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Making Datasets Playable – 03/05/18 – Slide No. 1
‘Games for Learning’ Symposium 2018
Dr. Stefan Werning (Utrecht University)
Using Analytical Game Design to make
datasets ‘playable’ in the classroom
Making Datasets Playable – 03/05/18 – Slide No. 2
Team (in alphabetical order)
• Dennis Jansen, UU – NMDC, d.jansen5@students.uu.nl
• Brian de Lint, UU – NMDC, b.delint@students.uu.nl
• Amanda Moss, University of Groningen, a.moss.1@student.rug.nl
• Andrea di Pastena, UU – Comp.Lit., a.dipastena@students.uu.nl
• Supervisor: Stefan Werning, UU, s.werning@uu.nl
Making Datasets Playable – 03/05/18 – Slide No. 3
Preliminary Notes
• Reports on the early stages of an ongoing research project
• Our focus is on game design and media literacy considerations
⇒ We are not educational scholars!
⇒ In fact, we are looking for educational expertise related to this subject matter!
• Try out the prototype (both card game and digital version)!
 To access the digital prototype
after the symposium itself,
contact me at s.werning@uu.nl.
Making Datasets Playable – 03/05/18 – Slide No. 4
Context:
Data visualization and its discontents
• Making sense of datasets
becomes increasingly
socially relevant
• Epistemic limitations of
current visualization
methods
– E.g. (Passmann 2013) on Gephi
• Requires exploring and
teaching new ways to
‘make sense’ of data
– Sonification (e.g. Hermann/Ritter
1999) and haptification experiments
– Developing new quasi-sensory
modalities (David Eagleman at TED)
Paßmann, Johannes. 2013. “Forschungsmedien Erforschen.
Über Praxis Mit Der Daten-Mapping-Software Gephi.”
Navigationen 13 (2): 113–30.
Hermann, Thomas, and Helge Ritter. 1999. “Listen to Your Data:
Model-Based Sonification for Data Analysis.” In Advances in
Intelligent Computing and Multimedia Systems, edited by GE
Lasker and MR Syed, 8:189–94. Windsor, Ontario: International
Institute for Advanced Studies in System Research and
Cybernetics. https://doi.org/10.4119/unibi/2701116.
Making Datasets Playable – 03/05/18 – Slide No. 5
Making Datasets Playable
• Tapping into the ‘sense of
play’ (de Koven)
• Sample dataset on the
video game industry
– Combines sales and ratings data from
vgchartz and Metacritic
• Translated in a collectible
card game (CCG) using
nanDECK
– For use in the Niveau 3 BA course
Computer Games in Context at UU
• Students ‘play’ a publisher
Making Datasets Playable – 03/05/18 – Slide No. 6
Physical vs. Digital ‚Version‘
• Only the physical card game was
used in class
• Physical version (PRO)
– Affords more discussion as other students sit
around and advise the two players
– Tangible
– Cards on hand are more easily comparable
– Complex placement mechanics are easier to
implement
– Simple rule changes can be tested on the fly
– Players can’t look into each others’ cards
• Requires online connectivity in the digital version
• Physical version (CON)
– Only two players (as cards can only be
differentiated via orientation)
– Changing the card layout and data set used is
time-consuming and cumbersome
– Calculations (e.g. score) need to be kept simple
Making Datasets Playable – 03/05/18 – Slide No. 7
Educational goals of the project
• Learning goals
(according to Bloom’s taxonomy; cf. e.g. Arnab
et al. 2015)
– Understand the basic ‘mechanics’ of the game
industry (platforms, sales, awareness, critics)
• Also applicable to other creative industry (media
industry literacy)
– Understand and perform/apply the rationality of
game publishers
– Memorize important properties of the games in
the dataset
– Analyse/contextualize the data
• I.e. learn how games relate to one another, which
games were important and how/why etc.
– Raise questions about the games industry and
the games as its model
• Connect with 21st century skills
debate (according to Romero et al. 2014)
– “Learning to learn”
– Entrepreneurialism
– Risk-taking
– Creativity
– Understanding systems
Arnab, Sylvester, Theodore Lim, Maira B. Carvalho, Francesco
Bellotti, Sara Freitas, Sandy Louchart, Neil Suttie, Riccardo Berta,
and Alessandro De Gloria. "Mapping learning and game
mechanics for serious games analysis." British Journal of
Educational Technology 46, no. 2 (2015): 391-411.
Romero, M., M. Usart, and M. Ott. 2014. “Can Serious Games
Contribute to Developing and Sustaining 21st Century Skills?”
