Discourse Centric Learning Analytics: Mapping the Terrain
Keywords:discourse, discourse centric learning analytics, social learning analytics, educational data mining, computer supported collaborative learning, collaborative learning, learning analytics
There is an increasing interest in developing learning analytic techniques for the analysis, and support of, high-quality learning discourse. This paper maps the terrain of discourse-centric learning analytics (DCLA), outlining the distinctive contribution of DCLA and outlining a definition for the field moving forwards. It is our claim that DCLA provides the opportunity to explore the ways in which discourse of various forms both resources and evidences learning; the ways in which small and large groups, and individuals, make and share meaning together through their language use; and the particular types of language — from discipline specific, to argumentative and socio-emotional — associated with positive learning outcomes. DCLA is thus not merely a computational aid to help detect or evidence “good” and “bad” performance (the focus of many kinds of analytics), but a tool to help investigate questions of interest to researchers, practitioners, and ultimately learners. The paper ends with three core issues for DCLA researchers — the challenge of context in relation to DCLA; the various systems required for DCLA to be effective; and the means through which DCLA might be delivered for maximum impact at the micro (e.g., learner), meso (e.g., school), and macro (e.g., government) levels.
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