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
Cite this paper
Huang, C. , Chen, H. , Chang, S. and Chien, S. (2015) Application of Social Computing to Collaborative Web Search. Open Journal of Social Sciences
, 28-33. doi: 10.4236/jss.2015.39005
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