Analyzing Sequential Data in Computer-Supported Collaborative Learning

Dietmar Janetzko (1) , Frank Fischer (2)

1 Institute of Computer Science and Social Research
2 Faculty of Education and Educational Psychology

Representations and changes between them play a major role in cognitive development (e.g., Vosniadou, & Brewer, 1992) and education (e.g., Hewson, Beeth, & Thorley, 1998). 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 present and discuss the methodological aspects of KT and delineate the Web-based computer program (knowledge tracking engine, KTE) set up to run KTanalyses (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.

Mots clés: shared knowledge; probabilistic models; network representation of cognition; change of representation; sequential data

Domaines: Environnements Informatiques pour l'Apprentissage Humain

Dietmar Janetzko, Frank Fischer. Analyzing Sequential Data in Computer-Supported Collaborative Learning. Computer support for collaborative learning: Foundations for a CSCL community (CSCL 2002), 2002, United States. pp.585-586. ⟨hal-00197403⟩