Design And Implementation Of Core Functions Of Online Learning Recommendation Platform Based On Weibo

Massive Open Online Course (MOOC) becomes a hit these years. Millions of learners are attracted all over the world by its open conception, world-class learning recourses and modern teaching model. As the number of MOOC platforms and courses growing with the whole MOOC production, the problem of information overload and lack of integration do influence the MOOC experience. In this paper, we invented an Online Learning Management and Recommendation Platform, trying to solve this problem by helping MOOC learners integrate, manage and discover online courses.First of all, in the level of integration, we designed a web crawler system for MOOC courses. As courses are distributed in different platforms and ways, we built course crawlers especially for each of the platforms. Then we created a crawler management structure to automatically manage the crawlers and update data at regular intervals. With this strategy, we successfully managed to get more than1000courses from Coursera, Udacity and edX.Secondly, in the level of management, a course management system is built to offer services such as course searching, curriculum management, notes, rating and so on to help people study MOOC productively and socially. The system takes the B/S systematic structure as the basic framework and is now online as a Weibo application.At last, based on the data we collected, we implemented some main-streaming recommendation methods using Apache Mahout in the system to help people discover new courses. Characteristics of different period and the Cold-start problem are taken into deep concern when we chose the recommendation strategy.The idea of MOOC is to make education unlimited and it shows the potential to be the future of education. But there is still a long way to go. In this study, by doing conception study, problem analysis, product research, function design and implement we try to improve its learning experience.