Integrating Data Mining Technology into Web-based Autonomous Language Learning

This paper attempts to integrate data mining technology into web-based autonomous language learning (WALL). WALL emphasizes learner autonomy and provides abundant resources for learner to choose freely, but it lacks the mechanism to provide customized and personalized guidance in accordance with each learner's preferences and characteristics. Through data mining on-line learning traces, we can discover some valuable information about the learners and their habitual use of teaching resources, based on which, some recommendations can be given to each learner by the system. It makes WALL more individualized and intelligent. This paper presents the framework of this extended data mining system on WALL and describes in detail the functions and operations of each module, followed by the description of the basic algorithms of the central module. Finally, it confirms the feasibility and the far-reaching pedagogical implications of this data mining application.