The Personalized Learning Evaluation System Of Physics Based On Decision Tree Algorithm

According to the development of computer and communication technique ,it is a age of information and economy, people is changing their ways of lives by the application of information technology in each field .The development of distance education is facing a challenge. The evaluation of the students'learning is very important through teaching process. Under the distance education environment, teaching is separate to learning and the students learning by themselves, all these characters make the teaching supervisor feel lack of useful data when evaluating. This is because of the lack of the monitoring on students'learning process and the evaluation of the students'behavior online by the distance educational system based on web. Nowadays, there are many kinds of problems in many educational environments based on web, primarily are the lack of methods on teaching and communication between teachers and students. What we have gotten for personalized learning environment can not solve the needs on personalized learning, thus these can not serve the learner very well on personalized learning, also the learner can not adjust themselves in time according to their shortcoming. To solve this problem, we present a environment model of personalized learning based on data mining technology, especially for the evaluation online of physics.Since we can find many unknown and useful regulations through using data mining technology, it is undoubtedly giving the personalized education service powerful support. Here we analyzed decision tree classification algorism, compared the ID3 algorism and the C4.5 algorism. Though the study on the C4.5 algorism, we used a easier algorism of C4.5 decision tree regulation based on attribute correlation which is already existed, and applied it into the individualized learning evaluation .We testified it by experiment data.