Learning focused search in an online social network community.

Abstract

Current search engines satisfactorily return relevant, ranked results to most posed queries. However, when searching on a dense topic for individual or collaborative learning purposes, the highest ranked results retrieved by these engines might not be the best starting point for learners given their current level of competence. We leverage concepts and computational solutions related to peer knowledge and interaction data in order to convert ranked search results in So.cl into sequenced results that allow learners to start with sources that are accessible and understandable before moving to increasingly advanced and complex content.

Keywords: social search, collaborative learning