Refining StreamBED Through Expert Interviews, Design Feedback, and a Low Fidelity Prototype

TL;DR

This paper discusses the redesign of StreamBED, a VR training tool for citizen scientists, using expert feedback and low fidelity prototypes to improve qualitative stream assessment training.

Contribution

It introduces a research through design approach incorporating expert insights and prototype feedback to enhance VR training for environmental monitoring.

Findings

Training should facilitate personal narratives

Maximize realism in virtual environments

Use social dynamics to scaffold learning

Abstract

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.