Research Of The Mobile Learning System Based On Context-aware

As a new learning style, Mobile learning provides a service that learners canstudy in anytime and anywhere with mobile devices. Seen from the globaldevelopment tends, mobile learning not only acts as a good complement to traditionallearning style, but also will play a key role in field of education in the future.Therefore, the research of mobile learning has practical meaning. With more and moreinformation resources, using personalized recommendation technology will providedifferent services to different users. Importing contextual information into therecommendation system will improve the recommendation quality.This dissertation, firstly, gives the detail introduction of the required basic theoryoverseas and domestic research status which include the development of mobilelearning theory, context-aware theory and their applications. Summarizing thecontext-aware technology applied in recommender systems. Secondly, thisdissertation proposes a personalized recommender algorithm based on users’ contextclustering. This algorithm uses the idea of contextual pre-filtering. A method of fuzzyclustering is used on the users’ context in history data set. Then we use user-basedcollaborative filtering algorithm for personalized recommendation. The proposedmethodology is tested using public datasets and the result shows that it can be used inmultidimensional context information. The recommendation precision is higher thantraditional collaborative filtering algorithm. Thirdly, this dissertation designs aprototype of mobile learning system based on context-aware. The prototype is dividedinto users’ device layer, context-aware data processing layer, context-aware datamining layer and learning resources storage layer. The four layers are introducedrespectively. This dissertation gives a resolution of recommender algorithm moduleapplied into the system. In the end, the dissertation introduces the implementation ofthe system, which includes the process of setting up the system developmentenvironment, key technologies used in developing, client and server resolution. Thesystem has been tested through experiment, and the result provides the algorithm has efficiency in some degree when it is applied into the mobile learning system.