Predictive Analysis of Student Engagement and Academic Performance in Virtual Learning Environments Using a Hybrid Markov: Machine Learning Model

The study aims at improving the prediction of student engagement and academic success in virtual learning environments (VLEs) by proposing a hybrid ensemble model that integrates Markov Chains, Hidden Markov Models (HMMs), and supervised machine learning algorithms. Traditional models often fail to capture the temporal dynamics of student behavior or provide timely and personalized interventions. To address these limitations, this study leverages sequential modeling and classification techniques on behavioral data collected from 500 students across multiple online courses. The methodology includes data preprocessing, feature extraction, and model training using Decision Trees and Support Vector Machines (SVMs), alongside probabilistic modeling through Markov Chains and HMMs. Model evaluation was conducted using accuracy, F1-score, precision, recall, ROC-AUC, and confusion matrices. Results show that the hybrid ensemble model outperforms individual models, achieving an accuracy of 91.9% and an F1-score of 89.9%. Forum participation and assignment completion emerged as the most influential predictors. Temporal modeling revealed that students in the high engagement state tend to remain consistent, while those in the low engagement state are unlikely to improve without support. Medium engagement students demonstrated the highest behavioral volatility, highlighting the importance of adaptive interventions. The proposed model not only enhances predictive accuracy but also provides interpretable insights for early detection and support. These findings offer practical implications for educators, academic advisors, and learning system designers seeking to optimize student outcomes and retention in online learning contexts. Future research could explore integrating additional engagement indicators such as emotional and social interaction metrics, as well as testing the model across diverse cultural and institutional settings to further validate its generalizability.