Research Of Knowledge Point Intelligent Navigation Based On Association Rules

At present, web-based learning is gradually affecting the traditional learning mode of College Students. However, when learners face a large number of knowledge points in the process of web-based course learning, they sometimes meet knowledge navigation lost, unable to acquire tacit knowledge and other problems. In order to solve these problems, this paper proposes a method which can guide and help learners to learn through navigation. Therefore, the paper focuses on the research of design theory and implementation method about knowledge point intelligent navigation. It can enhance the effectiveness of navigation, by which applies user personalization model and improved association rules algorithm to knowledge point navigation. Consequently, that can improve learning efficiency and positivity of learners. Research of this paper mainly reflects in the following aspects:(1) The construction precondition and principle of knowledge point model is studied in knowledge point intelligent navigation. Through analyzing the relationship among the division of knowledge point, the correlation structure of knowledge point is defined. And it proposes the semantic network organization method of knowledge point. Accordingly, the four-layer structure of knowledge point model is constructed, which provides a theoretical support for the design of knowledge point intelligent navigation.(2) According to requirement analysis and structural analysis of the navigation, with consideration of the impact of learners' knowledge background and learning information upon personalized navigation, the user personalization model is designed. The model mainly includes learner basic information, learning characteristics, cognitive level and learning record. The model of knowledge point intelligent navigation is constructed on this basis, and the strategy of the intelligent navigation is summarized.(3) The association rules mining algorithm is applied to the intelligent navigation, emphatically analyzes FP-growth algorithm. Improve the FP-growth algorithm according to the original algorithm's shortages. And that makes them test and compare through experimental simulation, thus improves the performance of algorithm.(4) Finally, the intelligent navigation system is designed and implemented including its function, database and so on. Moreover, the results of navigation system are compared, tested and analyzed. Look forward to the next phase of work at the same time.