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

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.