Analyzing Sequential Data in Computer-Supported Collaborative Learning

Abstract

Representations and changes between them play a major role in education (e.g., Hewson, Beeth, & Thorley, 1998), problem solving (e.g., Bauer & Reiser, 1990), cognitive development (e.g., Vosniadou & Brewer, 1992), and the history of science (e.g., Kuhn, 1970). By definition, change of representations is also indispensable for collaborative work since a common understanding or shared knowledge can only be achieved by a partial convergence of the knowledge structures of the collaborating subjects. This articles presents and discusses knowledge tracking (KT), viz., an approach to analyze cognition on the basis of symbolic sequential data. We describe the methodological aspects of KT and delineate the Web-based computer program (knowledge tracking engine, KTE) set up to run KT-analyses (http://www.knowledge-tracking.com/). An empirical study in collaborative learning is taken to exemplify the usage of KT in analysis of computer supported collaboration.