Predicting student dropout in subscription-based ...
Type de document :
Compte-rendu et recension critique d'ouvrage
Titre :
Predicting student dropout in subscription-based online learning environments: The beneficial impact of the logit leaf model
Auteur(s) :
Coussement, Kristof [Auteur]
Phan, Minh [Auteur]
Lille économie management - UMR 9221 [LEM]
De Caigny, Arno [Auteur]
Lille économie management - UMR 9221 [LEM]
Benoit, Dries [Auteur]
Raes, Annelies [Auteur]
Phan, Minh [Auteur]
Lille économie management - UMR 9221 [LEM]
De Caigny, Arno [Auteur]
Lille économie management - UMR 9221 [LEM]
Benoit, Dries [Auteur]
Raes, Annelies [Auteur]
Titre de la revue :
Decision Support Systems
Pagination :
113325
Éditeur :
Elsevier
Date de publication :
2020-08
ISSN :
0167-9236
Mot(s)-clé(s) en anglais :
Learning analytics
Proactive student management
Subscription-based online learning
Student dropout
Logit leaf model
Machine learning
Proactive student management
Subscription-based online learning
Student dropout
Logit leaf model
Machine learning
Résumé en anglais : [en]
Online learning has been adopted rapidly by educational institutions and organizations. Despite its many advantages, including 24/7 access, high flexibility, rich content, and low cost, online learning suffers from high ...
Lire la suite >Online learning has been adopted rapidly by educational institutions and organizations. Despite its many advantages, including 24/7 access, high flexibility, rich content, and low cost, online learning suffers from high dropout rates that hamper pedagogical and economic goal outcomes. Enhanced student dropout prediction tools would help providers proactively detect students at risk of leaving and identify factors that they might address to help students continue their learning experience. Therefore, this study seeks to improve student dropout predictions, with three main contributions. First, it benchmarks a recently proposed logit leaf model (LLM) algorithm against eight other algorithms, using a real-life data set of 10,554 students of a global subscription-based online learning provider. The LLM outperforms all other methods in finding a balance between predictive performance and comprehensibility. Second, a new multilevel informative visualization of the LLM adds novel benefits, relative to a standard LLM visualization. Third, this research specifies the impacts of student demographics; classroom characteristics; and academic, cognitive, and behavioral engagement variables on student dropout. In reviewing LLM segments, these results show that different insights emerge for various student segments with different learning patterns. This notable result can be used to personalize student retention campaigns.Lire moins >
Lire la suite >Online learning has been adopted rapidly by educational institutions and organizations. Despite its many advantages, including 24/7 access, high flexibility, rich content, and low cost, online learning suffers from high dropout rates that hamper pedagogical and economic goal outcomes. Enhanced student dropout prediction tools would help providers proactively detect students at risk of leaving and identify factors that they might address to help students continue their learning experience. Therefore, this study seeks to improve student dropout predictions, with three main contributions. First, it benchmarks a recently proposed logit leaf model (LLM) algorithm against eight other algorithms, using a real-life data set of 10,554 students of a global subscription-based online learning provider. The LLM outperforms all other methods in finding a balance between predictive performance and comprehensibility. Second, a new multilevel informative visualization of the LLM adds novel benefits, relative to a standard LLM visualization. Third, this research specifies the impacts of student demographics; classroom characteristics; and academic, cognitive, and behavioral engagement variables on student dropout. In reviewing LLM segments, these results show that different insights emerge for various student segments with different learning patterns. This notable result can be used to personalize student retention campaigns.Lire moins >
Langue :
Anglais
Vulgarisation :
Non
Collections :
Source :