MOOC (Massive Open Online Courses) is a kind of large-scale open and online education. It has got rapid development both home and abroad. With the Internet technology and information technology, it provides a new way of education, and it has brought a disruptive innovation in the field of traditional education. However with the continuous development of MOOC, its disadvantages and shortcomings are gradually highlighted and can not be ignored, such as high rate of lesson drop-out, unsupported the personalized. At the same time, the MOOC platform has a large number of course resources. They are in a state of scattered distribution. And it is hard to find and use. These shortcomings put forward new requirements and challenges for MOOC platform’s course resource organizational model and architecture, such as dynamic relation learning resources, personalized learning etc. In order to deal with these problems and challenges, we do the following research:(1) We put forward an organization model of course resources which are on the MOOC platform. According to this model, we use learning object packaging model in the inner course. And between courses, we use the semantic description of the course based on the ontology and generative information. This model not only describe the basic information of the courses, but also show the semantic relation of the courses. So the model is the base of personalized and intelligent learning.(2) We design an architecture of this MOOC platform. The organization model of course resources needs the MOOC platform to provide technical support. Thus basing on the organization model, we design the architecture of MOOC platform by analyzing the functional requirements and technical requirements. From bottom to top, this architecture contains data layer, semantic layer, logic layer and presentation layer.(3) Basing on the architecture of this MOOC platform, we design a personalized recommendation process. The reason why we design the organization model and platform is to realize the dynamic association of learning resources, semantic retrieval of learning resources and personalized recommendation and so on. This paper shows how to realize the intelligent operation of learning resources based on the example of personalized recommendation. |