Augmenting Groupware with Intelligence:Supporting Informal Communication, Trust, and Persona Management

Joe Tullio

Dissertation ProposalMay 1, 2003

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Overview

Introduction/MotivationInformal communication and calendarsIntersection of CSCW, IUI, Ubiquitous computing

Thesis statement/ContributionsRelated workProposed work:

Augmenting calendars with attendance predictionsEffects of augmented calendars on user attitudes and

behaviorsStrategies for managing persona in groupware – a

taxonomyTimeline for completion

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Calendars as tools for informal communication

Definitions: GCS, Informal communicationStudies:

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

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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

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Calendars in the CSCW framework

Calendar as the shared artifactPeople communicate informallyPeople maintain and browse calendarsPeople communicate a persona through the calendar

IUI/User modeling

Calendars can be inaccurateWrong recurrence boundariesConflicting eventsInfrequently attended events

We have a basis for modelingDomain knowledge Attendance history

There is uncertainty in event attendanceInherent error & misrepresentationBayesian networks

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Ubicomp

Leverage mobile devicesIndividual practices

Ubiquity of calendarsCalendar itself can be used as a

sensor in context-aware applications

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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 personaPersona – One’s representation through a

shared artifactIntelligent systems can diminish control

over this persona

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Proposed research

Build a prototype groupware calendar that incorporates attendance prediction

Use this prototype to explore:Feasibility of a Bayesian modelEffect on user communication practicesEffect on user attitudes toward adoption, trust

Develop a framework for persona managementGroupware augmented with intelligenceFocus on learning

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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.

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Research contributions

Technological solution to the problem of inaccurate calendarsImpacts many context-aware applicationsAnalysis of feasibility

Socio-technical effects of this solutionIdentify changes in communication patternsExamine user attitudes toward intelligent

assistancePersona management

Framework for designers and researchersGround intelligent groupware to the social

needs of the workplace

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Related work:Studying calendars

Aforementioned work by Palen, Grudin

Academic environment – MitchellUbicomp systems with calendars:

Horvitz et al - PrioritiesTang et al – AwarenexMarx et al - CLUES

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Related work:Intelligent groupware

These systems use different representations, UIs, and learning algorithms…

Challenging in terms of evaluating theirEffects on communication practices Influence on adoptionUser trust, especially in early stages of learning

Horvitz et al - CoordinateBegole et al - Rhythm ModelingAshbrook & Starner – GPS, Markov modelsHudson et al – Predicting availability with sensors

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Related work:Learning/Trust/Persona

Mechanisms to support trustSome initiated by users, others implemented by

designersAssist in learning and to manage appearance

Plausible deniabilityCan use impoverished information, system error to

justify absence

Maes – Agents for email, meeting schedulingTiernan/Czerwinski – Notification agentsFarnham – Social networksDe Angeli et al – Biometric verification

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Overview

Introduction/MotivationInformal communication and calendarsCSCW, IUI, Ubiquitous computing

Thesis statement/ContributionsRelated workProposed work:

Augmenting calendars with attendance predictionsEffects of augmented calendars on user attitudes and

behaviorsStrategies for managing persona in groupware – a

taxonomyTimeline for completion

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Augur: A probabilistic shared calendar (Goecks, Nguyen)

Calendars shared from personal mobile devicesSupport individual practices

Probabilistic model predicts future attendance at co-scheduled eventsMake the calendar a better predictor of activity for

both workgroups and context-aware applications

Visualize predictions in a browsable calendarAwareness for informal communication

From Ambush: Support for “ambushing”

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Representation: Bayesian networks

Compact, descriptive representation of a domain with uncertainty

Need domain knowledge, some structure

Capable of learning over timeCapable of generating explanations

if needed

Augur Bayesian network

Augur system

architecture

Augmented personal calendar

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Results to date

Ambush: Stabilization on routine eventsSVMs 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.

