A Personalized User Evaluation Model for Web-Based Learning Systems

With the development of computer science and multimedia technology, Web-based learning becomes increasingly popular. User evaluation plays a significant role in the process of guided learning. In recent years, there is great progress in the development of evaluation technology. However, few evaluation methods fully take online learning activity analysis and individual differences into account. This paper proposes a personalized user evaluation model for Web-based learning systems. The model is utilized to record and analyze various learning activities throughout the entire learning process. Considering individual differences, learners are clustered and specific evaluation standards are set for different clusters. Comprehensive evaluation is achieved by combining Analytic Hierarchy Process, Fuzzy C-Means clustering and normalization algorithm. Through the comparison with several other common evaluation methods, experimental results show that the proposed method outperforms existing ones on the accuracy of learner evaluation.