Research And Design On Learning Resource Recommendation System In Ubiquitous Learning Environment

In recent years, with the popularity of the concept of life-long learning and whole-people learning, informal learning has been gotten more and more attention. The development of mobile communication technology makes the way of informal learning experience from Electronic Learning (E-learning) to Mobile Learning (M-learning) and then to Ubiquitous Learning (U-learning), the way of learning becomes more flexible, convenient and personalized. Because of the4A (Anyone, Anytime, Anywhere, Any-device) characteristics, U-learning adapts to the social demand for learning and becomes more popular, it also has become the trend of the future learning. And the U-learning resources are the essential supports for U-learning, so how to design and develop efficient, convenient and personalized U-learning resources becomes important in the study of U-learning. Based on this, the study firstly focuses on U-learning resources, the resources were designed from information module, evolution module and activities module, making them ubiquitous, situationaland connective.Nowadays, the Internet has become an important way for learners to acquire learning resources, however, with the explosive growth of cyber source, only through the traditional search engine to obtain information will we get redundant and inaccurate results, which make the learners, obtain the unsatisfactory resources. And in recent years, the successful application of personalized recommendation technology in business field provides a new way to solve this problem. Pulling the personalized recommendation technology into education field and combined with U-learning resources will help learners to get U-learning resources more convenient. Based on this, the study analyzed and compared the current personalized recommendation technology, combining the characteristics of U-resources, and finally used collaborative filtering algorithms to recommend learning resources to learners.This study firstly designs the learning resource model based on the analysis of the characteristics of U-learning and its resources. Next, the study determines using collaborative filtering recommendation to recommend the U-learning resources through the analysis and comparison of a variety of personalized recommendation technology. Then, the study designs the learners’interest model, and recommends the U-learning resources to learners using collaborative filtering algorithms. Finally, the study designs the U-Learning Resource Recommendation System concretely from the requirement analysis to the code, applying the theory to practice.