Application of Social Computing to Collaborative Web Search

Recently, e-learning has been paid much attention in the area of education. However, it is difficult for the low-achievement students to find out the key points and keywords while searching online articles. They usually cannot accurately obtain the website information even after searching for large amount of data in the Internet. Meanwhile, these low-achievement students often lack of the related prior knowledge to determine if the website is useful. Accordingly, they select those websites based on their disorderly instincts. In this work, an intelligent collaborative web search assistance platform is proposed. A group grading module is presented to derive three parameters that are used to calculate the ranking of each website via the Support Vector Regression method. The effects of website ranking shorten the searching processes, and the learners can thus have more time to focus on comprehending the contents of the recommended website. The experimental results reveal that the proposed algorithm can effectively guide learners to search the appropriate website; accordingly, the target of self-learning assistance can be achieved and the learning performance of the students is enhanced.