Research On Network Multi-paradigm Learning Flat With Intelligence

With the development of Computer network and Communication Technology, network-based learning is getting more and more popular. Hence, network-based distance education becomes the new trend of CAI development. In this new kind of learning environment, people can teach and learn without time and place constraint, which brings great convenience and flexibility for people who want to learn. Modern network-based learning is a kind of learning behavior that performs in the network environment, whose resource, personnel and management are all distributed. This distributed learning environment poses great challenges to traditional teaching method. Owing to its own limits, traditional CAI technology and stand-alone intelligent tutoring system (ITS) can no longer meets the demands of modern education. In this circumstance, various network-based learning systems or flats have been put into use. However, there are still some deficiencies in existing network-based learning pltforms due to their poor design: (1) Applying the same learning materials to all students regardless of their personality; (2) Lack of interaction;(3) Fixed teaching paradigms without flexibility; (4) Inflexible user interface. (5) Lack of self-adaptation and self-study ability. Because of the above shortcomings, these systems cannot ensure good teachingquality and learning effect.hi order to solve the problems mentioned above. We consider to construct an adaptive intelligent learning platform in other ways rather than ITS. Combining the mature technology in stand-alone ITS and Distributed Artificial Intelligence Technology, we consider employing middle-ware Technology and agent Technology to develop a new network-based learning flat. Multi-paradigm Learning can be realized on this platform, which means during the learningprocess, teaching paradigm can dynamically changes according to learners' states. Thus, flexible learning can be carried out This design points out a feasible way to realize personalized learning in distributed environment.Based on the above analysis, we construct a Network Multi-paradigm Learning flat with Intelligence(NMPLF). In this model, we incorporate component technology, middle-ware technology and agent technology to establish its overall structure. To realize multi-paradigm learning, we convert teaching paradigm into corresponding paradigm component using component technology. Thus, the system can dynamically change one teaching paradigm into another with the collaboration of Multi-paradigm Transformer. As a result, better learning effect can be achieved. We also discuss the following things in NMPLF:(1) Student model construction;(2) Definitions about learning state; (3) Transition process from one learning state to another; (4) Method for establishing an ideal student model; (5) Method for students' initial classification. ;(6) Collaboration between different agents, etc.Finally, we develop an experimental system to do some experiments. As the result demonstrates, the system can change paradigm dynamically and improve learner's learning effect as well.