From Exploration to Construction
How to Support the Complex Dynamics of Information Seeking
Hugo C. Huurdeman PhD candidate University of Amsterdam
webarchiving.nl

Introduction: a paradox
• Models of
information
seeking describe
fundamentally
different macro-
level stages in
complex tasks
+uncertainty-uncertainty optimism confusion clarity confidence (dis)satisfaction
doubt direction
FormulationInitiation Selection Exploration Collection Presentation

Introduction: a paradox
• Models of
information
seeking describe
fundamentally
different macro-
level stages in
complex tasks
+uncertainty-uncertainty optimism confusion clarity confidence (dis)satisfaction
doubt direction
FormulationInitiation Selection Exploration Collection Presentation
Search
• However, current search systems usually
provide a streamlined and static feature set
• To what extent do current search
approaches support complex tasks?

Multistage Information Seeking Models
macro perspective1
Read more:
Huurdeman & Kamps (2014), From Multistage Information Seeking Models
to Multistage Search Systems. Proc. IIiX 2014. http://dx.doi.org/10.1145/2637002.2637020
Huurdeman & Kamps (2015). Supporting the Process - Adapting Search Systems
to Search Stages. Proc. ECIL 2015. http://dx.doi.org/10.1007/978-3-319-28197-1_40

1.1 Information Seeking Models
• Information seeking modeled in
a multitude of ways:
• as behavioral patterns (Ellis)
• as nonlinear activities (Foster)
• as problem-solving (Wilson)
• as temporal stages (Kuhlthau), ..
• Our main focus:
• temporally based IS models
• Kuhlthau [1991] (Vakkari [2001])
• cognitively complex (work) tasks
• involving learning & construction
information
search
information
seeking
information
behavior
[Wilson99]

1.2 Kuhlthau: Information Search Process [1991]+uncertainty-
feelings
thoughts
actions
vague focused
seeking relevant information
(exploring)
seeking pertinent information
(documenting)
uncertainty optimism confusion clarity confidence (dis)satisfaction
doubt direction
FormulationInitiation Selection Exploration Collection Presentation

1.2 Vakkari’s adaptation (in [Vakkari01])
Prefocus Focus formulation Postfocus
seeking general background
information
seeking specific
information
faceted backgr.
information
relevance hard to judge relevance easier to judge
decrease of number of broader terms
information
sought
relevance
search terms increase of number of search terms, synonyms, narrower terms

1.3 Implications for design of search systems
• Observation: good general
understanding of macro level
inf. seeking stages, but hard to
translate to concrete micro level
system design choices.
macro
micro
system
design
inf.
seeking
stages

Search user interfaces supporting seeking
micro perspective2

3.1 Search user interfaces supporting seeking
• Search User Interface (SUI) design:
• no straightforward task to design a UI with a high usability [Shneiderm05]
• A (limited) number of available frameworks, guidelines and design
pattern libraries for SUIs
• e.g. M. Wilson’s framework of SUI features [Wilson11]
Search
Input
Control
Informational
Personalizable

2.1 SUI approaches: traditional search
• Streamlined interfaces
• Focus on
• query formulation
• result list inspection
• Advantages: [Hearst09]
• lower cognitive load
• more accessible
• more understandable
Highly optimized for lookup tasks, less for open-ended queries

• Research-based tasks
• WebART project
• new media researchers
• action research setting, structured literature review
• Search systems allow for answering new research
questions, but also have limitations
• lack of transparency
• lack of process support
2.2 When traditional search does not work well
researcher
research
activities
corpus
creation
analysis
dissemination
webarchiving.nl

2.3 SUI approaches: exploratory search
• Supporting open-
ended inf. seeking
• Support learning and
investigation activities
for complex
information problems
[Marchionini06]
• Many potential exploratory SUI features [White09], e.g.
• rapid query refinement, facets (input, control)
• leveraging context, visualizations (informational)
• histories/workspaces/task management (personalizable)

2.4 SUI approaches: sensemaking & analytics
• Support analysis & synthesis in interface
• Potential functions facilitating notetaking, hypothesis formulation
& collaborative search [Hearst09]
• some overlap with exploratory search

