gordon clark2011
TRANSCRIPT
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Clark, D. B., Nelson, B., Slack, K., Martinez-Garza, M., & D’Angelo, C. M. (2011). Games and sims bridging intuitive and formal understandings of physics. Talk commissioned by the Gordon Research Conference on Visualization, Smithfield, Rhode Island.
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games and sims bridging intuitive and formal understandings of physicsDouglas Clark, Brian Nelson, Kent Slack, Mario Martinez-Garza, & Cynthia D’Angelo
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digital simulations?
computational models of real or hypothesized situations or phenomena that allow users to explore the implications of manipulating or modifying parameters within the models
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digital games?
definitions of games focus on rules, choices, play, and systems for tracking progress or success
digital games involve:• digital models that allow users to make interesting
choices with meaningful implications• an overarching set of explicit goals with
accompanying systems for measuring progress• subjective opportunities for play and engagement
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digital games
virtual worlds
digital simulations
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Games ≠ GoodGames ≠ Bad
Ga
are games good = bad question
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just like… Labs ≠ Good Labs ≠ Bad
Ga
just like…
are labs good = bad question (or lectures, novels, movies, etc.)
(NRC, 2005)
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Games ≠ GoodGames ≠ Bad
Ga
games = medium with specific affordances and constraints (just like books, simulations, labs, movies, and lectures)
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Games ≠ GoodGames ≠ Bad
Ga
better question:
which designs and structures optimize which outcomes for whom and how?
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digital games are to simulations as feature films are to animations
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good digital games help people construct productive mental models for operating on the underlying simulations
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affordances
good digital games can provide:• engagement / approachable entry • context / identification• point of view / pathway• stakes / investment• monitoring / feedback / pacing / gatekeeping
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competition between learning goals and game design goals (e.g., visual complexity, competing mechanics, surface vs. core features)
Tech
Game Design
Learning Goals
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“game” = the software
“Game” = community, practices, artifacts, and interactions around the game
(Gee, 2007)
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"conceptually-embedded" games = science processes embedded within the game world "conceptually-integrated" games = science concepts integrated directly into core mechanics of game environment
(Clark & Martinez-Garza, in press)
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Vygotsky’s “spontaneous” and “scientific” concepts
different ways of knowing physicscan be used to bootstrap one another
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What design principles for digital games will support the development of intuitive understanding (“spontaneous” concepts”) and help bridge these concepts with instructed “scientific” concepts?
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do students
learn?
is learning skewed by
prior experience or gender?
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students made progress on challenging items based on the FCI(but effect sizes and power modest)
Experimental group N p <
US undergrad & graduate students 24 0.0290
Taiwan & US 7-9th grade students 250 0.0190
US undergrad physics students 155 0.0010
US Title I urban 6th grade students 69 0.0197
US undergrad ed-psych students 72 0.0060
(Learning and Affective Outcomes discussed in Clark, Nelson, Chang, Martinez-Garza, Slack, & D’Angelo, in press)
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similarities across countries and genders in terms of gaming habits and attitudes about SURGE
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equitable outcomesboys replay levels somewhat more frequently.
no significant gender differences in learning outcomes
learning outcomes not correlated with reported gaming habits.
similarities between countries in affective and learning outcomes.
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visualizing gameplay data
frequency of death by location in cp_dustbowl(Team Fortress 2)
commercial game design knows the value of gameplay data
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Heat map of player locations every 5 seconds(Halo 3)
