Building engagement in a classification data-analysis, citizen science video game using Self-Determination Theory's intrinsic and extrinsic motivator

游戏化的公民科学项目，如Foldit(2008)和EyeWire(2012)，都依赖于志愿者的长期参与。然而，参与人数一直不能令人满意。这个项目试图解决缺乏足够的志愿者参与游戏化的公民科学项目的问题。其目的是在分类数据分析的基础上建立一个引人入胜的公民科学视频游戏，同时战略性地利用自决理论设定的价值观。虽然已经使用统一游戏引擎和大规模多人在线科学开发的API开发了一个原型，但这个项目的目的是通过建立一个在线公民科学社区和移动版本的游戏的基础来扩展现有的模型。其目标是展示如何通过在线平台挖掘玩家的关联感和能力，并通过手机游戏增强受众，以扩大志愿者对公民科学的长期参与。

Gamified citizen science projects such as Foldit (2008) and EyeWire (2012) have relied upon prolonged engagement from volunteers. However, participant numbers have been unsatisfactory. This project attempts to address the lack of sufficient volunteer engagement in gamified citizen science projects. The aim is to build an engaging citizen science video game based in classification data-analysis while strategically employing the values set by Self-Determination Theory. Whilst a prototype has been developed using the Unity Game Engine and an API developed by Massive Multiplayer Online Science, the intent of this project is to expand upon the existing model by building the base for an online citizen science community and a mobile version of the game. The goal is to demonstrate how tapping into players' sense of relatedness and competence through an online platform and bolstering the audience through a mobile game can serve to expand prolonged engagement by volunteers in citizen science.