2.5 Implications for search stage support (2/2)
• Observation: good
understanding of search system
features at the micro level, but
fragmented understanding of
how they can support
information seeking stages at
the macro level
macro
micro
search
system
features
inf.
seeking
stages

Reconciling macro and micro views
• Would it be possible
to reconcile the
macro level and
micro level views?
macro
micro
search
system
features
inf.
seeking
stages

“The Utility of SUI Features”
Our study: investigating the utility of various
SUI features at different macro-level stages
From: Huurdeman, Wilson & Kamps (2016), Active and Passive Utility of
Search Interface Features in Different Information Seeking Task Stages.
Proc. ACM CHIIR 2016. http://dx.doi.org/10.1145/2854946.2854957

3. Setup
• User study (26 participants; 24 analyzed)
• Undergrads Univ. of Nottingham (6 F, 12 M, 18-25y)
• Experimental SUI resembling common Search Engine
• Within-participants
• Task stage independent variable
• Task design: explicit multistage approach

3. Setup: Multistage Task Design
sim. work task: writing essay
subtask subtask subtask
prepare list of
3 topics
choose topic;
formulate specific
question
find and select
additional
pages to cite
15 minutes 15 minutes 15 minutes
initiation
topic selection
exploration
focus formulation
collecting
presenting

3. Setup: Multistage Task Design
sim. work task: writing essay
subtask subtask subtask
prepare list of
3 topics
choose topic;
formulate specific
question
find and select
additional
pages to cite
15 minutes 15 minutes 15 minutes
initiation
topic selection
exploration
focus formulation
collecting
presenting
General Assigned Topics (b/o discussions teaching staff)
• Autonomous Vehicles (AV)
• Virtual Reality (VR)

3. Setup: Protocol
Training task
Pre-
Questionnaire
Topic
Assignment
Introduction
system
Task
Post-task
Questionnaire
3x
Post-experiment
questionnaire
Debriefing
interview

• Experimental system: SearchAssist
• Results, Query Corrections, Query
Suggestions: Bing Web API
• Category Filters: DMOZ
• Categorization and analysis:
• Max Wilson’s framework of SUI features
[Wilson11]

Control

Control
Input

Control
Input
Informational

Control
Input
PersonalizableInformational

3. Setup: Logging
eyetribe.com

3. Setup: Data / Task details
• AV & VR topics invoked comparable behaviours:
• analysed as one topic set
• Total duration main tasks
• Total task time: 32:56
• 36.8% SUI, 33% Task screen, 30.2% Webpages
Stage 1: 11:32 Stage 2: 8:24 Stage 3: 12:59

Findings: Active Behaviour
behaviour directly and indirectly derivable from logs4

4.1 Active Behaviour: Clicks
0
4
8
Sig. clicks on interface
features over time
Stage 1 Stage 2 Stage 3

4.1 Active Behaviour: Clicks
0
4
8
Sig. clicks on interface
features over time
Category filters ➡
Stage 1 Stage 2 Stage 3

4.1 Active Behaviour: Clicks
0
4
8
Sig. clicks on interface
features over time
Category filters ➡
Tag Cloud ➡
Stage 1 Stage 2 Stage 3

4.1 Active Behaviour: Clicks
0
4
8
Sig. clicks on interface
features over time
Category filters ➡
Tag Cloud ➡
Search button ➡
Stage 1 Stage 2 Stage 3

4.1 Active Behaviour: Clicks
0
4
8
Sig. clicks on interface
features over time
Category filters ➡
Tag Cloud ➡
Search button ➡
Saved Results
Stage 1 Stage 2 Stage 3

4.2 Active Behaviour: Queries
•Mean number of queries** (unique):
• Stage 1: 9.5 (8.1) ➡ Stage 2: 5.5 (5.1) ➡ Stage 3: 5.9 (5.3)
0
2,5
5
7,5
10
Stage 1 Stage 2 Stage 3
Search Box
Query Suggestions
Recent Queries

4.3 Active Behaviour: Query words
•Mean number of query words**:
“virtual reality” (P.02)
“impact of virtual reality on
society art and culture“
“autonomous vehicles” (P.06)
“autonomous vehicles costs
insurance industry”
0
1,25
2,5
3,75
5
Stage 1 Stage 2 Stage 3
Mean Number of Query words

