Learning resource recommendation systems can help learners find suitable resources (e.g., books, journals, …) for learning and research. In particular, in the context of online learning due to the impact of the COVID-19 pandemic, the learning resource recommendation is very necessary. In this study, we propose using session-based recommendation systems to suggest the learning resources to the learners. Experiments are performed on a learning resource dataset collected at a local university and a public dataset. After preprocessing the data to convert it to session form, the Neural Attentive Session-based Recommendation (NARM) and Recurrent Neural Networks (GRU4Rec) models were used for training, testing, and comparison. The results show that recommending learning resources according to the NARM model is more effective than that of the GRU4Rec model, and thus, using the session-based recommendation system would be a promising approach for learning resource recommendation.