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Supporting collaborative learning and e-Discussions using artificial intelligence techniques.

Bruce McLaren; Oliver Scheuer; Jan Mik¨átko
In: International Journal on Artificial Intelligence in Education (IJAIED), Vol. 20, No. 1, Pages 1-46, IOS Press, 2010.


An emerging trend in classrooms is the use of networked visual argumentation tools that allow students to discuss, debate, and argue with one another in a synchronous fashion about topics presented by a teacher. These tools are aimed at teaching students how to discuss, argue and think critically, important skills not often taught in traditional classrooms. Yet how do teachers support students during these e-discussions, which happen at a rapid pace, with possibly many groups of students working simultaneously? Our approach is to pinpoint and summarize important aspects of the discussions (e.g., Are students staying on topic? Are students making reasoned claims and arguments that respond to the claims and arguments of their peers?) and alert the teachers who are moderating the discussions. The key research question of this work is: Is it possible to automate the identification of salient contributions and patterns in student e-discussions? In this talk, I will present the systematic approach we have taken, based on artificial intelligence (AI) techniques and empirical evaluation, to grapple with this question, which involved the development of machine-learned classifiers and a novel AI-based graph-matching algorithm that classifies arbitrarily sized clusters of contributions. We have run systematic empirical evaluations of the resultant classifiers using actual classroom data. Our evaluations have uncovered satisfactory or better results for many of the classifiers and have eliminated others. Based on this work, we have been developing, over the past four years, a general, web-based learning environment -- LASAD ( -- that we hope can support argumentation learning and this type of automated analysis in a variety of contexts. This work contributes to the fields of computer-supported collaborative learning and artificial intelligence in education by introducing sophisticated and empirically evaluated analysis techniques that combine structural, textual, and temporal data.