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

Coussement, Kristof; Phan, Minh Hieu; Caigny, Arno De; Benoit, Dries F.; Raes, Annelies

doi:10.1016/j.dss.2020.113325

Cited by 80 publications

(63 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In general, these findings are in line with findings of previous studies on dropout rates from online courses before the outbreak of the COVID-19 pandemic [ [11] , [12] , [13] , [21] , [22] , [23] , [24] , [25] , [26] , [27] , [28] , [29] , [30] ]. However, these studies primarily considered students who studied using online learning environments on their own and not within a class-context (i.e., without a student-teacher interaction).…”

Section: Discussionsupporting

confidence: 91%

Evaluating students’ engagement with an online learning environment during and after COVID-19 related school closures: A survival analysis approach

Spitzer

Gutsfeld

Wirzberger

et al. 2021

Trends in Neuroscience and Education

View full text Add to dashboard Cite

Section: Discussionsupporting

confidence: 91%

Evaluating students’ engagement with an online learning environment during and after COVID-19 related school closures: A survival analysis approach

Spitzer

Gutsfeld

Wirzberger

et al. 2021

Trends in Neuroscience and Education

View full text Add to dashboard Cite

“…Neural network [15,18,19,24] No retraining is required because it learns events; it is applicable to real-world issues, and there are few parameters to alter, making it simple to use.…”

mentioning

confidence: 99%

[Retracted] Enhancement of Predicting Students Performance Model Using Ensemble Approaches and Educational Data Mining Techniques

Ragab

Aal

Jifri

et al. 2021

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

Student performance prediction is extremely important in today’s educational system. Predicting student achievement in advance can assist students and teachers in keeping track of the student’s progress. Today, several institutes have implemented a manual ongoing evaluation method. Students benefit from such methods since they help them improve their performance. In this study, we can use educational data mining (EDM), which we recommend as an ensemble classifier to anticipate the understudy accomplishment forecast model based on data mining techniques as classification techniques. This model uses distinct datasets which represent the student’s intercommunication with the instructive model. The exhibition of an understudy’s prescient model is evaluated by a kind of classifiers, for instance, logistic regression, naïve Bayes tree, artificial neural network, support vector system, decision tree, random forest, and k -nearest neighbor. Additionally, we used set processes to evolve the presentation of these classifiers. We utilized Boosting, Random Forest, Bagging, and Voting Algorithms, which are the normal group of techniques used in studies. By using ensemble methods, we will have a good result that demonstrates the dependability of the proposed model. For better productivity, the various classifiers are gathered and, afterward, added to the ensemble method using the Vote procedure. The implementation results demonstrate that the bagging method accomplished a cleared enhancement with the DT model, where the DT algorithm accuracy with bagging increased from 90.4% to 91.4%. Recall results improved from 0.904 to 0.914. Precision results also increased from 0.905 to 0.915.

show abstract

“…Other studies have also reported that online classes have experienced a large increase in dropouts and low attendance [42]. However, the use of students' dropout detection tools would allow educational institutions to identify students who are at risk of dropping out and highlight variables that may be addressed to help students complete their education [26].…”

Section: Fig 1 Number Of Studies On Benefits Of Learning Analytics To Higher Education Institutionsmentioning

confidence: 99%

The Benefits of Learning Analytics to Higher Education Institutions: A Scoping Review

Quadri

Shukor

2021

Int. J. Emerg. Technol. Learn.

View full text Add to dashboard Cite

Learning analytics is a form of data analysis that allows teachers, lecturers, educational experts, and administrators of online learnings to look for students’ online traces and information associated with the learning processes. The fundamental goal of learning analytics in online classrooms and com-puter-supported instruction is to enhance the learning experience and the entire learning process. This review aims at reviewing some of the benefits available through using learning analytics in higher education institutions (HEI) for the students, teaching staff and the management. The search for relevant literature was conducted by searching online databases which in-clude Web of Science, SCOPUS, Science Direct, IEEE, Emerald, Springer, ERIC and Association for Computing Machinery (ACM). The analysis of the literatures obtained from the online databases revealed that learning analytics provide series of benefits to students, teaching staffs and the management of higher education institutions. The benefits include prediction and identification of target courses, curriculum development and improvement, improved students’ learning outcomes, improved instructors’ performance and monitoring of students’ dropout and retention. It is recommended that higher education institutions adopt the use of learning analytics in their online teaching and learning.

show abstract

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

Cited by 80 publications

References 32 publications

Evaluating students’ engagement with an online learning environment during and after COVID-19 related school closures: A survival analysis approach

Evaluating students’ engagement with an online learning environment during and after COVID-19 related school closures: A survival analysis approach

[Retracted] Enhancement of Predicting Students Performance Model Using Ensemble Approaches and Educational Data Mining Techniques

The Benefits of Learning Analytics to Higher Education Institutions: A Scoping Review

Contact Info

Product

Resources

About