techniques and tools for field-based early-stage study and iteration of ubicomp applications

Techniques and tools for field-based early-stage studyand iteration of Ubicomp applications

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Scott CarterEECS DepartmentUniversity of California, [email protected]

CommitteeJennifer Mankoff (chair), Anind Dey, John Canny, Peter Lyman

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what are ubicomp apps? examples…social proximity jabberwocky (paulos ’04)

location urban tapestry (lane ‘05)

activity

CarterTechniques and tools for field-based early-stage study and iteration of Ubicomp applications

resource queries transit services tour guide (abowd ’97)

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capture and access field data health (alivetech ’05) reminder apps


gaming cysmn (blast theory ’01)

location,proximity-based

what are ubicomp apps? examples…

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ubicomp evaluation is hard (Carter ’05)

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ubicomp evaluation is hard

rapid prototyping

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rapid prototyping

scale

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rapid prototyping

scale

unobtrusiveness

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rapid prototyping

scale

unobtrusiveness

data sparsity

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thesis

Leverage pre-existing infrastructures and the increasing power of mobile devices to address the challenges of early stage field evaluations of ubicomp applications.

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why early stage and field?

early stage

field

>> potential to iterate>> could answer some research questions

>> realism especially important in ubicomp >> Sharp & Rehman ‘05: “Real-world deployment is the only way”>> Kjeldskov: might be able to recreate field in a lab

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approach: WoZ

researcher designs app behavior in momento and provides instructions for wizards

wizards use momento to simulate app behavior

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approach: report methods

researcher designs experiment, configures momento or reporter

researcher runs experiment, monitoring tool

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limitations: when challenges do not applydata easy to get (no sparsity)

infrastructure already available (no scale issues)

target users early, early adopters (no unobtrusiveness)

tools off-the-shelf (no prototyping)

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most relevant related work

rapid prototyping

Topiary (Li ’04)

scale

unobtrusiveness

data sparsity

rapid prototypingContextESM (Intille ’03)

scale

unobtrusiveness

data sparsity

WoZ Report methods

SUEDE (Klemmer ‘00)

DART (Dow ‘05)

Doorman (Makela ‘01)

Crossweaver (Sinha ‘03)

Active Capture (Chang ‘05)

other work

iESP (Intel ’03)

Mobile probes (Hulkko, ‘04)

other work

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outline

ubicomp challenges case study

report methods

momento

validation plans

timeline

summary

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ubicomp challenges case study: dr. bluesocial mobile game

get points by collecting cures, healing the sick

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ubicomp challenges case study: dr. blue

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ubicomp challenges case study: dr. blue

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case study: dr. blue: rapid prototypingmajor difficulty: building a mobile application

communicationsensordisplay

also difficult: game engine

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case study: dr. blue: scale

everyone needs a device and recording method

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case study: dr. blue: unobtrusivenessnew device and interface

interruptions: evaluation and application behavior

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case study: dr. blue: sparsity

encounters may be limited

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outline


report methods

momento

validation plans

timeline

summary

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momento

WoZ simulation using SMS/MMS infrastructure


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momento

WoZ simulation using SMS/MMS infrastructure

… and J2ME on the mobile device


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momento

input and output


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momento: building dr. blue


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momento: limitations

SMS/MMS, wizard latency


24-hour wizards?

synchronous and streaming data

gui creation (damask – Lin ’02)

momento: summary


scale

unobtrusivenessdata sparsity

rapid prototypingany device supporting sms/mms

esm feedback on critical events

easy to change application behavior

integrate context

more control

participants

rules to take advantage of external services

wizards

familiar devices, but interruptions

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outline


report methods

momento

validation plans

timeline

summary

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report methods

diary study

experience-sampling method (esm)


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diary study issues


unobtrusiveness

data sparsity

scale

neglect due to burden of study

generic responses

no feedback

data re-entry, transcribing

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diary study technique (Carter & Mankoff ’05)

capture

annotate

upload

feedback

elicitation


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diary study technique: reporter


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reporter


scale


rapid prototypingany digital capture device

rapid feedback cycle

-

familiar devices

no interruptions

no transcribing

responses tied to captured artifacts feedback not in situ

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esm: momento


scheduled or context events

automatic question formatting

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esm: momento


scale


many devices

responses in situ

rapid prototypingknowledge feedback to application behavior

redirect questions

participants

integrate context

familiar devices, but interruptions

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outline


report methods

momento

validation plans

timeline

summary

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validation: diary study pipeline

testbed: diary study at uc berkeley

interviews with researchers

compare to traditional diary studies (literature)

unobtrusiveness

data sparsity

scale

success?

fewer instances of neglect due to burden of study

more specific recall, fewer generic responses

reduce time to data analysis (no data re-entry)

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validation: momento esm study

sensor data for awareness app

location, activity data

interviews with wizards, participants

unobtrusiveness

data sparsity

scale

success?

minimal neglect due to burden of study

useful feedback on critical events

wizards able to manage momento queries and complete other work

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validation: momento scale study

implement dr. blue

wide deployment (>~30)

feedback on critical events, retainment

unobtrusiveness

data sparsity

scale

success?

