Improving Experience Sampling Method for Better Data Collection in Mobile HCI
This study explores enhancements to the Experience Sampling Method (ESM) to address data loss issues, determined to reach at least 20%. Conducted by Vassilis Javed Khan and colleagues, the project focused on busy parents, examining their willingness to automatically share context-relevant information. The study involved a week-long ESM application, reviewing participant responses daily. Results show a mean recovery rate of 60.13% for previously unanswered activities, indicating a 29.35% improvement in data collection. The personalized answers allow for better engagement and accuracy in context capturing.
Improving Experience Sampling Method for Better Data Collection in Mobile HCI
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Presentation Transcript
Reconexp: Improving ESM Vassilis-Javed Khan, Panos Markopoulos, Berry Eggen, Boris de Ruyter, Wijnand IJsselsteijn Mobile HCI, Amsterdam, 04 september 2008
INDEX • What is Experience Sampling Method? • Our Application and Study • How does it improve ESM?
Experience Sampling Method • A quasi-naturalistic method that involves signaling questions at subjects at random times throughout the day.
ESM is gaining popularity in HCI • Hudson et al. (2002) have used the ESM to explore attitudes about availability of managers at IBM Research • Consolvo and Walker (2003) have used the ESM for evaluating an Intel Research system called Personal Server • Froehlich et al. (2007) used ESM to investigate the relationship between explicit place ratings and implicit aspects of travel such as visit frequency
HOWEVER… • Reported loss of data: at least 20%
HOWEVER… • Reported loss of data: at least 20% • How significant is the data lost?
GOAL OF OUR STUDY • Find out what information are “busy parents” willing to automatically share among themselves + under which context
1. Insert info to personalize the ESM OVERVIEW 2. Execute ESM for a week 3. Foreach day review answers & fill out the missing points
OVERVIEW OF THE STUDY • INITIAL TASK (WEBSITE): • Name Places (during a typical working day)
OVERVIEW OF THE STUDY • INITIAL TASK (WEBSITE): • Name Places & activities (during a typical working day)
OVERVIEW OF THE STUDY • INITIAL TASK (WEBSITE): • Name Places & activities (during a typical working day) • Link information to context (place & activity)
OVERVIEW OF THE STUDY • INITIAL TASK (WEBSITE): • Name Places & activities (during a typical working day) • Link information to context (place & activity) • A TYPICAL WORKING WEEK (WEBSITE & PDA): • Ask several times about context (at that time) & info willing to automatically share,
OVERVIEW OF THE STUDY • INITIAL TASK (WEBSITE): • Places and activities (during a typical working day) • Link information to context (place & activity) • A TYPICAL WORKING WEEK (WEBSITE & PDA): • Ask several times about context (at that time) & info willing to automatically share, • SEMI-STRUCUTRED INTERVIEW.
11 PARTICIPANTS • Mean Age: 38(max: 44, min: 28, σ = 5.72) • Mean Number of children: 1.91(max: 4, min: 1, σ = 0.79) • Mean Age of children: 5.47(max: 8.5, min: 0.7, σ = 2.57) • Mean Years of marriage: 10.86(max: 20, min: 2, σ = 5.22) • Mean Hours of work per week: 28.18(max: 40, min: 20, σ = 6.63)
LOG • Log into the system • Link information statements to previously not answered question • Name Activity which was not answered • Name Activity which was not answered using existing value • Name Location which was not answered • Name Location which was not answered using existing value • Name “Other” Activity • Name “Other” Activity using existing value • Name “Other” Information • Name “Other” Information using existing value • Name “Other” Location • Name “Other” Location using existing value
RESULTS • Mean number of actions performed (logins not counted in this number) 55.64 • Mean logins (in 5 days) 2.91 • Mean percentage of activities (2nd question) not answered: 48.81% • Mean percentage of activities recovered (with the website use): 60.13% • Overall improvement of the website to the method is 29.35%
CONCLUSIONS • Improves on the data loss of ESM • Participants & Researcher can have an overview of the data during the execution of the study • Answers are personalized • Answers can be annotated at a later point in time • V.j.khan@tue.nl • HTTP://WWW.AWARENESS.ID.TUE.NL