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Research of Knowledge Point Intelligent Navigation Based on Association Rules

Author: LiuWei
Tutor: WuYanWen
School: Central China Normal University
Course: Circuits and Systems
Keywords: Knowledge Knowledge model User personalization model Association rules Knowledge intelligent navigation
CLC: TP393.092
Type: Master's thesis
Year: 2009
Downloads: 124
Quote: 1
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Abstract


Currently , network learning is gradually affect college students are the traditional ways of learning , however , in the learning process of the network , in the face of a lot of knowledge in the network courses , learners sometimes produce knowledge Trek unable to obtain tacit knowledge problems. To solve these problems , it is necessary to guide and assist learners learning by way of the navigation . Therefore , this article focuses on the study of the design theory and implementation methods of knowledge intelligent navigation , user personalization model and the improved association rules algorithm applied to the navigation of the knowledge , enhance the effectiveness of navigation to a certain extent , and thus improve learning efficiency and enthusiasm . The main research focus is reflected in the following aspects : (1) knowledge point model of knowledge intelligent navigation premise and principle ; knowledge associated structure defined by analyzing the relationship of knowledge points divided , and semantic network organization knowledge points , in order to build a four-layer structure of the knowledge model provides theoretical support for the design of intelligent navigation knowledge . (2 ) the demand for intelligent navigation of knowledge , analysis and structural analysis , taking into account the learners' background knowledge and learning personalized navigation , design user personalization model , including basic information , learning characteristics recognized knowledge level and learning record four parts . On this basis , construct a model of the knowledge intelligent navigation summarized intelligent navigation strategy . (3 ) intelligent navigation of the knowledge in the use of association rule mining methods , focusing on the FP-growth algorithm has been improved for the lack of the algorithm , the improved algorithm with the original algorithm and simulation experiments tested to improve the performance of the algorithm . (4) Finally, knowledge intelligent navigation system functions , data table structure design and implementation, and system navigation results of the comparison of test and analysis . Described deficiencies exist on paper , Looking forward to the next phase of work .

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