4.4 Active Behaviour: Visited pages
• Visited pages (unique)**:
• Stage 1: 8.0 (7.3)
• Stage 2: 6.4 (5.9)
• dwell time highest
• Stage 3: 14.2 (10.8)
• Mean rank visited pages
• from 3.1 to 6.4
0
4
8
12
16
Stage 1 Stage 2 Stage 3
Results List
Saved Results

4.5 Active Behaviour: Wrapup
• Clicks:
• decreasing for Query Box (input), Category Filters & Tag
Cloud (control)
• increasing for Saved Results (personalizable)
• Queries:
• decreasing over time, but more complex
• Popularity of certain features and impopularity of others:
•Some features used in passive instead of active ways?

Findings: Passive Behaviour
behaviour not typically caught in interaction logs5
eyetribe.com

Passive behaviour: mouse hovers
• Mouse movements:
• movements to reach a feature, also to aid processing contents [Rodden08]
•Focus here on mouse movements not leading to click
• Tendencies mostly overlap with active interaction measure
0%
25%
50%
75%
100%
1 2 3

Passive behaviour: mouse hovers
Category filters** ➡
• Mouse movements:
• movements to reach a feature, also to aid processing contents [Rodden08]
•Focus here on mouse movements not leading to click
• Tendencies mostly overlap with active interaction measure
0%
25%
50%
75%
100%
1 2 3

Passive behaviour: mouse hovers
Category filters** ➡
Tag Cloud* ➡
• Mouse movements:
• movements to reach a feature, also to aid processing contents [Rodden08]
•Focus here on mouse movements not leading to click
• Tendencies mostly overlap with active interaction measure
0%
25%
50%
75%
100%
1 2 3

Passive behaviour: mouse hovers
Category filters** ➡
Tag Cloud* ➡
Query Box** ➡
• Mouse movements:
• movements to reach a feature, also to aid processing contents [Rodden08]
•Focus here on mouse movements not leading to click
• Tendencies mostly overlap with active interaction measure
0%
25%
50%
75%
100%
1 2 3

Passive behaviour: mouse hovers
Category filters** ➡
Tag Cloud* ➡
Query Box** ➡
Results List* ⤻
• Mouse movements:
• movements to reach a feature, also to aid processing contents [Rodden08]
•Focus here on mouse movements not leading to click
• Tendencies mostly overlap with active interaction measure
0%
25%
50%
75%
100%
1 2 3

5.2 Passive Behaviour: eye fixations
Stage 1 (exploration) Stage 2 (focus formulation)
Stage 3 (postfocus, collection)
• Overview of eye movement via heatmaps:

Passive behaviour: eye tracking
eye tracking fixations
0
25
50
75
100
1 2 3
• Further insights via eye tracking fixation counts
• fixations > 80 ms, similar to e.g. [Buscher08]

Passive behaviour: eye tracking
eye tracking fixations
0
25
50
75
100
1 2 3
• Further insights via eye tracking fixation counts
• fixations > 80 ms, similar to e.g. [Buscher08]
Query Suggestions* ➡

Passive behaviour: eye tracking
eye tracking fixations
0
25
50
75
100
1 2 3
Tag Cloud* ➡
• Further insights via eye tracking fixation counts
• fixations > 80 ms, similar to e.g. [Buscher08]
Query Suggestions* ➡

Passive behaviour: eye tracking
eye tracking fixations
0
25
50
75
100
1 2 3
Category filters** ➡
Tag Cloud* ➡
• Further insights via eye tracking fixation counts
• fixations > 80 ms, similar to e.g. [Buscher08]
Query Suggestions* ➡

Passive behaviour: eye tracking
eye tracking fixations
0
25
50
75
100
1 2 3
Category filters** ➡
Tag Cloud* ➡
Query Box** ➡
• Further insights via eye tracking fixation counts
• fixations > 80 ms, similar to e.g. [Buscher08]
Query Suggestions* ➡

Passive behaviour: eye tracking
eye tracking fixations
0
25
50
75
100
1 2 3
Category filters** ➡
Tag Cloud* ➡
Query Box** ➡
Results List* ⤻
• Further insights via eye tracking fixation counts
• fixations > 80 ms, similar to e.g. [Buscher08]
Query Suggestions* ➡

