An ultrasonic system for intravascular measurement and visualisation of anatomical structures and blood flow

Imperial Users onl

Increasing Information Reposting Behavior in Online Learning Community

Online learning communities (OLCs) enable their learner to access different types of information through internet based structures anywhere anytime. OLCs are among the strategies used for the production and repost of information by learners interested in a specific area to support asynchronous learning. In this respect, learners become members of a particular domain and begin posting. OLC members consist of different sites with different educational backgrounds as well as different levels of expertise. This causes the sharing of posts which may not be appropriate for different learners. It also reduces data reposting behavior and subsequently decreases participation in information sharing. Furthermore, most learners of these communities take up a lurking position toward the posts. One of the ways proposed to increase information reposts is the selection and display of effective posts for each individual. Effective posts are selected in such a way that they can be more likely to be reposted by learners based on each learner's interests, knowledge and characteristics. The present paper intends to introduce a new method for selecting k effective posts to ensure the increase of information repost and participation in OLCs. In terms of participation in OLCs, learners are divided into two groups of posters and lurkers. Some solutions are proposed to encourage lurking learners to participate in content repostings. Comprehensive evaluations indicated that the proposed method had significantly solved the presented challenges

A Hybrid Approach for Thread Recommendation in MOOC Forums

Recommender Systems have been developed to provide contents and services compatible to users based on their behaviors and interests. Due to information overload in online discussion forums and users diverse interests, recommending relative topics and threads is considered to be helpful for improving the ease of forum usage. In order to lead learners to find relevant information in educational forums, recommendations are even more needed. We present a hybrid thread recommender system for MOOC forums by applying social network analysis and association rule mining techniques. Initial results indicate that the proposed recommender system performs comparatively well with regard to limited available data from users' previous posts in the forum