Games and Culture 10 (2): 148–77.
https://doi.org/10.1177/1555412014548919.
Making Datasets Playable – 03/05/18 – Slide No. 8
The In-Class Experiment
• Two work groups
• 15 min. introduction
• 45 min. play session
• 30 min. in-class discussion
• Online survey for more open-
ended questions
1. What did and didn't you enjoy about
the game?
2. What did you learn about the game
industry?
3. What did you learn about the dataset,
i.e. about the games represented by
the cards?
4. Think of one or more potential
changes or improvements that could
be implemented into the game. How
would these modifications change its
procedural rhetoric?
Making Datasets Playable – 03/05/18 – Slide No. 9
Limitations of the In-Class Experiment
• Did not afford multiple
playthroughs
– Familiarizing oneself with the data takes
time
– Understanding the game-as-model
requires game literacy (Bourgonjon 2014)
• Needs to be more reflectively
incorporated into a curriculum
(Squire 2002)
– “In a hypothetical Civilization III unit,
students might spent 25 percent of their
time playing the game, and the remainder
of the time creating maps, historical
timelines, researching game concepts,
drawing parallels to historical or current
events, or interacting with other media,
such as books or videos.”
Bourgonjon, Jeroen. "The meaning and relevance of video
game literacy." CLCWeb: Comparative Literature and
Culture, vol. 16, no. 5, 2014. Academic OneFile, Accessed
15 Mar. 2018.
Squire, Kurt. 2002. “Cultural Framing of Computer/Video
Games.” Game Studies 2 (1).
http://www.gamestudies.org/0102/squire/.
Making Datasets Playable – 03/05/18 – Slide No. 10
Evaluating Player Feedback
• Q1: Criticism
– Participants expected the design to be
naturalistic
• “I did not enjoy the fact that there where
preset numbers like sales and community
ratings because if you are a publisher of
games then you can not know these
numbers”
– “we did not enjoy the fact that you could
compete against your own cards. This
caused a divided playfield, were the teams
did not compete against each other, but
strategically located their own cards in
order to score as much points as possible”
– “You can't really work around a bad card”
• Q2: Learning about the industry
– “[successful] games [...] don't have to be
top notch games”
– “it can be smart to release a game which
you know won't succeed only to then
release a better game after it”
• Q3: Learning about the dataset
– “how some games turned out to be
successful based only on their choice of
platform”
– “very high buzz but low selling rate. It
made you think, and get curious about
why they talked about it that much”
– Occasionally more ‘analytical results: e.g.
“The games represented can be viewed
both as entertainment media or as
commodity products”
• Q4: Potential changes
– Comments often focused on usability and
appeal, i.e. on ‘product’ categories
– “interesting if the games decay over time
[…] I think that is something that happens
in the actual gaming world as well”)
Making Datasets Playable – 03/05/18 – Slide No. 11
Narrativization of in-game events
• When asked to reflect on their learning
experience, students usually responded by
retelling their gameplay as a story
(cf. Celia Pearce, narrative operators)
• Leveraging narrative sensemaking (e.g.
Cunliffe/Coupland 2012)
– Derived from narrative identity construction (Ricoeur)
– Applicability to learning about systems still needs to be
explored further
– Main difference compared to traditional data visualization
• Built-in semantic/narrative elements

Player stories (Atkins 2003)
• Sample ‘narratives’
– Card game involved only two players  i.e. often stories about
rivalry
– Sequence of games ‘played’ is interpreted hermeneutically
• Even though the cards are randomly distributed
– Individual games defying expectations
Cunliffe, Ann, and Chris Coupland. 2012. “From Hero
to Villain to Hero: Making Experience Sensible through
Embodied Narrative Sensemaking.” Human Relations
65 (1). SAGE Publications: London, England: 63–88.
Atkins, Barry. 2003. More than a Game: The Computer
Game as Fictional Form. Manchester: Manchester
University Press.
Making Datasets Playable – 03/05/18 – Slide No. 12
Narrativization of in-game events:
Random event cards
• Many players needed to be
prompted by the teacher
engaging in narrativization
•  “Individual cognitive
absorption” (e.g. Saadé/Bahli
2005)
– “Too busy ‘playing the game’”
• Random Event cards as a
potential remedy
– Material for narrativization to
counterbalance cognitive absorption
– Not solely about ‘getting good at the game’
– Support the teacher’s role in stimulating
narrativization
Saadé, Raatat, and Bouchaib Bahli. 2005. “The Impact of
Cognitive Absorption on Perceived Usefulness and Perceived
Ease of Use in on-Line Learning: An Extension of the
Technology Acceptance Model.” Information and Management
42 (2). North-Holland: 317–27.
