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Yet Another Magic Bullet A Tool for Assessing and Evaluating Learning Potential In Games
What? Model for evaluating and assessing games Subjective tool All learning in a game can be classified in one (or more) categories Learning NEED NOT = education Relative proportions are informative Currently developed for single-player games
Things I Can Learn deliberately designed by those who created the game Includes things designers *hope* people will take up Includes game-specific objectives as well as general ones
Things I MUST Learn should be a subset of the first category Required in order to achieve a specific goal or to win Includes strategy
Collateral Learning other things we can learn these are not necessarily designed into the game, although sometimes designers may hope that players choose to take these up
External Learning not  technically  considered part of the normal gameplay Includes social learning and outside communities  Also includes Cheats typically designed into the game for testing purposes often left in the game once it ships deliberate design elements on the part of the designers
Things I DID Learn “ your results may vary” Will often be specific to the goal  if there is more than one way to win if there are multiple goals that can be achieved Not included in analysis models
A Good Game Good balance Nothing I  MUST  learn that is outside of what I  CAN  learn. Allows for learning outside of game and from cheats and community.
A Good Game Things I  MUST  learn < ½ of what I  CAN  learn External learning  not necessary Collateral learning  possible Some Examples
MUST  learn =  CAN  learn Nothing to learn that isn’t part of the ‘goal’ Often edutainment fits in here Lack of  collateral learning  opportunities implies a single-purpose game (or an impoverished one) Some Examples
MUST  learn  ≈  CAN  learn Challenging    for some, frustrating Often requires players to repeat plays and levels many times Some Examples
MUST  learn  ≈   CAN  learn Puzzles Mini-games Some Examples
MUST  learn >  CAN  learn Need  outside help / resources  to get into the game or progress CAN still be good, but this has serious implications for audience and support requirements Very risky in serious games Some Examples
MUST  learn <<  CAN  learn Lacks direction Aimless Toy, not game Some Examples
MUST  learn  too small Not much to hold interest Some Examples That is what the game was meant to be. In reality, the game has no gameplay. A lack of AI means that the opposing truck does not even move from its starting location, so there is really no &quot;race&quot; to begin with. Winning is virtually guaranteed. As well, the game lacks collision detecting which means you can go through any objects like houses, boulders, trees, and bridges that you are required to cross. Besides this, the trucks do not have any top speed, which means you are able to accelerate into infinite, even when going backwards. Traveling off of the edge of the map is possible in the game. Despite the fact that the back of the box and advertising said it would have police chases, absolutely no police cars are actually present within the game. Source:  http://www.mobygames.com/game/big-rigs-over-the-road-racing
Little Game Short form game Some Examples
Drill Game Short form game Bad if not short Some Examples
Short Form Game Can be great if carefully designed Must be designed as <= 5 minute game. Includes many puzzles. Some Examples
MUST  learn includes collateral learning. Can make for great game Tends to worry traditional educators Can be very useful in serious games Games do not always need to be self-contained Some Examples
MUST learn = 0 No direction Even SIMs has some  MUST  learn Game on rails This is a toy Some Examples
No collateral learning. Imbalance between  CAN  &  MUST Some Examples
Re-cap No guarantees A useful lens Can indicate where we need to make changes / additions / deletions Some Examples
Thanks!

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Yet Another Magic Bullet: A Tool for Assessing and Evaluating Learning Potential In Games

  • 1. Yet Another Magic Bullet A Tool for Assessing and Evaluating Learning Potential In Games
  • 2. What? Model for evaluating and assessing games Subjective tool All learning in a game can be classified in one (or more) categories Learning NEED NOT = education Relative proportions are informative Currently developed for single-player games
  • 3. Things I Can Learn deliberately designed by those who created the game Includes things designers *hope* people will take up Includes game-specific objectives as well as general ones
  • 4. Things I MUST Learn should be a subset of the first category Required in order to achieve a specific goal or to win Includes strategy
  • 5. Collateral Learning other things we can learn these are not necessarily designed into the game, although sometimes designers may hope that players choose to take these up
  • 6. External Learning not technically considered part of the normal gameplay Includes social learning and outside communities Also includes Cheats typically designed into the game for testing purposes often left in the game once it ships deliberate design elements on the part of the designers
  • 7. Things I DID Learn “ your results may vary” Will often be specific to the goal if there is more than one way to win if there are multiple goals that can be achieved Not included in analysis models
  • 8. A Good Game Good balance Nothing I MUST learn that is outside of what I CAN learn. Allows for learning outside of game and from cheats and community.
  • 9. A Good Game Things I MUST learn < ½ of what I CAN learn External learning not necessary Collateral learning possible Some Examples
  • 10. MUST learn = CAN learn Nothing to learn that isn’t part of the ‘goal’ Often edutainment fits in here Lack of collateral learning opportunities implies a single-purpose game (or an impoverished one) Some Examples
  • 11. MUST learn ≈ CAN learn Challenging  for some, frustrating Often requires players to repeat plays and levels many times Some Examples
  • 12. MUST learn ≈ CAN learn Puzzles Mini-games Some Examples
  • 13. MUST learn > CAN learn Need outside help / resources to get into the game or progress CAN still be good, but this has serious implications for audience and support requirements Very risky in serious games Some Examples
  • 14. MUST learn << CAN learn Lacks direction Aimless Toy, not game Some Examples
  • 15. MUST learn too small Not much to hold interest Some Examples That is what the game was meant to be. In reality, the game has no gameplay. A lack of AI means that the opposing truck does not even move from its starting location, so there is really no &quot;race&quot; to begin with. Winning is virtually guaranteed. As well, the game lacks collision detecting which means you can go through any objects like houses, boulders, trees, and bridges that you are required to cross. Besides this, the trucks do not have any top speed, which means you are able to accelerate into infinite, even when going backwards. Traveling off of the edge of the map is possible in the game. Despite the fact that the back of the box and advertising said it would have police chases, absolutely no police cars are actually present within the game. Source: http://www.mobygames.com/game/big-rigs-over-the-road-racing
  • 16. Little Game Short form game Some Examples
  • 17. Drill Game Short form game Bad if not short Some Examples
  • 18. Short Form Game Can be great if carefully designed Must be designed as <= 5 minute game. Includes many puzzles. Some Examples
  • 19. MUST learn includes collateral learning. Can make for great game Tends to worry traditional educators Can be very useful in serious games Games do not always need to be self-contained Some Examples
  • 20. MUST learn = 0 No direction Even SIMs has some MUST learn Game on rails This is a toy Some Examples
  • 21. No collateral learning. Imbalance between CAN & MUST Some Examples
  • 22. Re-cap No guarantees A useful lens Can indicate where we need to make changes / additions / deletions Some Examples