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our initial efforts100,710,attemptcommand100,710,tick,-46.61,24.40,.00,.00,1.00100,710,tick,-46.61,24.40,.00,.00,2.00100,710,tick,-46.61,24.40,.00,.00,3.00100,710,tick,-46.61,24.40,.00,.00,4.00100,710,tick,-46.61,24.40,.00,.00,5.00100,710,impulse,-46.61,24.40,0,3,5.08100,710,tick,-43.82,24.40,3.00,.00,6.00100,710,tick,-40.82,24.40,3.00,.00,7.00100,710,tick,-37.82,24.40,3.00,.00,8.00100,710,tick,-34.82,24.40,3.00,.00,9.00100,710,impulse,-32.90,24.40,270,3,9.65100,710,tick,-31.82,23.32,3.00,-3.00,10.00100,710,tick,-28.82,20.32,3.00,-3.00,11.00100,710,impulse,-26.09,17.59,180,3,11.92100,710,tick,-26.09,17.32,.00,-3.00,12.00100,710,tick,-26.09,14.32,.00,-3.00,13.00100,710,tick,-26.09,11.32,.00,-3.00,14.00100,710,tick,-26.09,8.32,.00,-3.00,15.00100,710,tick,-26.09,5.32,.00,-3.00,16.00100,710,tick,-26.09,2.32,.00,-3.00,17.00100,710,tick,-26.09,-.68,.00,-3.00,18.00100,710,tick,-26.09,-3.68,.00,-3.00,19.00100,710,tick,-26.09,-6.68,.00,-3.00,20.00100,710,tick,-26.09,-9.68,.00,-3.00,21.00100,710,impulse,-26.09,-11.93,0,3,21.76100,710,tick,-25.34,-12.68,3.00,-3.00,22.00100,710,impulse,-23.60,-14.42,0,3,22.59100,710,tick,-21.08,-15.68,6.00,-3.00,23.00100,710,impulse,-20.60,-15.92,0,3,23.09100,710,collision,-15.74,-17.48,0,0,23.62100,710,impulse,-15.38,-17.36,90,3,23.67100,710,tick,-12.32,-15.32,9.00,6.00,24.00100,710,impulse,-9.17,-13.22,0,3,24.36100,710,collision,-5.57,-11.54,0,0,24.65100,710,tick,-1.37,-13.64,12.00,-6.00,25.00100,710,collision,6.55,-17.48,0,0,25.66100,710,tick,10.63,-15.44,12.00,6.00,26.00100,710,collision,18.67,-11.54,0,0,26.67100,710,tick,22.63,-13.52,12.00,-6.00,27.00100,710,impulse,23.59,-14.00,90,3,27.09100,710,collision,28.99,-15.41,0,0,27.55100,710,tick,23.59,-16.76,-12.00,-3.00,28.00100,710,impulse,22.15,-17.12,90,3,28.13100,710,impulse,16.87,-17.12,0,3,28.57100,710,tick,12.91,-17.12,-9.00,.00,29.00100,710,impulse,11.38,-17.12,0,3,29.17100,710,impulse,9.46,-17.12,0,3,29.50100,710,impulse,8.74,-17.12,0,3,29.74100,710,tick,8.74,-17.12,.00,.00,30.00100,710,impulse,8.74,-17.12,0,3,30.19
(etc)
Ploticus graphing package
(game play data analysis discussed in Martinez-Garza, Clark, Nelson, Slack, & D’Angelo, submitted)
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visualization of one student’s path through m1-1
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UUU
LUU
UULUUULULLU
…
UULU
“augmented” screenshot of SURGE gameplay
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sequential pattern analysis
UUU
LUU
UULUUULULLU
UULUUUU
LUU
UULUUULULLU
UULU
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hidden markov modelingZ3 + Z1 – Z2 = learning
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what next?
how can we provide players with access to these visualizations of their gameplay data to scaffold learning?
what types of visualizations would be diagnostically useful for teachers?
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SURGE design
engagement / approachable entry
context / identification
point of view / pathway
stakes / investment
monitoring / feedback / pacing / gatekeeping
Tech
Game Design
Learning Goals
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flexibly explore designs to integrate game, learning, and architecture goals
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players need to learn and use physics principles and representations to succeed in the game
subsequent levels aggregate concepts and representations
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embed game in a storyline with broad appeal
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support articulation of intuitive and formal ideas
prediction through navigation interface
– planned– real-time
explanation through dialog– standard game dialog text
selection– iconic of sentence fragment
construction
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integrate popular gameplay mechanics with formal physics representations and concepts
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protecting novice players from frustration cannot allow progress without mastery
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protecting novice players from frustration cannot allow progress without mastery
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focus on “just-in-time” feedback and signaling
(Cuing and Visual Signaling work discussed in Slack, Nelson, Clark, Martinez-Garza, & D’Angelo, in preparation)
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support broad challenge curve
• keep people from falling off with “just in time” support• minimize costs of failure and experimentation• encourage improved performance through non-game
mechanic influencing incentives• game increases difficulty correlated to performance• multiple paths or solutions of varying difficulty and reward
BoredDejected
Engaged
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Part III:
our next tech plan could be yours, too
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pragmatic tech constraints
schools– bandwidth – processing power – administrative privileges for installation – firewalls
development bottlenecks– multiple programmers simultaneously– non-programmers design and revise
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editor for level set-up strings
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WISE 4 = hub
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easy to add tools and activities
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no programming required
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lots of step types already
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teacher management tools including grading
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teachers can pause the class computers
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status updates and alerts for teachers
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plan
schools– bandwidth < 200 kb player & small xml files– processing power simple flash– administrative privileges for installation none– Firewalls port 80
development bottlenecks– multiple programmers simultaneously yes– non-programmers design and revise yes
SURGE FLASH PLAYER
WISEENVIRONMENT
WISE DATABASE
STUDENT PORTAL
TEACHER / RESEARCHER PORTAL
XMLDATA FILEXML
DATA FILEXMLDATA FILEXML
DATA FILEXMLDATA FILEXML
DATA FILE
XMLCATALOG FILE