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

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



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



We ! Models



Saracevic - Stratified Model



Bates - Stratified Model



Ingersen & Jarvelin - Cognitive Model





Dervin - Sensemaking Model



Pirolli & Card - Sensemaking Model



Kuhlthau - IS Process Model



Ellis - IS Process Model (mapped against Kuhlthau’s Model)



Marchionini - IS Process Model



Bates - Non-Linear ISP Model



Spink’s - Non-Linear ISP Model



T. Wilson - Levels Model



Jarvelin & Ingwersen - Levels Model



Elsweiler, M.L. Wilson & Kirkegaard Lunn - Levels Model (Casual Leisure Version of Jarvelin & Ingwersen)



White & Roth - Exploratory Search Model



We ! Models


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)


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)



Limitations of Models



Limitations of Models



Example Problem

• Marchionini’s model - good at conveying stages - but - not good at explaining exploratory behaviour



Example Problem

Its because its showing a sequence of stages And progress is aligned to one dimension



Motivation - Describing ES



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



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



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



Tetris Model of Resolving Info Needs

Complexity of

Info Need



Tetris Model of Resolving Info Needs

Knowledgeincreases



So what does that mean?



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



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



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



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 (!)



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 (!)



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



Life-Long Learning

(In certain knowledge areas)



Life-Long Learning

(My board on Foreign Languages, politics, and history)



Limitations



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


https://commons.wikimedia.org/w/index.php?curid=4603015




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/



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/



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)



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?



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



Que

stion

s?

https://xkcd.com/888/


Technology

CHIIR2017 - Tetris Model of Resolving Information Needs