Learning Path Recommender System based on Recurrent Neural Network

Abstract

Programming education has recently received increased attention due to growing demands for programming and information technology skills. However, a lack of teaching materials and human resources presents a major challenge to meeting the growing demand for programming education. One way to compensate for a shortage of trained teachers is to use machine learning techniques to assist learners. Therefore, we propose a learning path recommendation system based on a learner’s ability charts by means of a recurrent neural network. In brief, a learning path is constructed from a learner’s submission history with a trial-and-error process, and the learner’s ability chart is used as a barometer of their current knowledge. In this paper, an approach for constructing a learning path recommendation system by using ability charts and its implementation based on a sequential prediction model by a recurrent neural network, are presented. Experimental evaluation with data from an e-learning system is also provided.