minimal neglect due to burden of study

useful feedback on critical events

wizards able to manage momento queries and complete other work

rapid prototypingiteration time shorter than similar social mobile apps

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contributions

a tool (momento) that improves evaluations by simplifying prototyping, making them easier to scale, reducing obtrusiveness, and facilitating data collection

a diary study technique that is easier to scale, less obtrusive, and yields better quality data than previous diary study techniques

identification of major challenges in ubicompevaluation: rapid prototyping, scale, unobtrusiveness, and data sparsity

validation of momento and diary study technique

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timeline

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timeline

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timeline: diary study

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timeline: momento

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summary

Leverage pre-existing infrastructures and the increasing power of mobile devices to address the challenges of field evaluations of Ubicomp applications

Wizard-of-Oz and report methods for early stage, field experiments

Ubicomp evaluation challenges: rapid prototyping, scale, unobtrusiveness, data sparsity

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acknowledgements

Scott Lederer, Christopher Beckmann, Jonathan Hey, Jeffrey Heer, Tara Matthews, Scott Klemmer, James Lin, Tye Rattenbury, Alan Newberger, David Nguyen, Judd Antin, Joe Hall, Nathan Good, Siobhan Hadley

Prof. Robert Kraut, Prof. Susan Fussell, Prof. Jason Hong, Laura Dabbish, Joe Tullio, Jeffrey Nichols, James Fogarty

Techniques and tools for field-based early-stage studyand iteration of Ubicomp applications

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Scott CarterEECS DepartmentUniversity of California, [email protected]

CommitteeJennifer Mankoff (chair), Anind Dey, John Canny, Peter Lyman

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other slides

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worldwide txt stats

number of computer users: about 1 bil by 2006 (IDC)email users: 700 mil by 2006 (Radicati Group)

about 2 bil mobile subscribers by 2006 (In-Stat/MDR)70% use sms (text.it) (number much lower in us)

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scope

formative and early stages

field methods

distributed, asynchronous activities

>> easier to iterate, could answer some research questions

>> realism especially important in ubicomp >> Sharp & Rehman ‘05: “Real-world deployment is the only way…”>> Kjeldskov: might be able to recreate field in a lab

>> distributed harder, concentrate on asynchronous

broad user base>> minimize programming

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rapid prototyping

scale

unobtrusiveness

data sparsity

ambiguity

>> simplify interface iteration, reduce coding, reduce burden of reinstallations

>> minimizing infrastructure deployment and time, encourage uptake

>> use familiar devices and lower distraction costs

>> capture and encourage feedback from critical events

>> allow people to control error handling

thesis: addressing challenges

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approach: wizard of oz

Kelley (1984)

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Kelley (1984)

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diary

experience sampling

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diary

experience sampling>> researchers send questions

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diary

experience sampling

>> participants report on events

>> researchers send questions

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diary studies

feedback or elicitation

participants report on events

form completion (feedback)participant controlled capture (elicitation)


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advances in recording devices

smaller

part of everyday infrastructure

easier to transfer files

annotation

new, more frequent uses for media


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findings

+ photos best for who and where recognition

audio elicitation

tangible objects

raw location information

- poor recognition

+/- clandestine captures

- poor recall

+ attitudes and beliefs

- poor sequencing and recall

+ peripheral information often crucial


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findings

tangible objects and attitudes and beliefs


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findingsperipheral information often crucial


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momento

desktop


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applications


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applications

interruptibility updates (social, activity)

Momento: How busy are you right now? 1 not busy at all, 5 is really busy.

…

P: Who is most available?

Momento: Joe


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applications

txt adventure (social, gaming)

Momento: You're in a forest. There is a trail to the N and a cabin to the W. Which way would you like to go? N/W

P: N

Momento: You open the cabin but there was a troll and he squashed you with his fist. Game over. Bad luck


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applications

near by (location, resource)


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applications

idea tagger (capture and access)


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diary study or esm?


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diary study or esm?


>> auto-driven: participant-sensed events>> creativity, attitudes, beliefs, breadth

diary study

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diary study or esm?


>> auto-driven: participant-sensed events>> creativity, attitudes, beliefs, breadth

diary study

>> researcher-driven: more control>> time use, depth

esm

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validation: interface support

augment current application

add functional feature

success?

demonstrate it works

no deployment

novel approach

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other slides

When Participants Do the Capturing

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Carter & Mankoff

diary studies and related methods

beeper studies

When Participants Do the Capturing

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Carter & Mankoff

diary studies and related methods

cultural probes

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rapid prototyping

scale

unobtrusiveness

data sparsity

ambiguity

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case study: dr. blue: ambiguity

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ubicomp evaluation challenges

rapid prototyping

scale

unobtrusiveness

data sparsity

ambiguity

Metrics: adoption, awareness, distraction, invisibility, (Scholtz and Consolvo)

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ubicomp challenges case study: hebb

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case study: hebb: rapid prototyping

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case study: hebb: rapid prototyping>> simplify interface iteration, reduce coding, reduce burden of reinstallations

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case study: hebb: scale

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case study: hebb: scale>> minimize infrastructure deployment and time, encourage uptake

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case study: hebb: unobtrusiveness

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case study: hebb: unobtrusiveness>> use familiar devices and lower distraction costs

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case study: hebb: sparsity

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case study: hebb: sparsity

>> capture and encourage feedback from critical events

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case study: hebb: ambiguity

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case study: hebb: ambiguity

>> allow people to control error handling

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ubiquitous computing (weiser)

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ubiquitous computing

off-the-desktop interfaces

techniques and tools for field-based early-stage study and iteration of ubicomp applications

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