Knowledge organization through multiple representations in a computer-supported collaborative learning environment

Abstract

Computer-supported collaborative learning (CSCL) environments provide learners with multiple representational tools for storing, sharing, and constructing knowledge. However, little is known about how learners organize knowledge through multiple representations about complex socioscientific issues. Therefore, the purpose of this study was to investigate learners' knowledge organization (KO) through multiple representations in a CSCL environment. We designed a learning unit on nuclear energy and implemented it with a group of 20 college students. the participants used a web-based hypertext KO platform that incorporated three representational modes: textual, pictorial, and concept map. the platform interlinked learners' knowledge entries based on similar keywords. Utilizing mixed methods research we analyzed the individual entries and the knowledge base to determine KO both at the individual and the collective levels. We found that the density of the knowledge base was high; the learners mostly benefited from their text- and concept map-based entries, though the picture-based entries were also an important means for connecting entries with similar content and hence creating a dense knowledge base. Our results suggest that KO with multiple representations can create a more comprehensive knowledge base. Using distinct analytical approaches will allow CSCL researchers to better identify KO both at the individual and collective levels.