Footprints: History-Rich Tools for
Information Foraging
Wexelblat & Maes, 1999
Presented to EECE 519
March 1, 2007
Desy Wa...
Goal
To evaluate both subjective and objective
usefulness of Footprints (interaction
history tools)
To validate the auth...
Context
1999
Human-Computer Interaction (HCI)
MIT Media Lab
Context
1988 - Don Norman:
 history rich objects acquires new affordances
 we can use these affordances to interact wit...
Interaction History
Interaction history:
traces/records of interaction between humans
and an object that affect subsequen...
Interaction History Framework
Goals:
 boundary of possible interaction history
systems
 system design guidance for buil...
Interaction History Framework
1. Proxemic vs Distemic
2. Active vs Passive
3. Rate/Form of Change
4. Degree of Permeation
...
The Footprints Tools
Tools applying interaction history to the
problem of navigation in a complex
information space
Usin...
Map
 Social
 Passive
 Distemic
 Unpermeated
Path
 Social
 Passive
 Unpermeated
 Distemic/proxemic
Annotation
 Social
 Passive
 Proxemic
 Permeated
Signposts (Comments)
 Social
 Active
 Proxemic
 Permeated
Experiment
Timed (20 mins) browsing task (buy a car
with a $20,000 budget)
Group 1: unaided
Group 2: with Footprints
Hypotheses
Objective measures:
Footprints tools would increase the number of
alternatives generated and reduce the number...
Results
Objective measures:
 The number of alternatives generated by the
two subject groups was not significantly
differ...
Results
Subjective measures:
 No significant differences were observed
 One exception: interaction history models
helpe...
Conclusion
Footprints tools are successful in two
respects:
 They enable users to get the same work done
with significan...
Analysis
For casual tasks such as the one used in
the experiment, I think this tool is too
much for the users. Users are ...
Analysis
Social tagging systems are:
 Distemic
 Active
 Rapidly changed
 Unpermeated
 Social
 Supportive for variou...
Question
Is there any relation between interaction
history and recommendation system?
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Footprints: History-Rich Tools for Information Foraging (Wexelblat & Maes, 1999)

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  • Pirolli & Card @ Palo Alto Research Center (formerly Xerox PARC)
    Pirolli presented a talk in 1993 titled "Information foraging: A new view on problems in human-computer interaction," for Human-Computer Interaction Consortium Winter Workshop in Atlanta, Georgia. His first published article on information foraging was in 1995 titled "Information foraging in information access environments" (Pirolli & Card 1995).
    Information foraging uses the analogy of wild animals gathering food to analyze how humans collect information online: the web as a patchy information evironment, humans satisfice & follow information scent
    Edit Wear & Read Wear – for software development projects, keeping track of which portions of the code & documentation were being the most heavily modified, read, etc
  • Example (physical world): driving your car down an unfamiliar highway and approach a curve, borrowing a book
    Example (digital world): X shopping for a new car on the web, Y knows X and asks X vs Z doesn’t know X and doesn’t ask X
  • Footprints: History-Rich Tools for Information Foraging (Wexelblat & Maes, 1999)

    1. 1. Footprints: History-Rich Tools for Information Foraging Wexelblat & Maes, 1999 Presented to EECE 519 March 1, 2007 Desy Wahyuni
    2. 2. Goal To evaluate both subjective and objective usefulness of Footprints (interaction history tools) To validate the authors' theory/framework of interaction history using the tools
    3. 3. Context 1999 Human-Computer Interaction (HCI) MIT Media Lab
    4. 4. Context 1988 - Don Norman:  history rich objects acquires new affordances  we can use these affordances to interact with the object in new ways 1990s - Peter Pirolli & Stuart Card:  the concept of information foraging 1992/93: Will Hill and Jim Hollan:  Edit Wear & Read Wear
    5. 5. Interaction History Interaction history: traces/records of interaction between humans and an object that affect subsequent interaction (of other humans) with the same object Digital object vs physical object Footprints project
    6. 6. Interaction History Framework Goals:  boundary of possible interaction history systems  system design guidance for building history-rich interfaces
    7. 7. Interaction History Framework 1. Proxemic vs Distemic 2. Active vs Passive 3. Rate/Form of Change 4. Degree of Permeation 5. Personal vs Social 6. Kind of Information (what, who, why, and how)
    8. 8. The Footprints Tools Tools applying interaction history to the problem of navigation in a complex information space Using metaphor of physical world navigation: maps, paths, and signposts
    9. 9. Map  Social  Passive  Distemic  Unpermeated
    10. 10. Path  Social  Passive  Unpermeated  Distemic/proxemic
    11. 11. Annotation  Social  Passive  Proxemic  Permeated
    12. 12. Signposts (Comments)  Social  Active  Proxemic  Permeated
    13. 13. Experiment Timed (20 mins) browsing task (buy a car with a $20,000 budget) Group 1: unaided Group 2: with Footprints
    14. 14. Hypotheses Objective measures: Footprints tools would increase the number of alternatives generated and reduce the number of pages visited Subjective measures: Users would find it easier to find and understand relevant information, and would have a greater sense of satisfaction
    15. 15. Results Objective measures:  The number of alternatives generated by the two subject groups was not significantly different  The mean number of pages required to reach the same alternative level was significantly less for the Footprints group
    16. 16. Results Subjective measures:  No significant differences were observed  One exception: interaction history models helped (or increased satisfaction level of) experienced users but not naïve users
    17. 17. Conclusion Footprints tools are successful in two respects:  They enable users to get the same work done with significantly less effort  Experienced users were able to recognize the information models left behind by other users and reported a significantly higher sense of satisfaction when working with these models
    18. 18. Analysis For casual tasks such as the one used in the experiment, I think this tool is too much for the users. Users are lazy. Footprints vs del.icio.us?  The way people tag online resources can be seen as the interaction between human and digital object.  Tags are the traces.
    19. 19. Analysis Social tagging systems are:  Distemic  Active  Rapidly changed  Unpermeated  Social  Supportive for various kinds of information
    20. 20. Question Is there any relation between interaction history and recommendation system?

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