3.4 Passive Behaviour: Active vs. Passive
0%
2%
4%
6%
8%
Stage 1 Stage 2 Stage 3
Subtle differences between passive and active use:

3.4 Passive Behaviour: Active vs. Passive
0%
2%
4%
6%
8%
Stage 1 Stage 2 Stage 3
Tag Cloud [5.8% fixations ⬌ 3.1% clicks]
Subtle differences between passive and active use:

3.4 Passive Behaviour: Active vs. Passive
0%
2%
4%
6%
8%
Stage 1 Stage 2 Stage 3
Query Suggestions [3.6% fix. ⬌ 1.9% clicks]
Tag Cloud [5.8% fixations ⬌ 3.1% clicks]
Subtle differences between passive and active use:

3.4 Passive Behaviour: Active vs. Passive
0%
2%
4%
6%
8%
Stage 1 Stage 2 Stage 3
Query Suggestions [3.6% fix. ⬌ 1.9% clicks]
Tag Cloud [5.8% fixations ⬌ 3.1% clicks]
Recent Queries [3% fix. ⬌ 2% clicks]
Subtle differences between passive and active use:

3.4 Passive Behaviour: Active vs. Passive
0%
2%
4%
6%
8%
Stage 1 Stage 2 Stage 3
Query Suggestions [3.6% fix. ⬌ 1.9% clicks]
Tag Cloud [5.8% fixations ⬌ 3.1% clicks]
Recent Queries [3% fix. ⬌ 2% clicks]
Subtle differences between passive and active use:
Opposite for Category Filters [5% ⬌ 3.8%]

5.4 Passive Behaviour: Wrapup
•Fixations & mouse moves
• validating active behaviour
• subtle differences active and passive use
• Could subjective ratings and qualitative feedback
provide more insights?

Findings: Perceived Feature Utility
perceived usefulness (post-stage & experiment)6

6.2 Perceived Usefulness: post-experiment
• Post-experiment questionnaire:
• In which stage or stages were SUI features most useful?
• Pronounced differences
• significant differences for all features
0%
25%
50%
75%
100%
Query Box /
Results List
Category
Filters
Tag
Cloud
Query
Suggestions
Recent
Queries
Saved
Results

6.2 Perceived Usefulness: post-experiment
• Post-experiment questionnaire:
• In which stage or stages were SUI features most useful?
• Pronounced differences
• significant differences for all features
0%
25%
50%
75%
100%
Query Box /
Results List
Category
Filters
Tag
Cloud
Query
Suggestions
Recent
Queries
Saved
Results

6.2 Perceived Usefulness: post-experiment
• Post-experiment questionnaire:
• In which stage or stages were SUI features most useful?
• Pronounced differences
• significant differences for all features
0%
25%
50%
75%
100%
Query Box /
Results List
Category
Filters
Tag
Cloud
Query
Suggestions
Recent
Queries
Saved
Results

6.3 Perceived Usefulness: Category Filters
• “good at the start (…) but later I
wanted something more specific” (P.11)
• common remarks in 2nd and 3rd stage:
• “… could be more specific in its
categories”
• “…hard to find the category I want” (P.
27)

6.3 Perceived Usefulness: Tag Cloud
• at the start:
• “…aids exploring the topic” (P.06);
• “came up with words that I hadn’t thought of”
• later stages:
• “doesn’t help to narrow the search much” (P.18)
• “in the end seemed to be too general” (P.07)

6.3 Perceived Usefulness: Tag Cloud
• at the start:
• “…aids exploring the topic” (P.06);
• “came up with words that I hadn’t thought of”
• later stages:
• “doesn’t help to narrow the search much” (P.18)
• “in the end seemed to be too general” (P.07)
• Post-experiment comments:
• “…was good at the beginning, because when you
are not exactly sure what you are looking for, it can
give inspiration” (P.12)
• “… nice to look at what other kinds of ideas [exist]
that maybe you didn’t think of. Then one word may
spark your interest” (P.15)

6.3 Perceived Utility: Query Suggestions
• “…was good at the start but as soon
as I got more specific into my topic,
that went down” (P.11)
• “clicked [it] .. a couple of times .. it
gave me sort of serendipitous
results, which are useful” (P.24)

6.3 Perceived Utility: Recent Queries
• Naturally: “…most useful in the end
because I had more searches from
before” (P.26)
• “The previous searches became more
useful ‘as I made them’ because they
were there and I could see what I
searched before. I was sucking myself
in and could work by looking at
those.” (P.23)
• May aid searchers in
their information journey..

