A Comparison of Map vs . Text Directions for a Handheld Device in a Campus Setting: A Pilot Study
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A Comparison of Map vs. Text Directions for a Handheld
Device in a Campus Setting: A Pilot Study
Liz AtwaterDepartment of Psychology
George Mason University
Jason BurkeInstitute for Systems
Research
University of Maryland
Andrea KirkDepartment of Computer
Science
University of Maryland
December 2001
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Which one is more effective? – Less time, less errors
Does route complexity have an effect? Use by pedestrians instead of drivers
– Lack of landmarks– No street names, etc.
Map vs. Text Directions
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Context-aware, location-aware– Location awareness via GPS, RF, IR, etc.
Provides information depending on:– User profile– Device profile– Location– Context
Useful in many domains– Tourism– Commerce
Rover
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Williams studies (1999) – pilots finding nearest airport using maps or text– Maps are faster and more accurate– ERF tasks had better results with track-up– WRF tasks had better results with north-up
Aretz,1991 – ERF vs. WRF– Ego-centered frame track-up– World-centered frame north-up
Butz, 2001 – landmarks at key decision points
Background Research
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Experiment – Hypotheses
Hypotheses:– Null: There is no statistical difference between
completion time, consultation time and number of errors between text and map directions, regardless of route complexity.
– H1: Users will complete the tasks faster using map
directions.
– H2:Users will make fewer errors using map
directions.
– H3: Users will need less consultation time using text
directions.
– H4: Completion time will rise with increasing route
complexity.
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IVs & Treatments– Direction type: map vs. text– Route complexity: low, medium, high
• Low: 3 decision points, 893 ft • Medium: 5 decision points, 897 ft• High: 7 decision points, 883 ft
DVs– Completion time– Consulting time– Errors
Experiment – Variables
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Subjects– 7 male, 5 female– Undergrad & grad UMCP students
Other materials– Pre & post-task questionnaires– VZ-2
Experiment – Materials
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Navigate 3 routes using directions Within-subjects for routes Between-subjects for direction type 2 stopwatches Route permutations:
123 132
213 231
312 321
Experiment – Tasks
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Screen Shots
Text Implementation Map Implementation
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Main effect for route: significant Main effect for direction: ns Interaction effect: ns
Completion Time
0
50
100
150
200
250
300
350
400
High Medium Low
Route complexity
Tim
e (
s)
Map
Text
Results – Completion Time
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Main effect for route: significant Main effect for direction: ns Interaction effect: ns
Device Consulting Time
-20
0
20
40
60
80
100
120
High Medium Low
Route Complexity
Tim
e (
s)
Map
Text
Results – Consultation Time
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Main effect for route: significant Main effect for direction: ns Interaction effect: ns
Number of Errors
-0.5
0
0.5
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1.5
2
2.5
3
3.5
High Medium Low
Route Complexity
Nu
mb
er
of
err
ors
Map
Text
Results – Errors
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Observations
Learning seemed to have a significant effect on the results
Most errors occurred at non-dead ends
People are different– Huge variance in user performance in
both map and text implementations– Difficulty judging distances in text version– Rotate map for track-up bearings– Looking ahead caused problems
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Conclusions
Need many more subjects
Text directions are difficult to describe in college campus environment
Feedback from “real” context-aware equipment could improve performance
Track-up display for map could decrease orientation time
Hybrid to accommodate variations in user cognitive strengths