The Bias-Variance Tradeoff: How Data Science Can Inform Educational Debates

Abstract

In addition to providing a set of techniques to analyze educational data, I claim that data science as a field can provide broader insights to education research. In particular, I show how the bias-variance tradeoff from machine learning can be formally generalized to be applicable to several prominent educational debates, including debates around learning theories (cognitivist vs. situativist and constructivist theories) and pedagogy (direct instruction vs. discovery learning). I then look to see how various data science techniques that have been proposed to navigate the bias-variance tradeoff can yield insights for productively navigating these educational debates going forward.