Supporting computer-supported argumentative knowledge construction in multi-disciplinary groups of learners

TL;DR: The results showed that the transactive argumentative script in forms of question prompts improve learners’ argumentative structure during online discussion as well as individual domain-specific knowledge acquisition as the final product.

Abstract: Learning to argue is an essential objective in education and the ability to argue is a key skill in approaching complex problems as well as in collecting observational data and applying rules of formal logic. External representations such as computer support can foster interaction and discussion of collaborating partners and also argumentation only implicitly. Orchestration of argumentation and discussion in online learning environments in what has been named Computer Supported Collaborative Learning (CSCL) builds on multiple representations and instructional interventions. Argumentative knowledge construction is one of the most prominent scenarios in online collaborative learning environments that have been subjects of interest to many scholars in the domain of education and educational research. In argumentative knowledge construction, learners are supposed to build arguments and support a position, to consider and weigh arguments and counter-arguments, to test, enlighten, and clarify their uncertainties, to elaborate the learning material, and thus acquire knowledge and achieve understanding about complex ill-structured problems. The current empirical study used a set of transactive argumentation script to facilitate both process and outcome categories of argumentative knowledge construction within the context of multi-disciplinary learners in CSCL. The design of this transactive argumentation script builds on and modifies the coding scheme from Berkowitz and Gibbs (1983) that provide an extensive categorization of transactive contributions which have been regarded as important tool for learning. The results showed that the transactive argumentative script in forms of question prompts improve learners’ argumentative structure (i.e. formal quality of single arguments and argumentation sequences) during online discussion as well as individual domain-specific knowledge acquisition as the final product.