Augmenting Groupware with Intelligence:

1. Augmenting Groupware with Intelligence: Supporting Informal Communication, Trust, and Persona Management Joe Tullio Dissertation Proposal May 1, 2003

2. Overview Introduction/Motivation Informal communication and calendars Intersection of CSCW, IUI, Ubiquitous computing Thesis statement/Contributions Related work Proposed work: Augmenting calendars with attendance predictions Effects of augmented calendars on user attitudes and behaviors Strategies for managing persona in groupware – a taxonomy Timeline for completion

3. Calendars as tools for informal communication Definitions: GCS, Informal communication Studies: Palen, L. (1999) "Social, Individual & Technological Issues for Groupware Calendar Systems", CHI'99. Grudin, J. and Palen, L. (1997) "Emerging Groupware Successes in Major Corporations: Studies of Adoption and Adaptation", WWCA'97. “Calendar work” + • Locating colleagues • Assessing availability • Regulating privacy

4. CSCW framework (Dix 1994) Two people and a shared artifact People interact with one another and with the artifact People even communicate through the artifact

5. Calendars in the CSCW framework Calendar as the shared artifact People communicate informally People maintain and browse calendars People communicate a persona through the calendar

6. IUI/User modeling Calendars can be inaccurate Wrong recurrence boundaries Conflicting events Infrequently attended events We have a basis for modeling Domain knowledge Attendance history There is uncertainty in event attendance Inherent error & misrepresentation Bayesian networks

7. Ubicomp Leverage mobile devices Individual practices Ubiquity of calendars Calendar itself can be used as a sensor in context-aware applications

8. At the intersection Calendar is an artifact supporting informal communication Mobile, individual calendars can exhibit inaccuracies Inaccuracies can be mitigated with intelligent assistance Calendar representation can be seen as persona Persona – One’s representation through a shared artifact Intelligent systems can diminish control over this persona

9. Proposed research Build a prototype groupware calendar that incorporates attendance prediction Use this prototype to explore: Feasibility of a Bayesian model Effect on user communication practices Effect on user attitudes toward adoption, trust Develop a framework for persona management Groupware augmented with intelligence Focus on learning

10. Thesis statement The GCS’s role as a tool for computer-supported cooperative work can be better supported through the application of predictive user models. These models can improve it as a predictor of user activity and consequently as a facilitator of informal communication. I can validate this claim through an exploration of its use in a real-world setting. I can then develop a taxonomy of techniques for managing the persona conveyed by such artifacts along dimensions of the broader class of intelligent groupware applications.

11. Research contributions Technological solution to the problem of inaccurate calendars Impacts many context-aware applications Analysis of feasibility Socio-technical effects of this solution Identify changes in communication patterns Examine user attitudes toward intelligent assistance Persona management Framework for designers and researchers Ground intelligent groupware to the social needs of the workplace

12. Related work:Studying calendars Aforementioned work by Palen, Grudin Academic environment – Mitchell Ubicomp systems with calendars: Horvitz et al - Priorities Tang et al – Awarenex Marx et al - CLUES

13. Related work:Intelligent groupware These systems use different representations, UIs, and learning algorithms… Challenging in terms of evaluating their Effects on communication practices Influence on adoption User trust, especially in early stages of learning Horvitz et al - Coordinate Begole et al - Rhythm Modeling Ashbrook & Starner – GPS, Markov models Hudson et al – Predicting availability with sensors

14. Related work:Learning/Trust/Persona Mechanisms to support trust Some initiated by users, others implemented by designers Assist in learning and to manage appearance Plausible deniability Can use impoverished information, system error to justify absence Maes – Agents for email, meeting scheduling Tiernan/Czerwinski – Notification agents Farnham – Social networks De Angeli et al – Biometric verification

15. Overview Introduction/Motivation Informal communication and calendars CSCW, IUI, Ubiquitous computing Thesis statement/Contributions Related work Proposed work: Augmenting calendars with attendance predictions Effects of augmented calendars on user attitudes and behaviors Strategies for managing persona in groupware – a taxonomy Timeline for completion

16. Augur: A probabilistic shared calendar (Goecks, Nguyen) Calendars shared from personal mobile devices Support individual practices Probabilistic model predicts future attendance at co-scheduled events Make the calendar a better predictor of activity for both workgroups and context-aware applications Visualize predictions in a browsable calendar Awareness for informal communication From Ambush: Support for “ambushing”

17. Representation: Bayesian networks Compact, descriptive representation of a domain with uncertainty Need domain knowledge, some structure Capable of learning over time Capable of generating explanations if needed

18. Augur Bayesian network

19. Augur system architecture

20. Augmented personal calendar

21. Results to date Ambush: Stabilization on routine events SVMs used to identify role, location, event type. Event Type 80% Location 82% Role – more participants needed Publications: Tullio, J., Goecks, J., Mynatt, E., Nguyen, D.  Augmenting Shared Personal Calendars.  UIST 2002. Mynatt, E. and Tullio, J.  Inferring Calendar Event Attendance.  IUI 2001.

