Algorithmic Driven Decision-Making Systems in Education: Analyzing Bias from the Sociocultural Perspective

Abstract

This article analyzes biases in algorithmic driven decision-making systems in education considering some contributions from the Activity Theory of sociocultural tradition. First, it is identified how the sources of biases (theoretical, methodological, by interpretation, by decontextualization and by data training) are distributed in the elements of the analytical unit as well as in the systemic time. Consequently, the algorithm is not treated as a mediating artifact biased in itself, but biases are reflected in it and are linked to the practices carried out by the subjects involved in the systemic reality studied. Second, it is presented the results of a Systematic Literature Review that allows us to explore the ways in which the Journal of Learning Analytics community approaches the subject of biases according to the bias-sources classification previously constructed.