Empowering education: Harnessing ensemble machine learning approach and ACO-DT classifier for early student academic performance prediction
- 2 September 2024
- journal article
- research article
- Published by Springer Nature in Education and Information Technologies
- p. 1-29
Abstract
No abstract availableKeywords
This publication has 19 references indexed in Scilit:
- Learning path planning methods based on learning path variability and ant colony optimizationSystems and Soft Computing, 2024
- Forecasting students' adaptability in online entrepreneurship education using modified ensemble machine learning modelArray, 2023
- Parameters Sensitivity Analysis of Ant Colony Based Clustering: Application for Student Grouping in Collaborative Learning EnvironmentIEEE Access, 2023
- A gamified approach for improving the learning performance of K-6 students using Easter eggsMultimedia Tools and Applications, 2023
- Examining key factors of beginner’s continuance intention in blended learning in higher educationJournal of Computing in Higher Education, 2022
- Predicting student's dropout in university classes using two-layer ensemble machine learning approach: A novel stacked generalizationComputers and Education: Artificial Intelligence, 2022
- Predictive models assessment based on CRISP-DM methodology for students performance in Colombia - Saber 11 TestProcedia Computer Science, 2022
- Predicting Performance of Students Considering Individual Feedback at Online Learning Using Logistic Regression Model2021
- Enhancing the prediction of student performance based on the machine learning XGBoost algorithmInteractive Learning Environments, 2021
- Student Courses Recommendation Using Ant Colony Optimization2010