6.3 Perceived Utility: Saved Results
• “most useful in the end” (P.12)
• “At the start [I was] saving a lot of
general things about different topics.
Later on I went back to the saved
ones for the topic I chose and then
sort of went on from that and see what
else I should search” (P.26)
• “I just felt I was organizing my
research a little bit” (P.18)
• It “helps me to lay out the plans of my
research”.

Conclusion
towards more dynamic support7

0%!
20%!
40%!
60%!
80%!
100%!
Stage 1! Stage 2! Stage 3!
Percentageofparticipants!
input / informational!
control!
personalisable!
Stage 2! Stage 3!
input / informational!
control!
personalisable!
Conclusion: Findings Summary
• Informational features highly
useful in most stages
• Decreasing use of input features
• Control features decreasingly
useful
• likely caused by a user’s evolving
domain knowledge
• Personalizable features
increasingly useful
• ‘growing’ with a user’s
understanding, task
management support
SUI features perceived as most
useful, per stage

7. Conclusion: theoretical roundup
complex information seeking task
pre-focus stage:
• vague understanding
• limited domain knowledge
• trouble expressing
information need
• large amount of new
information
• explaining prominent role of
control features
• explore information
• filter result set
using [Kuhlthau04,Vakkari&Hakkala00,Vakkari01]

7. Conclusion: theoretical roundup
complex information seeking task
pre-focus stage:
• vague understanding
• limited domain knowledge
• trouble expressing
information need
• large amount of new
information
• explaining prominent role of
control features
• explore information
• filter result set
focus formulation stage:
• more directed search
• better understanding
• seeking more relevant
information, using
differentiated criteria
• control features become less
essential
• “not specific enough”
• personalizable feat’s more
important: may “grow” with
emerging understanding
using [Kuhlthau04,Vakkari&Hakkala00,Vakkari01]

7. Conclusion: theoretical roundup
complex information seeking task
pre-focus stage:
• vague understanding
• limited domain knowledge
• trouble expressing
information need
• large amount of new
information
• explaining prominent role of
control features
• explore information
• filter result set
focus formulation stage:
• more directed search
• better understanding
• seeking more relevant
information, using
differentiated criteria
• control features become less
essential
• “not specific enough”
• personalizable feat’s more
important: may “grow” with
emerging understanding
postfocus stage
• specific searches
• re-checks additional
information
• precise expression
• low uniqueness, high
redundancy of info
• long, precise, queries
• further decline of control
features
• frequent use of
personalizable features
• “see what else to search”
using [Kuhlthau04,Vakkari&Hakkala00,Vakkari01]

7. Conclusion: Future Work
•Our study: essay writing simulated work task
• Extension to other types of complex tasks, user
populations
•Further research into task-aware search systems
• additional features may be useful at different stages
• e.g. user hints, assistance
• improvement of current features

Towards “stage-aware” Systems
prefocus
focus
formulation
postfocus
searchersearch
system
search
interface
search
stage
stage-independent
functionalityranking stage-dependent
functionality
manual or automatic
detection

• INEX/CLEF Interactive Social
Book Search Lab
• http://social-book-search.humanities.uva.nl/#/interactive
• Gaede, Hall, Huurdeman, Kamps,
Koolen, Skov, Toms, Walsh (2015)
• Aim: support different stages
in the search process:
browse, search & review
• Joint study across
universities, 192 participants
Multistage interface: search
Multistage interface: browse
Multistage interface: review
Example: multistage interface

7. Conclusion: towards dynamic SUIs
•Most Web search systems converged over static
and familiar designs
• trialled features often struggled to provide value for
searchers
• perhaps impeding search [Diriye10] if introduced in simple
tasks, or at the wrong moment
•Our work provides insights into when SUI
features are useful during search episodes
• potential responsive and adaptive SUIs for complex tasks

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From Exploration to Construction
How to Support the Complex Dynamics of Information Seeking
Hugo C. Huurdeman University of Amsterdam @TimelessFuture
webarchiving.nl