Adaptive Collaboration Support for the Web

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Adaptive Collaboration Support for the Web

• Amy Soller
• Institute for Defense Analyses, Alexandria,
  Virginia, U.S.A.

Jonathan Grady October 12, 2005
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References

1. Soller, A. (2005). Adaptive Collaboration Support
   Technology. The Adaptive Web Methods and
   Strategies of Web Personalization. Draft
   Chapter. Springer.
2. Boticario, J., Gaudioso, E., Catalina C. (2003).
   Towards personalised learning communities on the
   Web. In P. Dillenbourg, A. Eurolings, editor.
   Proceedings of the First European Conference on
   Computer-Supported Collaborative Learning, pages
   115-122.
3. Constantino-González, M., Suthers, D. (2003).
   Automated Coaching of Collaboration based on
   Workspace Analysis Evaluation and Implications
   for Future Learning Environments. Proceedings of
   the 36th Hawaii International Conference on the
   System Sciences 2003 32.

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Agenda

• Introduction
• Strategic Pairing and Group Modeling
• Online Knowledge Sharing Discovery
• Collaboration Management Cycle
• Q A Session

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Background

• Many adaptive web techniques help individual
  users find and apply existing knowledge
• Content selection
• Adaptive presentation
• Navigation support
• What if the knowledge doesnt exist?

Introduction
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Background (cont.)
Intelligent Collaborative Learning
Adaptive Group Formation
Virtual Students
Adaptive Collaboration Support
(Adapted from Brusilovsky Peylo, 2003)
Introduction
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Adaptive Collaboration Support

• Adaptive technologies that facilitate, mediate,
  support
• Collaboration
• Interaction
• Knowledge Construction
• Coaches Monitors

Introduction
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Strategic Pairing Group Modeling
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Collaborative Filtering

• Recommend relevant items services, or provide
  guidance to individuals based on user models.
• Generalize info among several user models and
  provide recommendations for the group as a whole.
• Find similarities gt majority appeal

Strategic Pairing Group Modeling
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Building Group Models

• Group models store recommended content user
  reactions to these recommendations
• Elements of group models
• Group performance
• Group history
• Individual member profiles (?)
• Goal is to create groups with dynamics for
  successful collaboration

Strategic Pairing Group Modeling
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Approaches to Pairing Modeling

• 1st approach
• User models are pre-processed
• Groups constructed by selecting the most
  compatible members
• 2nd approach
• Facilitator analyzes group interaction after
  collaboration begins
• Dynamically facilitates group interaction, or
  modifies environment accordingly
• Logs user responses to interventions
• Many systems use a combination of the approaches

Strategic Pairing Group Modeling
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Example IMMEX

• Interactive MultiMedia Exercises
  (http//www.immex.ucla.edu/)
• Online version contains collaborative web
  navigation, synchronization, structured chat
• Constructs user models and predicts future
  learning behavior

Strategic Pairing Group Modeling
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Example IMMEX
Strategic Pairing Group Modeling
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Example IMMEX

• IMMEX aggregates user models to select optimal
  learning partners
• Approach boosts predictive capabilities of user
  models through HMM.
• Initiates collaboration, recommends resources,
  mediates communication
• Continually monitors and predicts problem-solving
  strategies by group members.

Strategic Pairing Group Modeling
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Example aLF WebDL

• Boticario et al. (2003)
• aLF non-adaptive website designed for
  collaborative education (similar to Courseweb)
• WebDL analyzes user/group interactions tailors
  services accordingly
• Multi-agent user modeling
• Advisor agent selects optimal response

Strategic Pairing Group Modeling
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Example aLF WebDL
Strategic Pairing Group Modeling
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Group Dynamics Facilitation

• Chat sequence analysis using HMM to predict
  effectiveness of interaction
• Sentence openers I think..., Do you know...
• Targeted mouse control
• Chiu (2004) if users could not anticipate when
  they would take control of the workspace, they
  became more actively involved in task-oriented
  dialog

Strategic Pairing Group Modeling
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Online Knowledge Sharing Discovery
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Knowledge Discovery

• Communities of Practice vs. Communities of
  Interest
• Shared workspaces vs. user goals
• Public workspaces gt persistent info
• Private workspaces gt transient info
• Social awareness networking tools
• Content, detail, language, time, context
• Visualizations of social network

Online Knowledge Sharing Discovery
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Example LiveJournal
Online Knowledge Sharing Discovery
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Example iVisTo
Online Knowledge Sharing Discovery
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Community Maintenance

• Environment must continue to foster collaboration
• Search Aids metadata, structures, tools
• Moderators
• Cross-community discussion groups
• Annotations of content
• Voting on content relevance

Online Knowledge Sharing Discovery
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Motivation Participation

• Reward members for taking action
• Peer reviews, reputation enhancers
• Trust relationships
• Function of competence, risk, utility, importance
  
• Still relies heavily on personal judgment
• User group models updated to reflect
  constructive feedback

Online Knowledge Sharing Discovery
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Example COLER

• Constantino-Gonzalez, Suthers (2003)

Online Knowledge Sharing Discovery
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Example COLER

• Focused on identifying competing solutions and
  participation level no expert model
• Conducted five experiments with groups of 3
  students
• 73 of generated advice was deemed Worth saying
  by expert
• Most students rated COLERs collaboration support
  as helpful.

Online Knowledge Sharing Discovery
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The Collaboration Management Cycle
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Overview

• Framework for guiding distributed virtual group
  activity

The Collaboration Management Cycle
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Phases 1 2

• Collect (1) Aggregate (2) online interactions
• Represent interactions in a standardized log
  format
• lttime 1400gt ltuser Tomgt ltevent
  clickentity5gt ltchat Im going to...gt

The Collaboration Management Cycle
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Conceptualizing Interactions

• Depends on performance metric
• High-level variables are collaboration or
  skill competency evaluated
• Simple statistics
• Probabilistic models
• Fuzzy logic

The Collaboration Management Cycle
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Phase 3

• Compare observed interaction with desired state
  (based on expert model)
• Must use the same computational representation as
  the observed interaction
• What if there are discrepancies?

The Collaboration Management Cycle
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Phase 4

• Mirroring tools
• Self-reflection and self-mediation
• Metacognitive tools
• Presents representations of both
  actual and potential interactions
• Guiding Systems
• Assess collaborations
• Provide hints coaches

The Collaboration Management Cycle
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Summary

• Adaptive Collaboration Support
• Models based on group interaction theories
• Identify and form optimal groups
• Facilitate and mediate collaboration among group
  members (coach monitor)
• Continually log interactions, adapting mediation
  and environment appropriately

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Questions?