Distributional regression modeling via generalized additive models for location, scale, and shape: An overview through a data set from learning analytics

The advent of technological developments is allowing to gather large amounts of data in several research fields. Learning analytics (LA)/educational data mining has access to big observational unstructured data captured from educational settings and relies mostly on unsupervised machine learning (ML) algorithms to make sense of such type of data. Generalized additive models for location, scale, and shape (GAMLSS) are a supervised statistical learning framework that allows modeling all the parameters of the distribution of the response variable with respect to the explanatory variables. This article overviews the power and flexibility of GAMLSS in relation to some ML techniques. Also, GAMLSS' capability to be tailored toward causality via causal regularization is briefly commented. This overview is illustrated via a data set from the field of LA. This article is categorized under: Application Areas > Education and Learning Algorithmic Development > Statistics Technologies > Machine Learning

Automated summary

The advancement in technology has enabled the collection of large amounts of data across various research fields. Learning analytics (LA)/educational data mining utilizes unsupervised machine learning (ML) algorithms to analyze the vast amount of unstructured observational data captured from educational settings. Generalized additive models for location, scale, and shape (GAMLSS) is a supervised statistical learning framework that offers flexibility and power in modeling the parameters of response variables based on explanatory variables. This article provides an overview of GAMLSS in comparison to other ML techniques, highlighting its potential for causal regularization. The article illustrates its application using a data set from the field of LA.