Personalized automatic recommendations for the web-based autonomous language learning system based on data mining technology

Web-based autonomous language learning system enjoys great popularity among college English learners in China. It provides abundant educational resources for the learner. Efficient use of available materials on the system needs intelligent resources recommendation and personalized guidance in learning strategies. This paper uses the data mining technology to build an automatic recommendation agent to enhance the function of web-based autonomous language learning system. The automatic recommendation agent serves two functions. One is to push related resources to the learner based on the results from association rule mining of all users´ resource navigation patterns. The other is to provide immediate and individualized guidance for the learner concerning strategies of online language learning. The knowledge of personalized guidance comes from the machine learning of strategies adopted by students with varied level of achievements in language learning. The framework and data structure of its implementation have been presented in this paper. Experiments show that the automatic recommendation agent improves the management of educational resources on web-based autonomous language learning system and better facilitate students´ language learning.