Advanced search
1 file | 1.10 MB Add to list

Predicting student dropout in subscription-based online learning environments : the beneficial impact of the logit leaf model

Author
Organization
Abstract
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.
Keywords
Arts and Humanities (miscellaneous), Information Systems and Management, Management Information Systems, Developmental and Educational Psychology, Information Systems, Learning analytics, Proactive student management, Subscription-based online learning, Student dropout, Logit leaf model, Machine learning, CUSTOMER CHURN PREDICTION, LOGISTIC-REGRESSION, CLASSIFICATION, PERFORMANCE, RETENTION, DECISION

Downloads

  • (...).pdf
    • full text (Published version)
    • |
    • UGent only
    • |
    • PDF
    • |
    • 1.10 MB

Citation

Please use this url to cite or link to this publication:

MLA
Coussement, Kristof, et al. “Predicting Student Dropout in Subscription-Based Online Learning Environments : The Beneficial Impact of the Logit Leaf Model.” DECISION SUPPORT SYSTEMS, vol. 135, 2020, doi:10.1016/j.dss.2020.113325.
APA
Coussement, K., Phan, M., De Caigny, A., Benoit, D., & Raes, A. (2020). Predicting student dropout in subscription-based online learning environments : the beneficial impact of the logit leaf model. DECISION SUPPORT SYSTEMS, 135. https://doi.org/10.1016/j.dss.2020.113325
Chicago author-date
Coussement, Kristof, Minh Phan, Arno De Caigny, Dries Benoit, and Annelies Raes. 2020. “Predicting Student Dropout in Subscription-Based Online Learning Environments : The Beneficial Impact of the Logit Leaf Model.” DECISION SUPPORT SYSTEMS 135. https://doi.org/10.1016/j.dss.2020.113325.
Chicago author-date (all authors)
Coussement, Kristof, Minh Phan, Arno De Caigny, Dries Benoit, and Annelies Raes. 2020. “Predicting Student Dropout in Subscription-Based Online Learning Environments : The Beneficial Impact of the Logit Leaf Model.” DECISION SUPPORT SYSTEMS 135. doi:10.1016/j.dss.2020.113325.
Vancouver
1.
Coussement K, Phan M, De Caigny A, Benoit D, Raes A. Predicting student dropout in subscription-based online learning environments : the beneficial impact of the logit leaf model. DECISION SUPPORT SYSTEMS. 2020;135.
IEEE
[1]
K. Coussement, M. Phan, A. De Caigny, D. Benoit, and A. Raes, “Predicting student dropout in subscription-based online learning environments : the beneficial impact of the logit leaf model,” DECISION SUPPORT SYSTEMS, vol. 135, 2020.
@article{8663579,
  abstract     = {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.},
  articleno    = {113325},
  author       = {Coussement, Kristof and Phan, Minh and De Caigny, Arno and Benoit, Dries and Raes, Annelies},
  issn         = {0167-9236},
  journal      = {DECISION SUPPORT SYSTEMS},
  keywords     = {Arts and Humanities (miscellaneous),Information Systems and Management,Management Information Systems,Developmental and Educational Psychology,Information Systems,Learning analytics,Proactive student management,Subscription-based online learning,Student dropout,Logit leaf model,Machine learning,CUSTOMER CHURN PREDICTION,LOGISTIC-REGRESSION,CLASSIFICATION,PERFORMANCE,RETENTION,DECISION},
  language     = {eng},
  pages        = {11},
  title        = {Predicting student dropout in subscription-based online learning environments : the beneficial impact of the logit leaf model},
  url          = {http://dx.doi.org/10.1016/j.dss.2020.113325},
  volume       = {135},
  year         = {2020},
}

Altmetric
View in Altmetric
Web of Science
Times cited: