1. Adaptive Collaboration Support for the Web Amy Soller Institute for Defense Analyses, Alexandria, Virginia, U.S.A. Jonathan Grady October 12, 2005

2. 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.

3. Agenda Introduction Strategic Pairing and Group Modeling Online Knowledge Sharing & Discovery Collaboration Management Cycle Q & A Session

4. Background Many adaptive web techniques help individual users find and apply existing knowledge: –Content selection –Adaptive presentation –Navigation support What if the knowledge doesn’t exist? Introduction

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

6. Adaptive Collaboration Support Adaptive technologies that facilitate, mediate, & support: –Collaboration –Interaction –Knowledge Construction Coaches & Monitors Introduction

7. Strategic Pairing & Group Modeling

8. 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 => majority appeal Strategic Pairing & Group Modeling

9. 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

10. 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

11. Example: IMMEX Interactive MultiMedia Exercises (http://www.immex.ucla.edu/)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

12. Example: IMMEX Strategic Pairing & Group Modeling

13. 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

14. 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

15. Example: aLF + WebDL Strategic Pairing & Group Modeling

16. 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

17. Online Knowledge Sharing & Discovery

18. Knowledge Discovery Communities of Practice vs. Communities of Interest Shared workspaces vs. user goals –Public workspaces => persistent info –Private workspaces => transient info Social awareness & networking tools –Content, detail, language, time, context –Visualizations of social network Online Knowledge Sharing & Discovery

19. Example: LiveJournal Online Knowledge Sharing & Discovery

20. Example: iVisTo Online Knowledge Sharing & Discovery

21. 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

22. 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

23. Example: COLER Constantino-Gonzalez, Suthers (2003) Online Knowledge Sharing & Discovery

24. 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 COLER’s collaboration support as helpful. Online Knowledge Sharing & Discovery

25. The Collaboration Management Cycle

26. Overview The Collaboration Management Cycle Framework for guiding distributed virtual group activity

27. Phases 1 & 2 Collect (1) &Aggregate (2) online interactions Represent interactions in a standardized log format: – The Collaboration Management Cycle

28. 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

29. 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

30. 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

31. 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

32. Questions?