Elsevier

Computers & Education

Volume 46, Issue 1, January 2006, Pages 71-95
Computers & Education

A framework to analyze argumentative knowledge construction in computer-supported collaborative learning

https://doi.org/10.1016/j.compedu.2005.04.003Get rights and content

Abstract

Computer-supported collaborative learning (CSCL) is often based on written argumentative discourse of learners, who discuss their perspectives on a problem with the goal to acquire knowledge. Lately, CSCL research focuses on the facilitation of specific processes of argumentative knowledge construction, e.g., with computer-supported collaboration scripts. In order to refine process-oriented instructional support, such as scripts, we need to measure the influence of scripts on specific processes of argumentative knowledge construction. In this article, we propose a multi-dimensional approach to analyze argumentative knowledge construction in CSCL from sampling and segmentation of the discourse corpora to the analysis of four process dimensions (participation, epistemic, argumentative, social mode).

Section snippets

Argumentative knowledge construction in computer-supported collaborative learning – theoretical background

Argumentative knowledge construction is based on the assumption that learners engage in specific discourse activities and that the frequency of these discourse activities is related to knowledge acquisition. Learners construct arguments in interaction with their learning partners in order to acquire knowledge about argumentation as well as knowledge of the content under consideration (Andriessen, Baker, & Suthers, 2003). This definition of argumentative knowledge construction includes that

Organization of the discourse corpora

Before analyzing raw discourse corpora (see Appendix A), the material needs to be organized, which particularly means sampling and segmenting the discourse corpora.

Process dimensions of argumentative knowledge construction in CSCL

Once a sample of the discourse corpora has been segmented, all segments can be coded with a set of categories. Categories should help to measure the constructs of the research questions. Assuming that collaborative learning does not comprise one single learning mechanism, we need to analyze multiple dimensions of learners’ discourse. Whereas dimensions such as participation can be measured objectively and reliably (e.g., by counting the number of words), other dimensions require a qualitative

Using the framework – Exemplary results of empirical studies

This framework has been applied in a series of studies with more than 600 participants that investigated the effects of instructional support in the form of computer-supported collaboration scripts that aim to support specific process dimensions of argumentative knowledge construction on processes and outcomes of argumentative knowledge construction (see Weinberger et al., in press-b, Weinberger et al., 2005). We investigated the effects of epistemic, argumentative and social script components

Summary of the framework and open questions

Quantitatively analyzing argumentative knowledge construction requires researchers to make decisions with respect to several questions. Considering theoretical background and research questions, discourse corpora need to be sampled, segmented and categorized. In a first step, discourse data in the CSCL context typically needs to be reduced. Even though time sampling methods have shown to be reliable means to measure frequencies of discourse activities, the analysis of coherent subsets of the

Acknowledgments

Armin Weinberger and Frank Fischer, Knowledge Media Research Center, Tübingen, Germany.

We developed this framework to analyze argumentative knowledge construction in computer-supported collaborative learning in the context of experimental studies funded by the DFG (Deutsche Forschungsgemeinschaft). We would like to thank Alexandra Gabler for her help with the coding scheme and training the coders.

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