Application of Machine Learning Techniques to Web-Based Intelligent Learning Diagnosis System

Abstract

This work proposes an intelligent diagnosis system that supports a Web-based thematic model, which aims to cultivate learners' ability of knowledge integration by giving the learners the opportunities to select the topics that they are interested, and gain knowledge on the specific topics by surfing on the Internet to search related courseware and discussing what they have learned with their colleagues. Based on the log files that record the learners' past online behavior, an intelligent diagnosis system is used to give appropriate guidance to assist the learners in improving their study behaviors and grade online class participation for the instructor. The achievement of the learners' final reports can also be predicted by the diagnosis system accurately. Our experimental results reveal that the proposed diagnosis system can efficiently help learners to expand their knowledge while surfing in cyberspace Web-based theme-based learning model.