通过专家访谈、设计反馈和低保真原型改进StreamBED
Refining StreamBED Through Expert Interviews, Design Feedback, and a Low Fidelity Prototype
摘要：
StreamBED是一个嵌入式VR培训，让公民科学家进行定性流评估。早期的研究结果获得了积极的反馈培训定性评估使用不同的流空间的虚拟表示，但提出了特定领域的挑战;新手生物学家有麻烦解释定性协议，并需要实质性的指导，寻找和解释环境的线索。为了解决这些问题，在重新设计，这项工作使用研究通过设计（RTD）的方法来考虑专家流生物学家，第一手流监测经验，与教育和游戏设计师的讨论，并从低保真度原型的反馈。定性研究结果发现，培训应促进个人的叙述，最大限度地提高现实主义，并应使用社会动态支架学习。
英文摘要：
StreamBED is an embodied VR training for citizen scientists to make qualitative stream assessments. Early findings garnered positive feedback about training qualitative assessment using a virtual representation of different stream spaces, but presented field-specific challenges; novice biologists had trouble interpreting qualitative protocols, and needed substantive guidance to look for and interpret environment cues. In order to address these issues in the redesign, this work uses research through design (RTD) methods to consider feedback from expert stream biologists, firsthand stream monitoring experience, discussions with education and game designers, and feedback from a low fidelity prototype. The qualitative findings found that training should facilitate personal narratives, maximize realism, and should use social dynamics to scaffold learning.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
