Research Of Personalized E-learning Solution Recommending System Based On Community Driving And Web Data Mining

Establishing Life-long Learning System is one of the most important contents of our education. The thesis aims at establishing an e-learning platform under life-long learning environment. It conceives a Recommending System(PELSRS) for e-learning personalized solution via integrating Data Mining theory and Community Driving theory. The system is compatible of different learning situations. It adopts personalized self-learning as a main learning method. It accumulates resources and information through Community Driving. And it achieves Personalized Recommendation by making full use of network intelligence.The thesis explores the correlation theory and supporting technology of PELSRS. Firstly, It discusses related theories of PELSRS, puts forward the Concept Model of Personalized e-learning Solution, and explores its personalized strategy. Then, The thesis discusses Community Driving's application in PELSRS theory, puts forward several integrated strategies, and researchs the strategies to sustain social motivation. Next, the thesis uses Web Data Mining technology to dig and recommend personalized e-learning solutions, designs a recommending process and a recommending engine based on Association Rules Mining technology. At last,the thesis puts froward the system's functional model, main database and user interface illustration, and pictures the future research work and developing trends of this field.