmioMaji2016ApproximationOS, title={Approximation of statistical implicative analysis to learning analytics: a systematic review}, author={Rub{\'e}n A. Pazmi{\~n}o-Maji and Francisco J. Garc{\'i}a-Pe{\~n}alvo and Miguel {\'A}ngel Conde Gonz{\'a}lez}, booktitle={TEEM}, year={2016} }