Will Structuring the Collaboration of Students Improve Their Argumentation?

Learning to argue in a computer-mediated and structured fashion is investigated in this research. A study was conducted to compare dyads that were scripted in their computer-mediated collaboration with dyads that were not scripted. A process analysis of the chats of the dyads showed that the scripted experimental group used significantly more words, engaged in significantly more broadening and deepening of the discussion, and appeared (in a qualitative sense) to engage in more critical and objective argumentation than the non-scripted control group</p

Learning Chemistry through Collaboration: A Wizard-of-Oz Studt of Adaptive Collaboration Support

Chemistry students often learn to solve problems by applying well-practiced procedures, but such a mechanical approach is likely to hinder conceptual understanding. We have developed a system aimed at promoting conceptual learning in chemistry by having dyads collaborate on problems in a virtual laboratory (VLab), assisted by a collaboration script. We conducted a small study to compare an adaptive and a non-adaptive version of the system, with the adaptive version controlled by a human wizard. Analyses showed a tendency for the dyads in the adaptive condition to collaborate better and to have better conceptual understanding. We present our research framework, our collaborative software environment, and results from the wizard-of-oz study

Using Machine Learning Techniques to Analyze and Support Mediation of Student E-Discussions

Students are starting to use networked visual argumentation tools to discuss, debate, and argue with one another about topics presented by a teacher. However, this development gives rise to an emergent issue for teachers: how do they support students during these e-discussions? The ARGUNAUT system aims to provide the teacher (or moderator) with tools that will facilitate effective moderation of several simultaneous e-discussions. Awareness Indicators, provided as part of a moderator’s user interface, help monitor the progress of discussions on several dimensions (e.g., critical reasoning). In this paper we discuss preliminary steps taken in using machine learning techniques to support the Awareness Indicators. Focusing on individual contributions (single objects containing textual content, contributed in the visual workspace by students) and sequences of two linked contributions (two objects, the connection between them, and the students’ textual contributions), we have run a series of machine learning experiments in an attempt to train classifiers to recognize important student actions, such as using critical reasoning and raising and answering questions. The initial results presented in this paper are encouraging, but we are only at the beginning of our analysis

Combining Structural Process-Oriented and Textual Elements to Generate Awareness Indicators for Graphical E-Discussions

Moderation of e-discussions can be facilitated by online feedback promoting awareness and understanding of the ongoing discussion. Such feedback may be based on indicators, which combine structural and process-oriented elements (e.g., types of connectors, user actions) with textual elements (discussion content). In the ARGUNAUT project (IST-2005027728, partially funded by the EC, started 12/2005) we explore two main directions for generating such indicators, in the context of a synchronous tool for graphical e-discussion. One direction is the training of machine-learning classifiers to classify discussion units (shapes and paired-shapes) into predefined theoretical categories, using structural and process-oriented attributes. The classifiers are trained with examples categorized by humans, based on content and some contextual cues. A second direction is the use of a pattern matching tool in conjunction with e-discussion XML log files to generate "rules" that find "patterns" combining user actions (e.g., create shape, delete link) and structural elements with content keywords