Armin Weinberger

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

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).

Specifying computer-supported collaboration scripts

Collaboration scripts facilitate social and cognitive processes of collaborative learning by shaping the way learners interact with each other. Computer-supported collaboration scripts generally suffer from the problem of being restrained to a specific learning platform. 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).

Analyzing collaborative learning processes automatically: Exploiting the advances of computational linguistics in computer-supported collaborative learning.

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 multi-dimensional 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.

Facilitating argumentative knowledge construction with computer-supported collaboration scripts

Online discussions provide opportunities for learners to engage in argumentative debate, but learners rarely formulate well-grounded arguments or benefit individually from participating in online discussions. Learners often do not explicitly warrant their arguments and fail to construct counterarguments (incomplete formal argumentation structure), which is hypothesized to impede individual knowledge acquisition. Computer-supported scripts have been found to support learners during online discussions. Such scripts can support specific discourse activities, such as the construction of single arguments, by supporting learners in explicitly warranting their claims or in constructing specific argumentation sequences, e.g., argument–counterargument sequences, during online discussions. Participation in argumentative discourse is seen to promote both knowledge on argumentation and domain-specific knowledge. However, there have been few empirical investigations regarding the extent to which computer-supported collaboration scripts can foster the formal quality of argumentation and thereby facilitate the individual acquisition of knowledge. One hundred and twenty (120) students of Educational Science participated in the study with a 2 × 2-factorial design (with vs. without script for the construction of single arguments and with vs. without script for the construction of argumentation sequences) and were randomly divided into groups of three. Results indicated that the collaboration scripts could improve the formal quality of single arguments and the formal quality of argumentation sequences in online discussions. Scripts also facilitated the acquisition of knowledge on argumentation, without affecting the acquisition of domain-specific knowledge.

Learning to argue online: Scripted groups surpass individuals (unscripted groups do not)

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 computer-supported scripts. In this study with a 2x2-factorial design (N=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.

Scripting Argumentative Knowledge Construction in Computer-Supported Learning Environments

Computer-supported collaborative learning (CSCL) environments may encourage learners to engage in argumentative knowledge construction. Argumentative knowledge construction means that learners work together to elaborate on concepts by constructing arguments and counterarguments. This is achieved through discourse with the goal of acquiring knowledge within a specific domain. However, learners may encounter problems relating to one of three dimensions of argumentative knowledge construction. First, learners seem to have difficulties in constructing arguments that contribute to solving the task. Second, learners’ arguments may lack important components such as data and warrants. Third, learners rarely build upon the arguments of their learning partners. Structuring argumentative knowledge construction with collaboration scripts is a promising instructional approach for facilitating specific process dimensions of argumentative knowledge construction. Little is known, however, about how to most effectively facilitate the acquisition of knowledge by directing collaboration scripts at specific dimensions of argumentative knowledge construction. This chapter will outline the theoretical background of argumentative knowledge construction and will then describe script components that target different dimensions of argumentative knowledge construction. The chapter will then discuss the empirical findings of two studies regarding the effects of these script components.

Supporting CSCL with automatic corpus analysis technology

Process analyses are becoming more and more standard in research on computer-supported collaborative learning. This paper presents the rational as well as results of an evaluation of a tool called TagHelper, designed for streamlining the process of multi-dimensional analysis of the collaborative learning process. In comparison with a hand-coded corpus coded with a 7 dimensional coding scheme, TagHelper is able to achieve an acceptable level of agreement (Cohens Kappa of .7 or more) along 6 out of 7 of the dimensions when we commit only to the portion of the corpus where the predictor has the highest certainty. In 5 of those cases, the percentage of the corpus where the predictor is confident enough to commit a code is at least 88% of the corpus. Consequences for theory-building with respect to automatic corpus analysis are formulated. Potential applications as a support tool for process analyses, as real-time support for facilitators of on-line discussions, and for the development of more adaptive instructional support for computer-supported collaboration are discussed.

Epistemic cooperation scripts in online learning environments: fostering learning by reducing uncertainty in discourse?

Using online learning environments in higher education offers innovative possibilities to support collaborative learning. However, online learning creates new kinds of problems for participants who have not previously worked with each other. One of these problems is uncertainty which occurs when participants do not know each other. According to the uncertainty reduction theory, low uncertainty level increases the amount of discourse and decreases the amount of information seeking. Therefore, uncertainty may influence online discourse and learning. This study investigates the effects of an epistemic cooperation script with respect to the amount of discourse, information seeking and learning outcomes in collaborative learning as compared to unscripted collaborative learning. The aim was also to explore how and what kind of information learners seek and receive and how learning partners react to such information exchange. The participants were 48 students who were randomly assigned to groups of three in two conditions, one with and one without an epistemic script. The results indicate that the epistemic script increased the amount of discourse and decreased the amount of information seeking activities. Without an epistemic script, however, learners achieved better learning outcomes. The results of two qualitative case-based analyses on information seeking will also be discussed.

Facilitating argumentative knowledge construction through a transactive discussion script in CSCL

Learning to argue is prerequisite to solving complex problems in groups, especially when they are multidisciplinary and collaborate online. Environments for Computer-Supported Collaborative Learning (CSCL) can be designed to facilitate argumentative knowledge construction. This study investigates how argumentative knowledge construction in multidisciplinary CSCL groups can be facilitated with a transactive discussion script. The script prompts learners to paraphrase, criticize, ask meaningful questions, construct counterarguments, and propose argument syntheses. As part of a laboratory experiment, 60 university students were randomly assigned to multidisciplinary dyads based on their disciplinary backgrounds (i.e. water management or international development studies). These dyads were randomly assigned to a scripted (experimental) or non-scripted (control) condition. They were asked to analyse, discuss, and solve an authentic problem case related to both of their domains, i.e. applying the concept of community-based social marketing in fostering sustainable agricultural water management. The results showed that the transactive discussion script facilitates argumentative knowledge construction during discourse. Furthermore, learners assigned to the scripted condition acquired significantly more domain-specific and domain-general knowledge on argumentation than learners assigned to the unscripted condition. We discuss how these results advance research on CSCL scripts and argumentative knowledge construction.

Computer-supported collaborative learning in higher education: scripts for argumentative knowledge construction in distributed groups

Learners rarely know how to construct knowledge together in argumentation. This experimental study analyzes two computer-supported collaboration scripts, which should facilitate processes and outcomes of argumentative knowledge construction. One script aims to support the construction of single arguments and the other script aims to support the construction of argumentation sequences. Both scripts were varied independently in a 2x2-factorial design. 120 students of Educational Science participated in the study in groups of three. Results show that the computer-supported scripts facilitate specific processes and outcomes of argumentative knowledge construction. Learners with scripts argued better and acquired more knowledge on argumentation than learners without scripts without impeding acquisition of domain specific knowledge.