Advancing Predictive Analytics in E-Learning Platform: The Dominance of Blended Models in Enrollment Forecasts
- Title
- Advancing Predictive Analytics in E-Learning Platform: The Dominance of Blended Models in Enrollment Forecasts
- Creator
- Sumalatha K.N.; Anupama N.; Sasikumar P.; Sucharitha M.M.; Basha M.S.A.
- Description
- The rapid expansion of e-learning platforms has revolutionized the landscape of education, particularly highlighting the significance of online courses in contemporary learning environments. This research focuses on Udemy, a prominent online learning platform, and aims to enhance the predictability of course enrollments within its IT & Software category. The study's central purpose is to leverage advanced machine learning techniques to predict course subscriber numbers, a crucial indicator of a course's popularity and success. Employing an extensive dataset from (Kaggle DB)Udemy, encompassing various course attributes such as ratings, reviews, and pricing, the study explores multiple machine learning models. These include Linear Regression, Decision Tree, Random Forest, Gradient Boosting, and K-Nearest Neighbors Regression. A key innovation of this research is the application of ensemble methods, particularly a blended model approach, to integrate predictions from multiple models, thereby enhancing accuracy and reliability. The findings of this study are significant. The ensemble approach, notably the blended model, outperforms individual predictive models in accuracy. Among the single models, Gradient Boosting Regression shows the highest effectiveness in forecasting enrollments. The research highlights the vital role of course characteristics, including ratings and reviews, in determining course popularity. This study contributes to the field of e-learning by introducing a novel, data-driven approach to predict course enrollments. It offers valuable insights for educators, course creators, and platform developers, emphasizing the potential of machine learning in optimizing content strategy and marketing efforts in the digital education domain. The application of ensemble machine learning methods presents a new horizon in educational analytics, paving the way for more nuanced and effective strategies in online education delivery and promotion. 2024 IEEE.
- Source
- 2024 2nd World Conference on Communication and Computing, WCONF 2024
- Date
- 2024-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- Blend Models; E-Learning Platform; IT Software Courses; Regression Models
- Coverage
- Sumalatha K.N., PG Department of Business Administration, Maris Stella College (Autonomous), Vijayawada, India; Anupama N., PG Department of Business Administration, Maris Stella College (Autonomous), Vijayawada, India; Sasikumar P., Department of Sciences and Humanities Christ (Deemed to be University), Bengaluru, India; Sucharitha M.M., Department of Professional Studies, Christ (Deemed to be University), Bengaluru, India; Basha M.S.A., GITAM School of Business, Gandhi Institute of Technology and Management (Deemed to be University), Bengaluru, India
- Rights
- Restricted Access
- Relation
- ISBN: 979-835039532-7
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Sumalatha K.N.; Anupama N.; Sasikumar P.; Sucharitha M.M.; Basha M.S.A., “Advancing Predictive Analytics in E-Learning Platform: The Dominance of Blended Models in Enrollment Forecasts,” CHRIST (Deemed To Be University) Institutional Repository, accessed April 21, 2025, https://archives.christuniversity.in/items/show/19120.