2 research outputs found
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