Evaluating Singleplayer and Multiplayer in Human Computation Games
This work addresses the incomplete knowledge about designing effective human computation games for both player appeal and problem-solving, though it is incremental in nature.
The researchers investigated how social conditions (singleplayer vs. multiplayer) and scoring mechanics (collaborative vs. competitive) affect player experience, task accuracy, and completion rates in a human computation game based on Super Mario Bros, finding that these design choices significantly impact game effectiveness.
Human computation games (HCGs) can provide novel solutions to intractable computational problems, help enable scientific breakthroughs, and provide datasets for artificial intelligence. However, our knowledge about how to design and deploy HCGs that appeal to players and solve problems effectively is incomplete. We present an investigatory HCG based on Super Mario Bros. We used this game in a human subjects study to investigate how different social conditions---singleplayer and multiplayer---and scoring mechanics---collaborative and competitive---affect players' subjective experiences, accuracy at the task, and the completion rate. In doing so, we demonstrate a novel design approach for HCGs, and discuss the benefits and tradeoffs of these mechanics in HCG design.