Download - Gordon clark2011
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.
games and sims bridging intuitive and formal understandings of physicsDouglas Clark, Brian Nelson, Kent Slack, Mario Martinez-Garza, & Cynthia D’Angelo
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
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
digital games
virtual worlds
digital simulations
Games ≠ GoodGames ≠ Bad
Ga
are games good = bad question
just like… Labs ≠ Good Labs ≠ Bad
Ga
just like…
are labs good = bad question (or lectures, novels, movies, etc.)
(NRC, 2005)
Games ≠ GoodGames ≠ Bad
Ga
games = medium with specific affordances and constraints (just like books, simulations, labs, movies, and lectures)
Games ≠ GoodGames ≠ Bad
Ga
better question:
which designs and structures optimize which outcomes for whom and how?
digital games are to simulations as feature films are to animations
good digital games help people construct productive mental models for operating on the underlying simulations
affordances
good digital games can provide:• engagement / approachable entry • context / identification• point of view / pathway• stakes / investment• monitoring / feedback / pacing / gatekeeping
competition between learning goals and game design goals (e.g., visual complexity, competing mechanics, surface vs. core features)
Tech
Game Design
Learning Goals
“game” = the software
“Game” = community, practices, artifacts, and interactions around the game
(Gee, 2007)
"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)
Vygotsky’s “spontaneous” and “scientific” concepts
different ways of knowing physicscan be used to bootstrap one another
What design principles for digital games will support the development of intuitive understanding (“spontaneous” concepts”) and help bridge these concepts with instructed “scientific” concepts?
do students
learn?
is learning skewed by
prior experience or gender?
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)
similarities across countries and genders in terms of gaming habits and attitudes about SURGE
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.
visualizing gameplay data
frequency of death by location in cp_dustbowl(Team Fortress 2)
commercial game design knows the value of gameplay data
Heat map of player locations every 5 seconds(Halo 3)
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)
visualization of one student’s path through m1-1
UUU
LUU
UULUUULULLU
…
UULU
“augmented” screenshot of SURGE gameplay
sequential pattern analysis
UUU
LUU
UULUUULULLU
UULUUUU
LUU
UULUUULULLU
UULU
hidden markov modelingZ3 + Z1 – Z2 = learning
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?
SURGE design
engagement / approachable entry
context / identification
point of view / pathway
stakes / investment
monitoring / feedback / pacing / gatekeeping
Tech
Game Design
Learning Goals
flexibly explore designs to integrate game, learning, and architecture goals
players need to learn and use physics principles and representations to succeed in the game
subsequent levels aggregate concepts and representations
embed game in a storyline with broad appeal
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
integrate popular gameplay mechanics with formal physics representations and concepts
protecting novice players from frustration cannot allow progress without mastery
protecting novice players from frustration cannot allow progress without mastery
focus on “just-in-time” feedback and signaling
(Cuing and Visual Signaling work discussed in Slack, Nelson, Clark, Martinez-Garza, & D’Angelo, in preparation)
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
Part III:
our next tech plan could be yours, too
pragmatic tech constraints
schools– bandwidth – processing power – administrative privileges for installation – firewalls
development bottlenecks– multiple programmers simultaneously– non-programmers design and revise
editor for level set-up strings
WISE 4 = hub
easy to add tools and activities
no programming required
lots of step types already
teacher management tools including grading
teachers can pause the class computers
status updates and alerts for teachers
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
