<p>Virtual learning environments (VLEs) have revolutionized online education by providing flexibility and accessibility while enabling the tracking of student engagement and performance. However, identifying at-risk students remains challenging due to issues such as class imbalance, dropout complexity, scalability and concept drift. To address these challenges, this research introduces a Socially Enhanced Graph with Attention and Dynamic Concept Adaptation (SEG-ADCA) framework for Early Prediction of At-Risk Students. The framework employs the Synthetic Minority Oversampling Technique (SMOTE) to effectively address class imbalance in the dataset, ensuring adequate representation of the minority class during model training and enhancing predictions for at-risk students, especially in scenarios with significantly fewer at-risk students. It incorporates a knowledge graph representation model based on temporal nodes to capture "enrolled in" relationships between students and courses, represented as a multi-dimensional matrix. This facilitates detailed analysis of time-dependent student-course interactions, enriching the feature space for prediction. The framework integrates Social Network Analysis (SNA) to capture relationships and interaction strengths among students, enhancing the understanding of peer influence, collaboration, and isolation, and providing deeper insights into social and academic dynamics essential for identifying at-risk students. The Gated Attention-Based Diffused Graph Convolutional Network (GADGCN) combines graph convolution with gated mechanisms to extract complex spatiotemporal dependencies and dynamically assign attention weights to hidden layers, improving feature relevance and boosting prediction accuracy. The Personalized Bi-Directional Gated Recurrent Model (PBGRM) incorporates a personalized behavior Embedding (PBE) layer, contextualizing critical features with personalized insights to capture individual learning patterns. The Dynamic Concept Drift Adaptation (DCDA) block addresses evolving patterns in student data by detecting and adapting to concept drift through incremental learning and feedback mechanisms, ensuring high prediction accuracy and robustness under changing conditions. Evaluated on the OULA, Junyi, Liru, and WorldUC datasets, the SEG-ADCA achieved superior performance metrics, including 98% accuracy, 88% precision, 89% recall, an F-measure of 87%, a MAE of 0.20, a RMSE of 0.43, and an AUC of 0.90, demonstrating its efficacy in predicting at-risk students, enabling timely interventions, and enhancing retention and engagement in online education.</p>