Data Mining Early Warning System Based on Virtual Learning Environment
نویسندگان
چکیده
In this paper, multi-angle analysis of quantitative data mining warning system is completed, by using software engineering method. The study on the core issue of early warning is conducted, during divided the learning process in VLE platforms into three stages: former learning, study of middle and after learning. The studying route of core issues is: under the mode of Windows Server Apache, MYSQL and PHP (WAMP) built a learning platform, and by using asynchronous data pool to completes learning behavior of student data tracking in learning platform VLE. Meanwhile, a visualized statistical graph is designed and implemented, which can enter the presupposition condition for different object. That provides the evidence for warning analysis. Based on Android, EXJS, HTML5, PHP technology ,we design a questionnaire system, under VLE Virtual learning platform, to analyze after learning visualized warning, which facilitate further improve the quality of learning virtual learning environment.
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