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

• Soller, A. (2005). Adaptive Collaboration Support
  Technology. The Adaptive Web Methods and
  Strategies of Web Personalization. Draft
  Chapter. Springer.
• 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.
• 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?