Distributional regression modeling via generalized additive models for location, scale, and shape: An overview through a data set from learning analytics. (21st October 2022)

• Record Type: Journal Article
• Authors: Marmolejo‐Ramos, Fernando; Tejo, Mauricio; Brabec, Marek; Kuzilek, Jakub; Joksimovic, Srecko; Kovanovic, Vitomir; González, Jorge; Kneib, Thomas; Bühlmann, Peter; Kook, Lucas; Briseño‐Sánchez, Guillermo; Ospina, Raydonal
• Is Part Of: Wiley interdisciplinary reviews. Volume 13:Number 1(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-10-21
• Subjects: causal regularization -- causality -- educational data mining -- generalized additive models for location, scale, and shape -- learning analytics -- machine learning -- statistical learning -- statistical modeling -- supervised learning; Data mining -- Periodicals; 006.31205
• DOI: 10.1002/widm.1479
• Languages: English
• ISSNs: 1942-4787

Abstract

Abstract: 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 Abstract : Performance of GAMLSS against some machine learning algorithms.