CHIIR2017 - Tetris Model of Resolving Information Needs
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Transcript of CHIIR2017 - Tetris Model of Resolving Information Needs
Dr Max L. Wilson http://cs.nott.ac.uk/~pszmw
The Tetris Model of Resolving Information Needs
(within the Information Seeking Process)
Max L. Wilson
University of Nottingham, UK@gingdottwit
CHIIR2017 Perspectives Paper
Dr Max L. Wilson http://cs.nott.ac.uk/~pszmw
Fair Warning
R1: “This is one of the most interesting papers I have read in some time.
[…]I haven't felt that way about anything I have read in a long time.”
R2: “This paper reads more like a student's narrative”
but
Dr Max L. Wilson http://cs.nott.ac.uk/~pszmw
Main Critique
R3: “it tries very hard to relate the goal of a very specific time-based problem-solving game with the concept of
information search.”
Which is absolutely fair, and worth considering
Dr Max L. Wilson http://cs.nott.ac.uk/~pszmw
Saracevic - Stratified Model
Dr Max L. Wilson http://cs.nott.ac.uk/~pszmw
Ingersen & Jarvelin - Cognitive Model
Dr Max L. Wilson http://cs.nott.ac.uk/~pszmw
Pirolli & Card - Sensemaking Model
Dr Max L. Wilson http://cs.nott.ac.uk/~pszmw
Kuhlthau - IS Process Model
Dr Max L. Wilson http://cs.nott.ac.uk/~pszmw
Ellis - IS Process Model (mapped against Kuhlthau’s Model)
Dr Max L. Wilson http://cs.nott.ac.uk/~pszmw
Marchionini - IS Process Model
Dr Max L. Wilson http://cs.nott.ac.uk/~pszmw
Bates - Non-Linear ISP Model
Dr Max L. Wilson http://cs.nott.ac.uk/~pszmw
Spink’s - Non-Linear ISP Model
Dr Max L. Wilson http://cs.nott.ac.uk/~pszmw
Jarvelin & Ingwersen - Levels Model
Dr Max L. Wilson http://cs.nott.ac.uk/~pszmw
Elsweiler, M.L. Wilson & Kirkegaard Lunn - Levels Model (Casual Leisure Version of Jarvelin & Ingwersen)
Dr Max L. Wilson http://cs.nott.ac.uk/~pszmw
White & Roth - Exploratory Search Model
SDescriptive & conceptual
models
Explanatory & predictive
models
Formal (mathematical)
models
• Describe How people interact with search process • The aim to gain a deep understanding of the users’ ISB and/
or develop theories of such behaviour. • E.g. Bates’ Berry Picking Model
• Ingwersen and Järvelin model of information seeking
• Provide insight into Why people behave in certain ways and
predict How people will behave under different circumstances. • The aim explain & predict search behavior e.g. querying,
selecting documents, stopping and marking documents • Such models and theories formalize the relationship between
the interactions of the users with the costs and gains of the IRS. • E.g. New Economic model of the Search Process
• The interactive Probability Ranking Principle (PRP) model
• Try to adapt the ISB models & combine them with the traditional evaluation (Cranfield-styled) measures.
• The aim is to translate user models into evaluation measures • E.g. Modeling the interaction of the users with the topic summaries and
predict the probability of clicking on a result
• Modeling user variance in time-biased gain
(Expertly presented by Maram Barifah at CHIIR2017 DC)
Dr Max L. Wilson http://cs.nott.ac.uk/~pszmw
Models help us to
• 1) Conceptually formalise and separate aspects of the model’s focus
• 2) Communicate more clearly about these aspects
• 3) Create hypotheses for future research and/or interpret research results
• 4) Produce implications for future systems
(my first assertion)
Dr Max L. Wilson http://cs.nott.ac.uk/~pszmw
Example Problem
• Marchionini’s model - good at conveying stages - but - not good at explaining exploratory behaviour
Dr Max L. Wilson http://cs.nott.ac.uk/~pszmw
Example Problem
Its because its showing a sequence of stages And progress is aligned to one dimension
Dr Max L. Wilson http://cs.nott.ac.uk/~pszmw
What I'm going to do now
• Not define model, theory, framework, etc
• Introduce the Tetris Model
• Abstraction - with a different focus
• Show that it helps us think about many search experiences
• And then acknowledge its limitations
Dr Max L. Wilson http://cs.nott.ac.uk/~pszmw
First - The Tetris Game
By Cezary Tomczak, Maxime Lorant - Own work, CC BY-SA 4.0, https://commons.wikimedia.org/w/index.php?curid=38787773