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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

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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 environmentAmbushingPersonal (PDA-based) calendar practicesNoisy calendars

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Evaluating attitudes/behaviors:Proposed activities

Four deployment phases1. Preliminary (Summer/Early Fall 2003)

• Initial attitudes and practices• Interviews

2. Calendar deployment (Early Fall 2003)• Collecting training data• Let users become accustomed

3. Intelligent calendar deployment (Late Fall 2003)• Investigate changes in attitudes/practices over time• Collect measures in accuracy

4. Persona management (Spring 2004)

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Participants

Study group20 participants from several FCE labsBoth “readers” and “writers”Some working closely, others infrequentlyPeriodic interviews before, during, after

deployment

Larger pool of readersExpecting advisees, students in courses to read Log accesses to identify browsing patternsLimit reading to school machines

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Challenges

No existing GCS infrastructureRamp up by first using shared calendar

without predictive featuresMust design for possibly several common tools

Dynamic schedules and personnelSome learned patterns are incompatible with

changes in term/personnel

Attitude changes versus behavior changesOpinions may change without measurable

changes in activity

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Why FCE is promising

Open environmentNot subject to closed calendars at higher

positions in the hierarchyExisting calendar habitsPersonal, “noisy” calendars the norm

Individual calendars demonstrate need for intelligent assistance

Abundance of events/activitiesAmbushing

Seems to be a common practice

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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?

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Observing behavior

Log calendar accessesBrowsing up/down the hierarchyConfidentiality

Semi-controlled situationsPresent interviewees with task using

the calendar, see how they would accomplish it

Control for same calendar informationThink-aloud

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Success metrics

System sees useNeglect over the first weeks may

necessitate new incentives (or setting)

Changes in behavior observed through logs and interviews

Changes in attitude evidenced through interviews

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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

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Management strategies

Strategy Description Example

Social norms Users develop new social practices to deal with a shared artifact.

Plausible deniab ility of instant messenger response.

Preferences Users configure settings that control how their informat ion is gathered and used.

Access-control lists, privacy settings in shared calendars.

Gaming Trial-and-error manipulation of inputs eventually results in learning a working conceptual model of the system.

Using particular keywords in email to boost its priority.

Learning Preferences are learned over time by unsupervised or example-based techniques.

Automatic scheduling systems for shared calendars.

Editing of output The system’s output is edited directly by the user to a desired state. Changes are possibly fed back into the system’s user model.

Customized status informat ion on instant messaging clients.

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User dimensions

Dimension Description Effects Privacy preferences User’s attitude toward what

informat ion should be shared and what should be kept private.

May impose restrictions on use of personal information and thus permit a less rich persona.

Collocatedness To what degree a user is geographically near her colleagues.

Collocated users may rely more on social norms to resolve issues concerning persona.

Frequency of interaction To what degree a user interacts with her colleagues in the workplace.

Coworkers with little interaction may rely more on technological means of persona management.

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Application dimensions

Dimension Description Example Many: Fogarty et al [32] Number of inputs The variety and amount of personal

informat ion used by the system. Few: Instant messenger status

Many: Augur Number of outputs The number of components comprising the machine-generated persona. Few: Instant messenger

status Accessible: Calendar events Accessibility of inputs Ease with which users can control the

informat ion used by the system. Less Accessible:

A/V sensors

Simple: Basic open-access calendar

Complexity of algorithm Complexity of the process which transforms personal informat ion into a shared persona. Complex: Augur

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Method

Populate framework with existing systems

In particular, look for examples of complex, intelligent groupware

Augur system: support persona management using frameworkDeploy after term change to explore useMitigate misrepresentation from error

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Success metrics

Dimensions of frameworkDoes the body of intelligent groupware divide

this way?Is the problem much more complex?

UtilityDoes 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

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TimelineFall/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 frameworkFall 2003:

Deployment/Evaluation of AugurBegin 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

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 FoundationCAREER Award #0092971.