A framework to analyze argumentative knowledge construction in computer-supported collaborative learning
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.
References (69)
- et al.
Eliciting self-explanations improves understanding
Cognitive Science
(1994) - et al.
Fostering collaborative knowledge construction with visualization tools
Learning and Instruction
(2002) - et al.
Patterns of female and male students’ participation in peer interaction in computer-supported learning
Computers & Education
(2003) - et al.
Group decision making and communication technology
Organizational Behavior and Human Decision Processes
(1992) - et al.
Epistemic cooperation scripts in online learning environments: Fostering learning by reducing uncertainty in discourse?
Computers in Human Behavior
(2005) - et al.
Social-cognitive behaviors and higher-order thinking in educational computer environments
Learning and Instruction
(1992) Peer interaction and learning in small groups
International Journal of Educational Research
(1989)Computer-mediated argumentative interactions for the co-elaboration of scientific notions
- et al.
Promoting reflective interactions in a CSCL environment
Journal of Computer Assisted Learning
(1997)