Using Decision Trees and Random Forest Algorithms to Predict and Determine Factors Contributing to First-Year University Students’ Learning Performance

Using Decision Trees and Random Forest Algorithms to Predict and Determine Factors Contributing to First-Year University Students’ Learning Performance

First-year students’ learning performance has received much attention in educational practice and theory. Previous works used some variables, which should be obtained during the course or in the progress of the semester through questionnaire surveys and interviews, to build prediction models. These...

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Enregistré dans:
Détails bibliographiques
Auteurs principaux: Thao-Trang Huynh-Cam, Long-Sheng Chen, Huynh Le
Format: article
Langue:EN
Publié: MDPI AG 2021
Sujets:
students’ learning performance
prediction model
random forest (RF)
decision tree (DT)
feature selection
technological and vocational education
Industrial engineering. Management engineering
T55.4-60.8
Electronic computers. Computer science
QA75.5-76.95
Accès en ligne:https://doaj.org/article/b2e7c55f69b846f0ad6e1ec1a64bc336
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https://doaj.org/article/b2e7c55f69b846f0ad6e1ec1a64bc336

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