22. Feasibility of Augur Do predictions converge with actual attendance over time? What type(s) of events perform better? Is the model’s structure appropriate? Completeness SVMs for event classification

23. User attitudes and behaviors Study Augur’s ability to support informal communication Study attitudes toward trust, adoption Building on the work of corporate calendar studies at Sun, Microsoft, Boeing and others Also designing to the practices of our academic environment Ambushing Personal (PDA-based) calendar practices Noisy calendars

24. Evaluating attitudes/behaviors:Proposed activities Four deployment phases • Preliminary (Summer/Early Fall 2003) • Initial attitudes and practices • Interviews • Calendar deployment (Early Fall 2003) • Collecting training data • Let users become accustomed • Intelligent calendar deployment (Late Fall 2003) • Investigate changes in attitudes/practices over time • Collect measures in accuracy • Persona management (Spring 2004)

25. Participants Study group 20 participants from several FCE labs Both “readers” and “writers” Some working closely, others infrequently Periodic interviews before, during, after deployment Larger pool of readers Expecting advisees, students in courses to read Log accesses to identify browsing patterns Limit reading to school machines

26. Challenges No existing GCS infrastructure Ramp up by first using shared calendar without predictive features Must design for possibly several common tools Dynamic schedules and personnel Some learned patterns are incompatible with changes in term/personnel Attitude changes versus behavior changes Opinions may change without measurable changes in activity

27. Why FCE is promising Open environment Not subject to closed calendars at higher positions in the hierarchy Existing calendar habits Personal, “noisy” calendars the norm Individual calendars demonstrate need for intelligent assistance Abundance of events/activities Ambushing Seems to be a common practice

28. Method - Interviews Augur as a communication tool (behavior): How often did you check your calendar/others’ calendars? To what purpose, if any? How often did you use the predictive features? Did you change your calendaring habits? Trust in intelligent assistance (attitude): How accurate were the predictions for others? Did they seem to improve or degrade? Were you represented accurately? Did you attempt to change your own predictions?

29. Observing behavior Log calendar accesses Browsing up/down the hierarchy Confidentiality Semi-controlled situations Present interviewees with task using the calendar, see how they would accomplish it Control for same calendar information Think-aloud

30. Success metrics System sees use Neglect over the first weeks may necessitate new incentives (or setting) Changes in behavior observed through logs and interviews Changes in attitude evidenced through interviews

31. Persona management • Persona – One’s representation through a shared artifact • Management implies negotiation with the system • Common property of groupware in general • Complicated by intelligence • Important in early stages of learning

32. Management strategies

33. User dimensions

34. Application dimensions

35. Method Populate framework with existing systems In particular, look for examples of complex, intelligent groupware Augur system: support persona management using framework Deploy after term change to explore use Mitigate misrepresentation from error

36. Success metrics Dimensions of framework Does the body of intelligent groupware divide this way? Is the problem much more complex? Utility Does the taxonomy identify new research areas? Does it provide design guidance? Are Augur users able to successfully manage their representation through the shared calendar? Interviews from previous phase

37. Timeline Fall/Spring 2000: Development/Test of Ambush system (published at IUI 2001) Fall/Spring 2001: Development/Test of Augur system (published at UIST 2002) Summer 2003: Prepare Augur for deployment, develop interview questions, solicit participants Literature review/development of framework Fall 2003: Deployment/Evaluation of Augur Begin design/implementation of persona management for Augur Spring 2004: Re-deploy Augur with persona management features Take advantage of schedule change Summer 2004: Writing and defense

38. Acknowledgments • Thanks to Elaine Huang, Jeremy Goecks, David Nguyen, my committee, the Everyday Computing Lab, and everyone else who discussed or critiqued this work with me. • Thanks also to the National Science Foundation • CAREER Award #0092971.