Facilitate the Facilitator

Awareness Tools to Support the Moderator to Facilitate Online Discussions for Networked Learning

Abstract

This paper is part of an ongoing European research project, called ARGUNAUT. We would like to present some of our findings regarding the development of online awareness tools supporting the moderator to best facilitate online discussions. The ARGUNAUT system is based on synchronous learning and embeds an integrated suite of tools in order to set up and moderate synchronous discussions. Our focus in this paper is on the Moderators Interface (MI), which is a tool especially designed for the moderator. This tool allows moderators to log on to one or more ongoing discussions and provides the moderator a set of awareness indicators to be used to remotely facilitate these discussions.
The central idea behind this project is to develop these awareness indicators, using data mining techniques and artificial intelligence trained on pedagogically annotated events of teaching and learning activities in online discussions, to inform the moderator about teaching and learning activities that occur in online discussions. The challenge is to find ways in which pedagogical ideas of online argumentation, teaching and learning processes can be articulated into rules used by the awareness tools to find patterns or examples of particular networked learning behaviour that signify these rules.

In this paper we firstly discuss our theoretical orientation towards online argumentation and dialogues. This results in a multidimensional analytical framework to analyse synchronous discussions in order to develop awareness tools for the MI. Secondly an overview of the ARGUNAUT system is presented focussing on the use of awareness indicators by moderators to facilitate their facilitation of online discussions. Using the MI the moderator can select a visualisation of the actual discussion graph as it is created by the students, in order to read their contributions. Furthermore the moderator can request various descriptive statistical information about the number and type of contributions made by students. More advanced visualisations for example show live interaction patterns of the participants using social network analysis (SNA) techniques and the Deep Loop classification of contributions written by the students. The Deep Loop, is an AI-based awareness indicator that can automatically detect for example if students are talking of / on-task, critical reasoning and question-answer patterns.

In addition to these awareness indicators the MI provides the moderators with a remote control which can be used by the moderator to directly facilitate the discussion. Based on MI observations the moderator can for example choose to send a pop-up message to a particular student, group of students or the entire class and guide them in their learning activities.

This paper ends with a discussion of current discourse analysis done by our team aimed at the identification of important critical moments in the discussion that feature particular dialogic properties and might need the attention of the moderator. Our research is currently focused on detecting patterns within the discussion that show elements of deepening and widening. When thinking about acts of deepening and widening (critical reasoning and dialogic reasoning), from a dialogical point of view the widening moves in particular are of great interest since not much is known about how widening moves are triggered. A widening move is often a creative act, i.e. the ability to step back and come with a new ‘solution’ not thought of before. Such moves however are very important in discussions as they stimulate people to think ‘out of the box’, and or stimulate further creative thinking amongst the group members. Our preliminary findings suggests that these critical moments coincide with moments where a discussion branching into several directions.

If branching events might be an indication of critical moments in the discussion, we need to see if they can be patterned in some way and test the possibility of using artificial intelligence to train classifiers. Being able to detect these moments automatically will mean a major step forward for online moderation and student engagement in rich dialogues representing multiple alternative points of view.