Application of Social Computing to Collaborative Online Learning Resource Recommender System
Recently, e-learning has been paid much attention in the area of education. However, it is increasingly difficult for low-achievement students to remain focused when learning on the Internet owing to the vast amount of information online and the many distractions from social media. Meanwhile, these low-achievement students often lack of the related prior knowledge to determine if the website is useful. Accordingly, useful online learning resource recommender algorithms can suggest learning resources fitting the task the low-achievement learners are currently working on or trying to gain knowledge about. In this work, an intelligent collaborative online learning resource recommender system 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 online learning resource ranking shortened the searching processes, and the learners can thus have more time to focus on comprehending the contents of the recommended online learning resources. The experimental results revealed that the proposed algorithm can effectively guide learners to access the appropriate online learning resources; accordingly, the target of self-learning assistance can be achieved and the learning performance of the students was enhanced.
Keywords- Collaborative Online Learning Resource Recommender, Intelligent Tutoring Systems, Information Retrieval, Support Vector Regressions.