Where social noise and structure converge : learning with social semantics

Abstract

Social constructivism suggests that learning is an inherently social phenomenon. The ap- proaches presented in this dissertation emphasize the social nature of information, knowledge and language, using a combination of ontologies, reference repositories, folksonomy analysis, graph-based disambiguation and topic modeling. Differences between the vocabulary of an individual and that of a Community of Practice present both a problem and an opportunity. The problem is that it can impede access to appropriate resources for learning, e.g. keyword-based search can be very sensitive to subtle lexical differences. I have shown that poor alignment of an individual’s vocabulary with that of a community can be addressed using enriched ontologies in combi- nation with semantic search. The opportunity is that analysis of the vocabulary and ‘lexical competence’ of an individual reveals his or her conceptual knowledge and allows computer algorithms to assist individuals with reflection and critical thinking. Uptake of the proper community vocabulary is a good predictor of an individual’s integration with a community, because it signifies the acceptance and acquisition of the community’s conceptualization and vocabulary. The tight integration between conceptual knowledge and vocabulary usage is useful in the context of assessment and personalization in order to tailor resources and individuals to the appropriate level of knowledge by means of topic models.