Resource Delivery Service System for User Engagement Improvement

Open educational resources (OER) are important assets for students or teachers, used to help them search for useful resources. However, it is a challenge to improve the user engagement of OER. In this paper, we propose a system, called resource delivery service system (RDSS), in the Taiwan Open Platform for Educational Resources (TOPER) that actively recommends educational resources to users. RDSS includes three modules: high-quality resource identification, teaching subject identification, and teacher attribute identification. These modules can be used to recommend resources to users of TOPER. We applied deep learning and support vector machine to construct these modules in RDSS. The experimental results demonstrated that RDSS can achieve an accuracy of 86% in high-quality resource identification, an accuracy over 88% in teaching subject identification, and an accuracy of 86% in teacher attribute identification.