Predicting Student Dropout in Self-Paced MOOC Course Using Random Forest Model
A significant problem in Massive Open Online Courses (MOOCs) is the high rate of student dropout in these courses. An effective student dropout prediction model of MOOC courses can identify the factors responsible and provide insight on how to initiate interventions to increase student success in a...
Guardado en:
Autores principales: | Sheran Dass, Kevin Gary, James Cunningham |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
MDPI AG
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/0f84ee38ea5e4165935ee7d901116b3e |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Analysis of the MOOC Capabilities for Student Training in the Humanities
por: Sergii Sharov, et al.
Publicado: (2021) -
Analisa Performa Penggunaan Feature Selection untuk Mendeteksi Intrusion Detection Systems dengan Algoritma Random Forest Classifier
por: Setiawan Budiman, et al.
Publicado: (2021) -
THE EXPERT SYSTEM OF CONTROL AND KNOWLEDGE ASSESSMENT
por: V. Golovachyova, et al.
Publicado: (2020) -
Pattern Recognition of Human Face With Photos Using KNN Algorithm
por: Dedy Kurniadi, et al.
Publicado: (2021) -
Optimization and improvement of fake news detection using deep learning approaches for societal benefit
por: Tavishee Chauhan, M.E, et al.
Publicado: (2021)