Machine Learning Model for Prediction of Student Attrition in E-learning Environment: Research Methodology

This paper presents a novel machine learning methodology for predicting student attrition in eLearning environments. Recognizing the limitations of traditional approaches, research exploring the power of ensemble machine learning, combining the strengths of Naïve Bayes, Gradient Boosting, and Random Forest algorithms. To further enhance predictive accuracy based on machine learning, integrating Simulated Annealing for parameter optimization and validation, allowing for fine-tuning of each individual model within the ensemble. An investigation of why students in Kenya public universities dropout from particular course, early identification and mitigation procedures of students attrition. The ensemble weights are iteratively adjusted and optimized to create a robust predictive machine learning model. This paper allows the machine learning model to learn complex patterns within the data that contribute to student’s attrition identification. Using a mixed method research design for optimal predictive machine learning in student attrition identification offers a robust approach to understanding and addressing the multifaceted issue of student dropout. Both quantitative and qualitative methods, researchers can develop more accurate, interpretable, and actionable models, ultimately leading to more effective interventions and improved student retention rates. Research validate the proposed framework using real-world eLearning datasets, comparing its performance against standalone models. The results demonstrate the effectiveness of combining ensemble learning with optimization techniques, highlighting the potential for improved precision in identifying at-risk students. This methodology contributes to the field of educational data mining by pioneering the use of Simulated Annealing for attrition prediction, offering a scalable solution for institutions to proactively support student retention and improve eLearning outcomes.