Utilizing random forest algorithm for early detection of academic underperformance in open learning environments

One of the primary benefits of Open Learning Environments (OLEs) is their scalability. OLEs provide flexible and accessible learning opportunities to a large number of students, often on a global scale. This scalability has led to the development of OLEs that cover a wide range of subjects and disci...

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Veröffentlicht in:	PeerJ. Computer science Jg. 9; S. e1708
Hauptverfasser:	Balabied, Shikah Abdullah Albriki, Eid, Hala F.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States PeerJ. Ltd 22.11.2023 PeerJ, Inc PeerJ Inc
Schlagworte:	Academic achievement Academic failure Accuracy Algorithms Analysis At risk students Behavior Colleges & universities Computer science Datasets Decision trees Forecasts and trends Learning analytics Learning management systems Machine learning Markov analysis MOOCs Neural networks Online instruction Open learning Open learning environments OULAD Performance evaluation Random forest algorithm Students Success OULAD Learning analytics Open learning environments Random forest algorithm MOOCs
ISSN:	2376-5992, 2376-5992
Online-Zugang:	Volltext
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