Refining StreamBED Through Expert Interviews, Design Feedback, and a Low Fidelity Prototype
This work addresses training issues for citizen scientists in stream monitoring, but it is incremental as it refines an existing VR system without major breakthroughs.
The study tackled the challenge of novice biologists struggling with qualitative stream assessment protocols in VR training by redesigning StreamBED based on expert feedback and design methods, resulting in recommendations to incorporate personal narratives, realism, and social dynamics for improved learning.
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.