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.

Foldit (2008) 和 EyeWire (2012) 等游戏化公民科学项目依赖于志愿者的长期参与。然而，参与人数却不尽如人意。该项目试图解决游戏化公民科学项目中志愿者参与不足的问题。其目的是构建一款基于分类数据分析的引人入胜的公民科学视频游戏，同时战略性地运用自决理论设定的价值观。虽然原型是使用 Unity 游戏引擎和大型多人在线科学开发的 API 开发的，但该项目的目的是通过为在线公民科学社区和游戏的移动版本建立基础来扩展现有模型。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，并通过手机游戏吸引观众，从而扩大志愿者对公民科学的长期参与。