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

• Published in: Algorithms Vol. 14; no. 11; p. 318
• Main Authors: Huynh-Cam, Thao-Trang, Chen, Long-Sheng, Le, Huynh
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.11.2021
• Subjects: Academic achievement; Algorithms; Artificial intelligence; At risk students; College students; Colleges & universities; Data mining; Decision making; decision tree (DT); Decision trees; Economic factors; Education; feature selection; Gender; Learning; Machine learning; Multilayer perceptrons; Performance prediction; prediction model; Prediction models; random forest (RF); Schools; Students; students’ learning performance; technological and vocational education; Tuition; University students; Taiwan
• ISSN: 1999-4893; 1999-4893
• DOI: 10.3390/a14110318

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...