Distributional regression modeling via generalized additive models for location, scale, and shape: An overview through a data set from learning analytics
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F23%3A00563344" target="_blank" >RIV/67985807:_____/23:00563344 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21730/23:00361699
Result on the web
<a href="https://dx.doi.org/10.1002/widm.1479" target="_blank" >https://dx.doi.org/10.1002/widm.1479</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1002/widm.1479" target="_blank" >10.1002/widm.1479</a>
Alternative languages
Result language
angličtina
Original language name
Distributional regression modeling via generalized additive models for location, scale, and shape: An overview through a data set from learning analytics
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
10103 - Statistics and probability
Result continuities
Project
<a href="/en/project/GJ18-04150Y" target="_blank" >GJ18-04150Y: Predictive modeling of student performance using learning resources</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery
ISSN
1942-4787
e-ISSN
1942-4795
Volume of the periodical
13
Issue of the periodical within the volume
1
Country of publishing house
US - UNITED STATES
Number of pages
22
Pages from-to
e1479
UT code for WoS article
000870901000001
EID of the result in the Scopus database
2-s2.0-85140231241