• #GamerGate
• Lootbox controversy
• School shooting in America
• Something with e-sports
• PSN hack
• Employee dispute
(West and Zampella moving from
Activision to EA)
• Crowdfunding fraud
• #FuckKonami (Konami vs. Hideo Kojima)
• The HOT COFFEE Mod
• Creation of the ESRB as a response to
Mortal Kombat
Making Datasets Playable – 03/05/18 – Slide No. 13
Outlook: Encouraging students
to co-design the game
• Necessary to challenge the
authority of the game as a
‘domain model’ (Marne Et Al.
2012, 209)
– Cf. e.g. (Kafai/Burke 2016) on the
“benefits of making games for
[constructionist] learning”
• Analytical Game Design
– Productive irritations
– Part of an ongoing practice-based
research process
• Game vignettes as ‘theoretical
objects’ (Mieke Bal)
Marne, Bertrand, John Wisdom, Benjamin Huynh-Kim-Bang,
and Jean-Marc Labat. 2012. “The Six Facets of Serious Game
Design: A Methodology Enhanced by Our Design Pattern
Library.” In 21st Century Learning for 21st Century Skills, edited
by Andrew Ravenscroft, Stefanie Lindstaedt, Carlos Delgado
Kloos, and Davinia Hernández-Leo, 208–21. Berlin &
Heidelberg: Springer.
Kafai, Yasmin B., and Quinn Burke. 2015. “Constructionist
Gaming: Understanding the Benefits of Making Games for
Learning.” Educational Psychologist 50 (4). Routledge: 313–34.
https://doi.org/10.1080/00461520.2015.1124022.
Making Datasets Playable – 03/05/18 – Slide No. 14
Outlook:
Generalizability of the Approach
• Making datasets ‘playable’ in the classroom
• Also applicable to other (types of) datasets
– Kickstarter
– Airbnb
– Fake News
• Methodological considerations
– Creating genre archetypes to analyze different types of data
• Similar to how visualization algorithms are tailored to particular datasets
• Cf. e.g. ForceAtlas2 as default algorithm for network visualization
– Evaluating learning results more systematically and over a longer period of time
– Combining this approach with traditional data visualization to learn about datasets
Making Datasets Playable – 03/05/18 – Slide No. 15

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Using Analytical Game Design to make datasets ‘playable’ in the classroom

  • 1. Making Datasets Playable – 03/05/18 – Slide No. 1 ‘Games for Learning’ Symposium 2018 Dr. Stefan Werning (Utrecht University) Using Analytical Game Design to make datasets ‘playable’ in the classroom
  • 2. Making Datasets Playable – 03/05/18 – Slide No. 2 Team (in alphabetical order) • Dennis Jansen, UU – NMDC, d.jansen5@students.uu.nl • Brian de Lint, UU – NMDC, b.delint@students.uu.nl • Amanda Moss, University of Groningen, a.moss.1@student.rug.nl • Andrea di Pastena, UU – Comp.Lit., a.dipastena@students.uu.nl • Supervisor: Stefan Werning, UU, s.werning@uu.nl
  • 3. Making Datasets Playable – 03/05/18 – Slide No. 3 Preliminary Notes • Reports on the early stages of an ongoing research project • Our focus is on game design and media literacy considerations ⇒ We are not educational scholars! ⇒ In fact, we are looking for educational expertise related to this subject matter! • Try out the prototype (both card game and digital version)!  To access the digital prototype after the symposium itself, contact me at s.werning@uu.nl.
  • 4. Making Datasets Playable – 03/05/18 – Slide No. 4 Context: Data visualization and its discontents • Making sense of datasets becomes increasingly socially relevant • Epistemic limitations of current visualization methods – E.g. (Passmann 2013) on Gephi • Requires exploring and teaching new ways to ‘make sense’ of data – Sonification (e.g. Hermann/Ritter 1999) and haptification experiments – Developing new quasi-sensory modalities (David Eagleman at TED) Paßmann, Johannes. 2013. “Forschungsmedien Erforschen. Über Praxis Mit Der Daten-Mapping-Software Gephi.” Navigationen 13 (2): 113–30. Hermann, Thomas, and Helge Ritter. 1999. “Listen to Your Data: Model-Based Sonification for Data Analysis.” In Advances in Intelligent Computing and Multimedia Systems, edited by GE Lasker and MR Syed, 8:189–94. Windsor, Ontario: International Institute for Advanced Studies in System Research and Cybernetics. https://doi.org/10.4119/unibi/2701116.
