Constructing E-learning Communities of Interest based on Learners Rating Prediction
Current e-learning applications are limited with respect to learning cooperation and communication among learners, because these applications such as on-line courses offered in China often involve large numbers of geographically dispersed students who have diverse learning preferences and different requirements. Constructing learner communities of interest is critical and necessary to implementing cooperative learning in an e-learning environment. This paper proposes a method for community construction which put the learners with similar interest together to form communities. Here, the similarity between two learners is measured by computing the cosine of the angle between their rating vectors. To address the problems of sparsity in the rating data set, a learners ratings on the learning objects which he has not rated is predicted by the similarity of objects. Experimental results derived from real learner data have shown that this method can organize learners properly and efficiently.
e-learning e-learning community similarity rating prediction
Jingtao Li Gang Liu Shengqi Lu
Fudan University Shanghai, China
国际会议
第四届国际计算机新科技与教育学术会议(2009 4th International Conference on Computer Science & Education)
南京
英文
1785-1789
2009-07-25（万方平台首次上网日期，不代表论文的发表时间）