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

thank you!

doug.clark@vanderbilt.edu

wise4.berkeley.edu