  • 5. Making Datasets Playable – 03/05/18 – Slide No. 5 Making Datasets Playable • Tapping into the ‘sense of play’ (de Koven) • Sample dataset on the video game industry – Combines sales and ratings data from vgchartz and Metacritic • Translated in a collectible card game (CCG) using nanDECK – For use in the Niveau 3 BA course Computer Games in Context at UU • Students ‘play’ a publisher
  • 6. Making Datasets Playable – 03/05/18 – Slide No. 6 Physical vs. Digital ‚Version‘ • Only the physical card game was used in class • Physical version (PRO) – Affords more discussion as other students sit around and advise the two players – Tangible – Cards on hand are more easily comparable – Complex placement mechanics are easier to implement – Simple rule changes can be tested on the fly – Players can’t look into each others’ cards • Requires online connectivity in the digital version • Physical version (CON) – Only two players (as cards can only be differentiated via orientation) – Changing the card layout and data set used is time-consuming and cumbersome – Calculations (e.g. score) need to be kept simple
  • 7. Making Datasets Playable – 03/05/18 – Slide No. 7 Educational goals of the project • Learning goals (according to Bloom’s taxonomy; cf. e.g. Arnab et al. 2015) – Understand the basic ‘mechanics’ of the game industry (platforms, sales, awareness, critics) • Also applicable to other creative industry (media industry literacy) – Understand and perform/apply the rationality of game publishers – Memorize important properties of the games in the dataset – Analyse/contextualize the data • I.e. learn how games relate to one another, which games were important and how/why etc. – Raise questions about the games industry and the games as its model • Connect with 21st century skills debate (according to Romero et al. 2014) – “Learning to learn” – Entrepreneurialism – Risk-taking – Creativity – Understanding systems Arnab, Sylvester, Theodore Lim, Maira B. Carvalho, Francesco Bellotti, Sara Freitas, Sandy Louchart, Neil Suttie, Riccardo Berta, and Alessandro De Gloria. "Mapping learning and game mechanics for serious games analysis." British Journal of Educational Technology 46, no. 2 (2015): 391-411. Romero, M., M. Usart, and M. Ott. 2014. “Can Serious Games Contribute to Developing and Sustaining 21st Century Skills?” Games and Culture 10 (2): 148–77. https://doi.org/10.1177/1555412014548919.
  • 8. Making Datasets Playable – 03/05/18 – Slide No. 8 The In-Class Experiment • Two work groups • 15 min. introduction • 45 min. play session • 30 min. in-class discussion • Online survey for more open- ended questions 1. What did and didn't you enjoy about the game? 2. What did you learn about the game industry? 3. What did you learn about the dataset, i.e. about the games represented by the cards? 4. Think of one or more potential changes or improvements that could be implemented into the game. How would these modifications change its procedural rhetoric?
  • 9. Making Datasets Playable – 03/05/18 – Slide No. 9 Limitations of the In-Class Experiment • Did not afford multiple playthroughs – Familiarizing oneself with the data takes time – Understanding the game-as-model requires game literacy (Bourgonjon 2014) • Needs to be more reflectively incorporated into a curriculum (Squire 2002) – “In a hypothetical Civilization III unit, students might spent 25 percent of their time playing the game, and the remainder of the time creating maps, historical timelines, researching game concepts, drawing parallels to historical or current events, or interacting with other media, such as books or videos.” Bourgonjon, Jeroen. "The meaning and relevance of video game literacy." CLCWeb: Comparative Literature and Culture, vol. 16, no. 5, 2014. Academic OneFile, Accessed 15 Mar. 2018. Squire, Kurt. 2002. “Cultural Framing of Computer/Video Games.” Game Studies 2 (1). http://www.gamestudies.org/0102/squire/.
