In a rapidly evolving digital landscape, recommender systems have become essential tools for helping users navigate overwhelming amounts of information in various domains. In e-learning contexts, these systems aim to support learners by identifying educational resources or relevant academic content that are significant for their learning experience. However, research incorporating domain knowledge, such as course-related concepts, to improve recommendation quality remains limited. This paper presents a new personalized recommender system in e-learning context, which is called KA-ERN: Knowledge-based Attention Ensemble Recurrent Network. Specifically, KA-ERN leverages a Knowledge Graph to capture the dependencies and semantic relationships between users, courses, and their related concepts and then, performs ensemble learning by combining the Bidirectional Long Short-Term Memory network (Bi-LSTM) with the Artificial Neural Network (ANN). An attention mechanism is added to enhance recommendation quality. Our approach is based on a two-stage architecture. First, entities and relations embeddings are generated and then concatenated as sequences allowing the model to capture complex relationships and contextual dependencies of user preferences. Secondly, these embeddings are provided as inputs to the KA-ERN model. The proposed combination of Bi-LSTM network, ANN, and attention mechanisms shows the advantage of using Knowledge graph embeddings over bipartite graph embeddings capturing only user-item interactions and experimental results achieving the best recommendation metrics, with RMSE of 0.045 and MAE of 0.034, outperforming baseline methods.