Using serious games to prime safety students for occupational noise exposure risk assessment

Ensuring the safety of our workforce requires substantial training and education of workers. This thesis investigates the potential of priming students with gamification and entertainment techniques in a virtual environment to enhance the training for occupational noise risk assessments. A simulator titled AssessVR, a PC-based application was created to enhance the learning experience of students and safety workers in identifying hazards and assessing risks. This simulator enables students to engage in hazard recognition, develop evaluation strategies, and perform risk assessment and analysis by utilizing the data generated within the simulation. Two studies, a pilot and an enhanced study, were conducted to assess priming learners' effectiveness in enhancing occupational noise risk assessment. The results of the pilot study point to the need for enhancing the gamification of the simulator. Consequently, AssessVR was redesigned as a serious game. In the second study, the gamified AssessVR was used to prime students before they engaged in the risk assessment with a non-gamified, AssessVR. An additional group of students was primed with a non-gamified AssessVR as a benchmark or the assessment of the gamification approach. The results demonstrated that the gamified-based priming significantly improved students' exposure assessments, a critical component of risk assessments. In their assessment, students primed with a gamified approach referenced exposure standards more frequently than those in a traditional utilitarian group. However, not all risk assessment dimensions were improved through gamification.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

thesis

Discipline thesis:degree_discipline

Occupational safety

Department dc:contributor.department

Department of Computer Science

Grantor

Iowa State University

Year dc:date.issued

2023

Author dc:creator

• Yrjo, Tavion

Advisors dc:contributor.advisor

• Keren, Nir
• Stone, Richard
• Simpson, Stephen

Language dc:language.iso

en

DOI

https://doi.org/10.31274/td-20240329-856

OAI identifier oai:identifier

oai:dr.lib.iastate.edu:20.500.12876/GvqXjj7w