Supporting Social Interaction in an Intelligent Collaborative Learning System
12 (1): "Part II of the Special Issue on Analysing Educational Dialogue Interaction"
Students learning effectively in groups encourage each other to ask questions, explain
and justify their opinions, articulate their reasoning, and elaborate and reflect upon their
knowledge. The benefits of collaborative learning, however, are only achieved by active, wellfunctioning
teams. This paper presents a model of collaborative learning designed to help an
intelligent collaborative learning system identify and target group interaction problem areas.
The model describes potential indicators of effective collaborative learning, and for each
indicator, recommends strategies for improving peer interaction. This collaborative learning
model drove the design and development of two tools that automate the coding, and aid the
analysis of collaborative learning conversation and activity. Empirical evaluation of these tools
confirm that effective learning teams are comprised of active participants who demand
explanations and justification from their peers. The distribution of conversational skills used by
members of a supportive group committed to their teammates' learning is compared to that of
an unfocused, unsupportive group. The results suggest that structured, high-level knowledge of
student conversation in context may be sufficient for automating the assessment of group
interaction, furthering the possibility of an intelligent collaborative learning system that can
support and enhance the group learning process.