Research Of The Personalized Learning System Based On Data Mining Technology

Progress of society and science has provided opportunities as well as challenges for the education development. The traditional teaching method can't meet the demands of this period. The development of modern distance education has become a global trend. Many governments take the development of modern distance education as an important strategic decision-making, they draw up specific plans and relevant policies, making the means of distance education to be taked advantage of in primary schools, secondary schools, universities and even businesses. Nevertheless, in the traditional learning system, without the full consideration of learner's requirements and individualities, the system self is centric, learners have to adapt to the system instead of adapted by the system. This kind of system doesn't carry out instructions according to learner's learning rule, resulting in the poor interaction and bad learning effects. Personalized distance education system reflecting thinking of Personalized Learning is an effective method to solve the contradicts between the individualization of learner's demand and the simplifying of teaching resources. In order to realize the Modern Distance Education of real individualization and guarantee the teaching quality, we must construct the personalized learning system.Although personalized learning is a new term,personalized learning in reality already exists, and is very common. A series of modern theories give thinking of personalized learning more soil, such as constructivism learning theory, humanistic learning theory, learning style theory and multiple intelligences Theory. These theories suggest that education should respect the learner's differences.The realization of personalized learning system need make use of many theories and technologies such as psychology, education theory, artificial intelligence, data mining technology and network communications. In recent years many countries have invested human and funds to study personalized learning system, however there are many problems need to be researched and resolved.In this paper, as analyzing the current development status of personalized learning system and data mining technology, it applies the data mining technology into the system .The main contributions of this paper include the following aspects.1. Establishing a classification model of learning style. The system knows learners'differences in the learning style, so that it can adopt flexible and diverse teaching strategies and methods, providing learning opportunities suit for learning style for each student and playing the strengths of the different learning styles. So it can improve the learner's learning efficiency. Therefore, we increase learning style classification model in the personalized learning system, it can predict whether learner's learning style is Visual, Aural or Read/Write. We use Naive Bayesian Classify technology to achieve the model learning style classification model.2. As analyzing the basic theory and application in education of data mining, we apply data mining technology in the personalized learning system and propose a method which may find some association rules of knowledge points test scores, learning styles and learning methods. In the personalized learning system we apply apriori algorithm and find rules implied between data items. These rules can provide guidance for personalized learning system's personalized service.3. Given the design concepts and applications of personalized learning system. Compared with the traditional distance education system, exploring in the application of personalized learning, and increasing the functions of data mining in the personalized learning system which can analyze the correlation of the learning style, test scores and learning methods. The system also increases the function of personalized learning. According to learning styles of learners, personalized learning modules organize the contents suit for the learner by the rules of rule base.