The development of information and communication technologies has significantly transformed teaching and learning practices in higher education. Digital pedagogy, supported by e-learning technologies and Virtual Learning Environments (VLEs), enables flexible, student-centered learning experiences and provides new opportunities for data-driven educational decision-making. This study examines the conceptual and technological foundations of digital pedagogy and analyses the role of Learning Management Systems and VLE platforms in modern educational information infrastructures. We paid particular attention to system architecture, database management, cloud computing, cybersecurity, and artificial intelligence integration. In this research, the Learning Analytics module was developed to analyse student activity logs and predict the risk of academic failure. The module processes behavioural and academic indicators obtained from LMS/VLE platforms, including login frequency, average session duration, assignment submission ratio, test scores, and forum activity. A synthetic dataset simulating LMS activity logs of 300 students was generated to experimentally evaluate the proposed approach. After data normalization, the dataset was divided into training and testing subsets using a 70/30 split. Two supervised machine learning models were implemented to predict student academic risk: Logistic Regression and Random Forest classifiers. The models were evaluated using standard metrics including Accuracy, Precision, Recall, and F1-score. Experimental results demonstrate that both models significantly outperform the baseline approach based solely on login frequency. The Random Forest classifier achieved the best performance with an accuracy of 0.911 and an F1-score of 0.900, while the Logistic Regression model showed a high recall of 0.950, which is particularly important for early identification of at-risk students. The findings confirm that learning analytics combined with machine learning techniques can effectively identify students at risk and support early intervention strategies. When integrated with well-designed digital pedagogy, LMS and VLE systems can significantly improve learning outcomes and support more effective, data-driven higher education environments.