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Gordon Clark 2011
- 1. 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.
- 2. SURGE games and simsbridging intuitive and formal understandings of physics Douglas Clark, Brian Nelson, Kent Slack, Mario Martinez-Garza, & Cynthia D’Angelo
- 3. 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
- 4. 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
- 6. digital simulations digital games virtual worlds
- 7. Games ≠ GoodGames ≠ Bad Ga are games good = bad question
- 8. just like… Labs ≠ GoodLabs ≠ Bad Ga just like… are labs good = bad question (or lectures, novels, movies, etc.) (NRC, 2005)
- 9. Games ≠ GoodGames ≠ Bad Ga games = medium with specific affordances and constraints (just like books, simulations, labs, movies, and lectures)
- 10. Games ≠ GoodGames ≠ Bad Ga better question: which designs and structures optimize which outcomes for whom and how?
- 11. digital games are to simulations as feature films are to animations
- 12. good digital games help people construct productive mental models for operating on the underlying simulations
- 13. affordances good digital games can provide: engagement / approachable entry context / identification point of view / pathway stakes / investment monitoring / feedback / pacing / gatekeeping
- 15. competition between learning goals and game design goals (e.g., visual complexity, competing mechanics, surface vs. core features) Learning Goals Tech Game Design
- 16. “game” = the software “Game” = community, practices, artifacts, and interactions around the game (Gee, 2007)
- 17. "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)
- 18. Vygotsky’s “spontaneous” and “scientific” concepts different ways of knowing physics can be used to bootstrap one another
- 19. What design principles for digital games will support the development of intuitive understanding (“spontaneous” concepts”) and help bridge these concepts with instructed “scientific” concepts?
- 20. do students learn?is learning skewed by prior experience or gender?
- 21. students made progress on challenging items based on the FCI(but effect sizes and power modest) (Learning and Affective Outcomes discussed in Clark, Nelson, Chang, Martinez-Garza, Slack, & D’Angelo, in press)
- 22. similarities across countries and genders in terms of gaming habits and attitudes about SURGE
- 23. equitable outcomes boys 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.
- 24. visualizing gameplay data commercial game design knows the value of gameplay data frequency of death by location in cp_dustbowl(Team Fortress 2)
- 25. Heat map of player locations every 5 seconds(Halo 3)
- 26. our initial efforts 100,710,attemptcommand 100,710,tick,-46.61,24.40,.00,.00,1.00 100,710,tick,-46.61,24.40,.00,.00,2.00 100,710,tick,-46.61,24.40,.00,.00,3.00 100,710,tick,-46.61,24.40,.00,.00,4.00 100,710,tick,-46.61,24.40,.00,.00,5.00 100,710,impulse,-46.61,24.40,0,3,5.08 100,710,tick,-43.82,24.40,3.00,.00,6.00 100,710,tick,-40.82,24.40,3.00,.00,7.00 100,710,tick,-37.82,24.40,3.00,.00,8.00 100,710,tick,-34.82,24.40,3.00,.00,9.00 100,710,impulse,-32.90,24.40,270,3,9.65 100,710,tick,-31.82,23.32,3.00,-3.00,10.00 100,710,tick,-28.82,20.32,3.00,-3.00,11.00 100,710,impulse,-26.09,17.59,180,3,11.92 100,710,tick,-26.09,17.32,.00,-3.00,12.00 100,710,tick,-26.09,14.32,.00,-3.00,13.00 100,710,tick,-26.09,11.32,.00,-3.00,14.00 100,710,tick,-26.09,8.32,.00,-3.00,15.00 100,710,tick,-26.09,5.32,.00,-3.00,16.00 100,710,tick,-26.09,2.32,.00,-3.00,17.00 100,710,tick,-26.09,-.68,.00,-3.00,18.00 100,710,tick,-26.09,-3.68,.00,-3.00,19.00 100,710,tick,-26.09,-6.68,.00,-3.00,20.00 100,710,tick,-26.09,-9.68,.00,-3.00,21.00 100,710,impulse,-26.09,-11.93,0,3,21.76 100,710,tick,-25.34,-12.68,3.00,-3.00,22.00 100,710,impulse,-23.60,-14.42,0,3,22.59 100,710,tick,-21.08,-15.68,6.00,-3.00,23.00 100,710,impulse,-20.60,-15.92,0,3,23.09 100,710,collision,-15.74,-17.48,0,0,23.62 100,710,impulse,-15.38,-17.36,90,3,23.67 100,710,tick,-12.32,-15.32,9.00,6.00,24.00 100,710,impulse,-9.17,-13.22,0,3,24.36 100,710,collision,-5.57,-11.54,0,0,24.65 100,710,tick,-1.37,-13.64,12.00,-6.00,25.00 100,710,collision,6.55,-17.48,0,0,25.66 100,710,tick,10.63,-15.44,12.00,6.00,26.00 100,710,collision,18.67,-11.54,0,0,26.67 100,710,tick,22.63,-13.52,12.00,-6.00,27.00 100,710,impulse,23.59,-14.00,90,3,27.09 100,710,collision,28.99,-15.41,0,0,27.55 100,710,tick,23.59,-16.76,-12.00,-3.00,28.00 100,710,impulse,22.15,-17.12,90,3,28.13 100,710,impulse,16.87,-17.12,0,3,28.57 100,710,tick,12.91,-17.12,-9.00,.00,29.00 100,710,impulse,11.38,-17.12,0,3,29.17 100,710,impulse,9.46,-17.12,0,3,29.50 100,710,impulse,8.74,-17.12,0,3,29.74 100,710,tick,8.74,-17.12,.00,.00,30.00 100,710,impulse,8.74,-17.12,0,3,30.19 (etc) Ploticusgraphing package (game play data analysis discussed in Martinez-Garza, Clark, Nelson, Slack, & D’Angelo, submitted)
- 27. visualization of one student’s path through m1-1
- 29. UULU UUU UULU UULU LLU LUU … “augmented” screenshot of SURGE gameplay
- 30. sequential pattern analysis UULU UULU UUU UUU UULU UULU UULU UULU LLU LLU LUU LUU
- 31. hidden markov modeling Z3 + Z1 – Z2 = learning
- 32. 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?
