| With the rapid development and sweeping application of modern communication, the Internet and multi-media technology bring us with abundant learning resources and convenient, flexible E-Learning environment which breaks the physical limitation of traditional classroom-based learning mode. However, there are still a lot of new requirements and problems that should be solved in E-Learning environments, such as lack of learning control and personalized learning guidance. It becomes a big problem when an E-Learning system facing with a large number of learning resources and students with big gaps, and scant data of teachers'direct guidance. Besides, how to employ e-learners'feedback, such as the records of learning activity, testing and message, to generate personalized guidance content for the purpose of achieving the fixed learning goal of traversing all the Knowledge Points (concepts in a domain knowledge base) in a course, is also a big issue that attracts eyes of researchers on E-Learning.Recently domestic and foreign researches on such area have achieved a lot. With researches from the content-based filtering to collaborative filtering and then to self-organized community, and with the effort in creation of China E-Learning Technology Standardization, and with the introduction and application of Ontology, it is recognized that the information filtering should not only depend on the information itself, but also on the transmitters and users of the information. By analyzing the preferences and behaviors and making effective use of relationship of users, more accurate and efficient information filtering would come true.Based on the e-learners of a great number and the abundant learning resources in Network Education College of Shanghai Jiao Tong University, we construct a personalized learning platform to help and to guide the e-learner to achieve better learning quality. And the difficulties coming up are resolved in this way: 1) Establishing a self-organized community based on P2P to provide a different but similar cluster for different e-learners to settle the problem of e-learners'loneliness feeling in the distribute environment. 2) Adding recommendation functionality between partners in the same community when facing a large amount of learning resources. 3) Learning content recommendation based on clustering analysis that is performed by teachers for the lack of adaptive learning guidance from teachers. 4) Employing learning path mining methodology by analyzing historical data of learning records and ontology of the knowledge points for the disordered learning resources.Learning guidance module and teaching guidance module are the key parts of the system implemented. The main function of learning guidance module is to provide a communication platform for similar learners, including resource recommendation, learning path mining (sequencing), while the other one is to get learners with similar features around to make personalized guidance and recommendation from teachers easier.Fortunately, we got substantive support from the NEC of SJTU in experiment. By applying the methodology in"Data Structure"course, some comparison data between the new and old platform and between different algorithms are worked out. Experiments show that the model improves the quality of learning and enhances their learning initiative greatly. Meanwhile, we apply the learning guidance model to the real e-course of"Computer Application Basic", and get a satisfying evaluation from both teachers and students.In the future research, we should not only focus on the accuracy of community self-organizing and recommendation, efficiency of information mining and representative of clustering features, but also concern about making the learning platform more intelligent and more interactive. That means, the platform will be able to reason and judge, and has great capability in adaptive learning and automatic recommendation according to the environment and historical data. |
PDF Full Text Request