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).

Collaboration scripts are activity programs which aim to foster collaborative learning by structuring interaction between learners. Computer-supported collaboration scripts generally suffer from the problem of being restrained to a specific learning platform and learning context. A standardization of collaboration scripts first requires a specification of collaboration scripts that integrates multiple perspectives from computer science, education and psychology. So far, only few and limited attempts at such specifications have been made. This paper aims to consolidate and expand these approaches in light of recent findings and to propose a generic framework for the specification of collaboration scripts. The framework enables a description of collaboration scripts using a small number of components (participants, activities, roles, resources and groups) and mechanisms (task distribution, group formation and sequencing). However, when learners are left to their own devices, they rarely engage in productive interactions such as asking each other questions, explaining and justifying their opinions, articulating their reasoning, or elaborating and reflecting upon their knowledge. Collaboration scripts aim to foster collaborative learning in shaping the way in which learners interact with one another. In specifying a sequence of learning activities, together with appropriate roles for the learners, collaboration scripts are designed to trigger engagement in social and cognitive activities that would otherwise occur rarely or not at all.Collaboration scripts are based upon the scripted cooperation approach, as described by O'Donnell (1999), which differs from other collaborative learning approaches chiefly in the fact that it focuses on the specific activities that learners are expected to engage in, whereas others leave them unspecified or vague. In targeting those activities which have

Collaborative learning in computer-supported learning environments typically means that learners work on tasks together, discussing their individual perspectives via text-based media or videoconferencing, and consequently acquire knowledge. Collaborative learning, however, is often sub-optimal with respect to how learners work on the concepts that are supposed to be learned and how learners interact with each other. Therefore, instructional support needs to be implemented into computer-supported collaborative learning environments. One possibility to improve collaborative learning environments is to conceptualize scripts that structure epistemic activities and social interactions of learners. In this contribution, two studies will be reported that investigated the effects of epistemic and social scripts in a text-based computer-supported learning environment and in a videoconferencing learning environment in order to foster the individual acquisition of knowledge. In each study the factors "epistemic script" and "social script" have been independently varied in a 2×2-factorial design. 182 university students of Educational Science participated in these two studies. Results of both studies show that social scripts can be substantially beneficial with respect to the individual acquisition of knowledge, whereas epistemic scripts apparently do not lead to the expected effects. Keywords

In this article we describe the emerging area of text classification research focused on the problem of collaborative learning process analysis both from a broad perspective and more specifically in terms of a publicly available tool set called TagHelper tools. Analyzing the variety of pedagogically valuable facets of learners' interactions is a time consuming and effortful process. Improving automated analyses of such highly valued processes of collaborative learning by adapting and applying recent text classification technologies would make it a less arduous task to obtain insights from corpus data. This endeavor also holds the potential for enabling substantially improved on-line instruction both by providing teachers and facilitators with reports about the groups they are moderating and by triggering context sensitive collaborative learning support on an as-needed basis. In this article, we report on an interdisciplinary research project, which has been investigating the effectiveness of applying text classification technology to a large CSCL corpus that has been analyzed by human coders using a theory-based multidimensional coding scheme. We report promising results and include an in-depth discussion of important issues such as reliability, validity, and efficiency that should be considered when deciding on the appropriateness of adopting a new technology such as TagHelper tools. One major technical contribution of this work is a demonstration that an important piece of the work towards making text classification technology effective for this purpose is designing and building linguistic pattern detectors, otherwise known as features, that can be extracted reliably from texts and that have high predictive power for the categories of discourse actions that the CSCL community is interested in.

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This is a pre-print version of the journal article, published in Learning and Instruction, 2007, 17 (4), copyright of Elsevier Ltd. http://dx.doi.org/10.1016/j.learninstruc.2007.03.007In collaborative learning the question has been raised as to how learners in small groups influence one another and converge or diverge with respect to knowledge. This article conceptualizes knowledge convergence and further provides measures for its assessment prior to, during, and subsequent to collaborative learning. In an exemplary study in the field of computer-supported collaborative learning with forty-eight (48) locally distant participants in 16 groups of three, we apply these measures and analyze the extent to which a computer-supported collaboration script can affect knowledge convergence. The study provides evidence for the applicability and sensitivity of the proposed knowledge convergence measures. Findings demonstrate that the instructional support increased productive divergence during collaboration and convergent individual outcomes

Students often face process losses when learning together via text-based online environments.Computer-supported collaboration scripts can scaffold collaborative learning processes by distributing roles and activities and thus facilitate acquisition of domain-specific as well as domain-general knowledge, such as knowledge on argumentation. Possibly, individual learners would require less additional support or could equally benefit from computersupported scripts. In this study with a 2×2-factorial design ( = 36) we investigate the effects of a script (with versus without) and the learning arrangement (individual versus collaborative) on how learners distribute content-based roles to accomplish the task and argumentatively elaborate the learning material within groups to acquire domain-specific and argumentative knowledge, in the context of a case-based online environment in an Educational Psychology higher education course. A large multivariate interaction effect of the two factors on learning outcomes could be found, indicating that collaborative learning outperforms individual learning regarding both of these knowledge types if it is structured by a script. In the unstructured form, however, collaborative learning is not superior to individual learning in relation to either knowledge type. We thus conclude that collaborative online learners can benefit greatly from scripts reducing process losses and specifying roles and activities within online groups.