Eli Nimy
Biographic Data
| ID | 9980275 |
|---|---|
| NAME | Eli Nimy |
| GIVEN NAMES | Eli |
| FAMILY NAME | Nimy |
| SIGNATURE | NIMY E |
| AFFILIATIONS | Sol Plaatje University |
| ORCID | 0000-0002-8597-1548 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2023 |
| LATEST PUBLICATION YEAR | 2024 |
| H-INDEX | 0 |
Modelling Student Retention in Tutorial Classes with Uncertainty—A Bayesian Approach to Predicting Attendance-Based Retention
A Bayesian additive regression tree (BART) is a recent statistical method that blends ensemble learning with nonparametric regression. BART is constructed using a Bayesian approach, which provides the benefit of model-based prediction uncertainty, enhancing the reliability of predictions. This study proposes the development of a BART model with a binomial likelihood to predict the percentage of students retained in tutorial classes using attendan…
Web-based Clustering Application for Determining and Understanding Student Engagement Levels in Virtual Learning Environments
The increasing use of virtual learning environments (VLEs) in recent years has transformed teaching and learning methods. Universities now combine VLEs with traditional classrooms to accommodate hybrid teaching and learning approaches. However, student engagement with VLEs varies, and universities lack the tools to effectively determine and analyse VLE engagement. Consequently, data-driven decision-making regarding VLE usage remains a challenge f…
No prominent works on this page.
Web-based Clustering Application for Determining and Understanding Student Engagement Levels in Virtual Learning Environments
The increasing use of virtual learning environments (VLEs) in recent years has transformed teaching and learning methods. Universities now combine VLEs with traditional classrooms to accommodate hybrid teaching and learning approaches. However, student engagement with VLEs varies, and universities lack the tools to effectively determine and analyse VLE engagement. Consequently, data-driven decision-making regarding VLE usage remains a challenge f…
Modelling Student Retention in Tutorial Classes with Uncertainty—A Bayesian Approach to Predicting Attendance-Based Retention
A Bayesian additive regression tree (BART) is a recent statistical method that blends ensemble learning with nonparametric regression. BART is constructed using a Bayesian approach, which provides the benefit of model-based prediction uncertainty, enhancing the reliability of predictions. This study proposes the development of a BART model with a binomial likelihood to predict the percentage of students retained in tutorial classes using attendan…
Artificial Intelligence (2 works) · Computer Science (2 works) · Machine learning (2 works) · Online Learning and Analytics (2 works) · Attendance (1 work) · Bayesian network (1 work) · Bayesian probability (1 work) · Cluster analysis (1 work) · Data science (1 work) · Data Stream Mining Techniques (1 work)