A Big-Data Oriented Recommendation Method in E-learning Environment

Abstract

E-learning is increasingly gaining popularity in organizational and institutional learning for its several benefits to learn anywhere, anytime, and anyplace. Therefore, explosive growth of E-learning has caused difficulty of locating appropriate learning activities for learner in this context, and it becomes relatively widespread learning method for learner. Several research in e-learning mainly focused on improving learner achievements based on recommendation technique. An ideal recommender system in e-learning environment should be built with both accurate and learning goals. To address this challenge, we propose a recommendation method based on machine learning technique. Based on this tool, a learning approach is designed to achieve personalized learning experiences by selecting the most appropriate learning activities. Moreover, some experiments were conducted to evaluate the performance of our approach. The results demonstrate that our method outperforms other state-of-the-art methods and reveals suitability of using recommender system in order to support online learning activities to enhance learning.