Personalized learning full-path recommendation model based on LSTM neural networks

Discovering useful hidden patterns from learner data for online learning systems is valuable in education technology. Studies on personalized learning full-path recommendation are particularly important for the development of advanced E-learning systems. In this paper, we present a novel model of full-path learning recommendation. This model relies on clustering and machine learning techniques. Based on a feature similarity metric on learners, we first cluster a collection of learners and train a long short-term memory (LSTM) model in order to predict their learning paths and performance. Personalized learning full-paths are then selected from the results of path prediction. Finally, a suitable learning full-path is recommended specifically to a test learner. In this study, a series of experiments have been carried out against learning resource datasets. By comparisons, experimental results indicate that our proposed methods are able to make sound recommendations on appropriate learning paths with significantly improved learning results in terms of accuracy and efficiency.