Distributional regression analysis of learning analytics and educational data

The advent of technological developments is allowing gathering large amounts of data in several research fields. Learning analytics/educational data mining (LA/EDM) has access to big observational unstructured data captured from educational settings and relies mostly on unsupervised machine learning algorithms to make sense of such type of data. Generalised additive models of location, scale and shape (GAMLSS) are supervised statistical learning approaches that allow modelling all the parameters of the distribution of the response variable w.r.t. the explanatory variables. This article briefly introduces the power and flexibility of GAMLSS to the LA/EDM community in order to prompt a distributional and interpretable statistical learning of data.