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

利用自决理论的内在和外在动机建立对分类数据分析、公民科学视频游戏的参与

• 批准号: 2890021
• 金额: --
• 依托单位: Queen Mary University of London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2890021
• 关键词: Building; engagement; classification; data; analysis

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.

诸如Foldit（2008）和Eyewire（2012）之类的游戏公民科学项目依靠志愿者的长期参与。但是，参与者的数字并不令人满意。该项目试图解决在游戏化公民科学项目中缺乏足够的志愿者参与。目的是建立一个基于分类数据分析的引人入胜的公民科学视频游戏，同时策略性地采用自决理论设定的价值观。尽管使用Unity游戏引擎和大型多人在线科学开发的API开发了一个原型，但该项目的目的是通过为在线公民科学界和游戏的移动版本建立基础来扩展现有模型。目的是说明如何通过在线平台攻击玩家的相关性和能力，并通过手机游戏来加强观众，从而扩大了志愿者在公民科学领域的长期参与。