- 33. SURGE design Learning Goals engagement / approachable entry context / identification point of view / pathway stakes / investment monitoring / feedback / pacing / gatekeeping Tech Game Design
- 34. flexibly explore designs to integrate game, learning, and architecture goals
- 35. players need to learn and use physics principles and representations to succeed in the game subsequent levels aggregate concepts and representations
- 36. embed game in a storyline with broad appeal
- 37. 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
- 38. integrate popular gameplay mechanics with formal physics representations and concepts
- 39. protecting novice players from frustration cannot allow progress without mastery
- 40. protecting novice players from frustration cannot allow progress without mastery
- 41. focus on “just-in-time” feedback and signaling (Cuing and Visual Signaling work discussed in Slack, Nelson, Clark, Martinez-Garza, & D’Angelo, in preparation)
- 42. support broad challenge curve Engaged Dejected Bored keep people from falling off with “just in time” support minimize costs of failureand 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
- 45. Part III:our next tech plan could be yours, too
- 46. pragmatic tech constraints schools bandwidth processing power administrative privileges for installation firewalls development bottlenecks multiple programmers simultaneously non-programmers design and revise
- 47. editor for level set-up strings
- 48. WISE 4 = hub
- 49. easy to add tools and activities
- 51. lots of step types already
- 52. teacher management tools including grading
- 53. teachers can pause the class computers
- 54. status updates and alerts for teachers
- 55. plan STUDENT PORTAL TEACHER / RESEARCHER PORTAL XML CATALOG FILE SURGE FLASH PLAYER WISE ENVIRONMENT XML DATA FILE XML DATA FILE XML DATA FILE XML DATA FILE XML DATA FILE XML DATA FILE 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 WISE DATABASE
Notas do Editor
- connects well with Richard Lowe’s emphasis on the composition processes
- Richard Lowe’s focus on scaffolding composition
- with Cohen’s d = 0.1541 and 0.344, respectively.
- connects well with Richard Lowe’s emphasis on the composition processes
- Torque marble blastMolecular workbench marble blast but move to wallsUnity marble blast kept walls because collisions were considered interesting part of game play and useful learningFlash / gravitee
- avoid twitch and support strategy -- no tight mazes -- more conceptual -- this wan’tengouh -- prediciton and explanaiton came out as suggestions from science ed but journal seems not gamelike -- now we are doing pred in navigationa and exp in game dialog
- challenges as players import game templates and expectations [impulse engine hard long key presses]
- Cutscenes, Static Fuzzies, Scores and Medals -- people blew right past, got low scores and continuedgate at end people very frustrated no progress wo mastery but much frustrationgate at each point people saw connection but issuesgate at each point and Orange Just-in-Time help
- Exploding no stabilize -- people careening madly much frustration and despairunlimited stabilize -- no frustration, no learning, and no sense of cheatinglimited stabilize –
- Initialassumption was that medals and score would allow advanced players to engage in challenge. Many didn’t care about medal or score though in particular and 1st plan had fixed path and medals to encourage replay and challenge for more advanced playwanted to see how fast they could get to the end. They then went back and created their own challenges. such as how fast could they travel. Because we were still in the marble blast template, though players needed to floow the same path. Current plan a few simple goals that everyone must complete with multiple opportunities for bonus goals. Open path. Also working to take CAT and HMM tech to adjust difficulty based on previous medals -- if you have all golds, getting another will be harder.
- Initialassumption was that medals and score would allow advanced players to engage in challenge. Many didn’t care about medal or score though in particular and 1st plan had fixed path and medals to encourage replay and challenge for more advanced playwanted to see how fast they could get to the end. They then went back and created their own challenges. such as how fast could they travel. Because we were still in the marble blast template, though players needed to floow the same path. Current plan a few simple goals that everyone must complete with multiple opportunities for bonus goals. Open path. Also working to take CAT and HMM tech to adjust difficulty based on previous medals -- if you have all golds, getting another will be harder.
- WISE = HUB
- America’s Lab Report