Dr Max L. Wilson http://cs.nott.ac.uk/~pszmw
My Main Position
• The Tetris Model captures: - the depth of complexity of an Information Need - the non-linear experience of searchers - IR, InfoSeeking, Exploratory Search in one model - non-searching Information Behaviours
• It can complement other e.g. stage based models
Dr Max L. Wilson http://cs.nott.ac.uk/~pszmw
Tetris Model of Resolving Info Needs
Complexity of
Info Need
Dr Max L. Wilson http://cs.nott.ac.uk/~pszmw
Tetris Model of Resolving Info Needs
Knowledgeincreases
Dr Max L. Wilson http://cs.nott.ac.uk/~pszmw
Lookup
• The info need is not complex
• Quick IR gets you the right piece
• Info Need resolved
• You have a clear board until you encounter a new Info Need
Dr Max L. Wilson http://cs.nott.ac.uk/~pszmw
Learn
• You thought the Info Need was simple
• But answer was more complicated than you expected
• You realise there is more to find out
• Then something helps you understand the deeper Info Need
Dr Max L. Wilson http://cs.nott.ac.uk/~pszmw
Learn
• You thought the Info Need was simple
• But answer was more complicated than you expected
• You realise there is more to find out
• Then something helps you understand the deeper Info Need
Dr Max L. Wilson http://cs.nott.ac.uk/~pszmw
Investigate
• Your initial Info Need is complex
• You need a few pieces to fix this
• And those pieces might make the Info Need more complex (!)
Dr Max L. Wilson http://cs.nott.ac.uk/~pszmw
Investigate
• Your initial Info Need is complex
• You need a few pieces to fix this
• And those pieces might make the Info Need more complex (!)
Dr Max L. Wilson http://cs.nott.ac.uk/~pszmw
Taking it Further
• Realistically - for any domain (the game space)
• We probably learned a few extra things along the way
• That we maybe leave for later
Dr Max L. Wilson http://cs.nott.ac.uk/~pszmw
Life-Long Learning
(In certain knowledge areas)
Dr Max L. Wilson http://cs.nott.ac.uk/~pszmw
Life-Long Learning
(My board on Foreign Languages, politics, and history)
Dr Max L. Wilson http://cs.nott.ac.uk/~pszmw
Critique: Time Pressure
• BUT - its interesting to think about it - we sometimes are working to a deadline - time limits in user studies DO affect behaviour - lots of research into the negative effect of time-delays etc
Image By Wyatt915 - Own work, Public Domain, https://commons.wikimedia.org/w/index.php?curid=4603015
Dr Max L. Wilson http://cs.nott.ac.uk/~pszmw
Critique: Random Pieces
• BUT - its interesting to think about it - Information Encountering - even to encounter lots of information in SERP
Image: http://ilikethesepixels.com/real-world-tetris-by-remi-gaillard/
Dr Max L. Wilson http://cs.nott.ac.uk/~pszmw
Critique: Resolving top down
• BUT - its interesting to think about - Sometimes we DO have to figure things out in an order
Image from: http://alyjuma.com/curiosity/
Dr Max L. Wilson http://cs.nott.ac.uk/~pszmw
Formalising & Communicating
• Because progress isn't tied to a dimension - We can conceptualise the complexity of the Info Need - And discuss the idea of progress - resolving
• We are all grappling with the Exploratory Search agenda - The Tetris Model helps us to communicate about it
• (but you cant, of course, e.g. communicate about stages)
Dr Max L. Wilson http://cs.nott.ac.uk/~pszmw
Create Hypotheses for Research& Implications for Systems
• We have already asked questions about time, encountering, etc
• What happens to people who’s Info Need keeps getting deeper?
• Can systems track Info Need depth, rather than searcher stage? - e.g. displaying results to resolve a predicted session - highlighting results that relate to previously seen info - can we highlight “the piece they need”?
• Is ‘encountering new info’ the reason that Query Suggestions can be disruptive to searchers?
Dr Max L. Wilson http://cs.nott.ac.uk/~pszmw
Conclusions
• Introduced the Tetris Model of Resolving Information Needs
• Captures: Info Need Complexity - ties this to Complexity of Search Experience
• Everything from Look Ups to Exploratory Search
• Has limitations (like all models) in what it captures - doesn’t capture stages, or user actions
Dr Max L. Wilson http://cs.nott.ac.uk/~pszmw
Que
stion
s?
https://xkcd.com/888/