  • 10. Making Datasets Playable – 03/05/18 – Slide No. 10 Evaluating Player Feedback • Q1: Criticism – Participants expected the design to be naturalistic • “I did not enjoy the fact that there where preset numbers like sales and community ratings because if you are a publisher of games then you can not know these numbers” – “we did not enjoy the fact that you could compete against your own cards. This caused a divided playfield, were the teams did not compete against each other, but strategically located their own cards in order to score as much points as possible” – “You can't really work around a bad card” • Q2: Learning about the industry – “[successful] games [...] don't have to be top notch games” – “it can be smart to release a game which you know won't succeed only to then release a better game after it” • Q3: Learning about the dataset – “how some games turned out to be successful based only on their choice of platform” – “very high buzz but low selling rate. It made you think, and get curious about why they talked about it that much” – Occasionally more ‘analytical results: e.g. “The games represented can be viewed both as entertainment media or as commodity products” • Q4: Potential changes – Comments often focused on usability and appeal, i.e. on ‘product’ categories – “interesting if the games decay over time […] I think that is something that happens in the actual gaming world as well”)
  • 11. Making Datasets Playable – 03/05/18 – Slide No. 11 Narrativization of in-game events • When asked to reflect on their learning experience, students usually responded by retelling their gameplay as a story (cf. Celia Pearce, narrative operators) • Leveraging narrative sensemaking (e.g. Cunliffe/Coupland 2012) – Derived from narrative identity construction (Ricoeur) – Applicability to learning about systems still needs to be explored further – Main difference compared to traditional data visualization • Built-in semantic/narrative elements  Player stories (Atkins 2003) • Sample ‘narratives’ – Card game involved only two players  i.e. often stories about rivalry – Sequence of games ‘played’ is interpreted hermeneutically • Even though the cards are randomly distributed – Individual games defying expectations Cunliffe, Ann, and Chris Coupland. 2012. “From Hero to Villain to Hero: Making Experience Sensible through Embodied Narrative Sensemaking.” Human Relations 65 (1). SAGE Publications: London, England: 63–88. Atkins, Barry. 2003. More than a Game: The Computer Game as Fictional Form. Manchester: Manchester University Press.
  • 12. Making Datasets Playable – 03/05/18 – Slide No. 12 Narrativization of in-game events: Random event cards • Many players needed to be prompted by the teacher engaging in narrativization •  “Individual cognitive absorption” (e.g. Saadé/Bahli 2005) – “Too busy ‘playing the game’” • Random Event cards as a potential remedy – Material for narrativization to counterbalance cognitive absorption – Not solely about ‘getting good at the game’ – Support the teacher’s role in stimulating narrativization Saadé, Raatat, and Bouchaib Bahli. 2005. “The Impact of Cognitive Absorption on Perceived Usefulness and Perceived Ease of Use in on-Line Learning: An Extension of the Technology Acceptance Model.” Information and Management 42 (2). North-Holland: 317–27. • #GamerGate • Lootbox controversy • School shooting in America • Something with e-sports • PSN hack • Employee dispute (West and Zampella moving from Activision to EA) • Crowdfunding fraud • #FuckKonami (Konami vs. Hideo Kojima) • The HOT COFFEE Mod • Creation of the ESRB as a response to Mortal Kombat
  • 13. Making Datasets Playable – 03/05/18 – Slide No. 13 Outlook: Encouraging students to co-design the game • Necessary to challenge the authority of the game as a ‘domain model’ (Marne Et Al. 2012, 209) – Cf. e.g. (Kafai/Burke 2016) on the “benefits of making games for [constructionist] learning” • Analytical Game Design – Productive irritations – Part of an ongoing practice-based research process • Game vignettes as ‘theoretical objects’ (Mieke Bal) Marne, Bertrand, John Wisdom, Benjamin Huynh-Kim-Bang, and Jean-Marc Labat. 2012. “The Six Facets of Serious Game Design: A Methodology Enhanced by Our Design Pattern Library.” In 21st Century Learning for 21st Century Skills, edited by Andrew Ravenscroft, Stefanie Lindstaedt, Carlos Delgado Kloos, and Davinia Hernández-Leo, 208–21. Berlin & Heidelberg: Springer. Kafai, Yasmin B., and Quinn Burke. 2015. “Constructionist Gaming: Understanding the Benefits of Making Games for Learning.” Educational Psychologist 50 (4). Routledge: 313–34. https://doi.org/10.1080/00461520.2015.1124022.
  • 14. Making Datasets Playable – 03/05/18 – Slide No. 14 Outlook: Generalizability of the Approach • Making datasets ‘playable’ in the classroom • Also applicable to other (types of) datasets – Kickstarter – Airbnb – Fake News • Methodological considerations – Creating genre archetypes to analyze different types of data • Similar to how visualization algorithms are tailored to particular datasets • Cf. e.g. ForceAtlas2 as default algorithm for network visualization – Evaluating learning results more systematically and over a longer period of time – Combining this approach with traditional data visualization to learn about datasets
  • 15. Making Datasets Playable – 03/05/18 – Slide No. 15