Analyzing Networked Learning Texts

Abstract

Social interactions are essential in understanding the collaborative processes in networked learning environments. Although individuals may learn by retrieving information from online archives, dictionaries and encyclopaedia, it is the interaction with others with similar, perhaps narrowly enjoyed interests that fuels the benefits of networked learning. This paper presents our ongoing work on a novel, automated method for extracting interaction data from threaded discussions of networked learning groups. Using natural language processing, the proposed method reduces large text-based datasets to community and conversational essentials that show the relations of importance to group members. By studying these relations, we hope to identify what matters in terms of learning in the online interaction space and to provide useful representations of online conversations to help networked learners (instructors and students) better understand the social environment in which they are participants. To do so also requires making accurate determinations of who is talking to whom. This paper discusses the methodological issues associated with extracting names from networked learning texts and our procedures for enhancing network information through new techniques of name extraction.