In learning analytics, early risk prediction plays a critical role in identifying students who are at risk of academic failure, enabling timely interventions to improve student outcomes. While effective in predictive accuracy, traditional machine learning models often require the training of multiple models for different time frames and suffer from a lack of explainability, limiting their practical application in educational settings. In this research, we propose ensemble dendritic neuron models (EDNMs), a novel approach for early risk prediction that addresses the challenges of existing models. EDNMs take advantage of a neural pruning mechanism inspired by biological neurons, allowing visual feature selection and explainability. The proposed EDNMs inherent visual feature selection improves transparency, making it easier for educators to interpret which factors contribute to the risk of a student. The performance of EDNMs is evaluated against RNN-based methods, demonstrating superior efficiency in prediction tasks and improved explainability. Crucially, the performance of early predictions over different timeframes is also provided by dynamically adjusting the dendritic state. Unlike conventional models that require multiple versions to accommodate predictions at different timeframes, EDNMs can adjust to varying weeks of early prediction with a single model, significantly reducing computational overhead. This study contributes to the growing field of explainable AI in education by offering a practical solution that enhances both the efficiency and transparency of early risk prediction systems.