Competency-based assessment of academic performance has been gaining momentum as it provides better measurements of knowledge and skills directly related to job performance, professional conduct and judgment, and other soft skills that traditional tests can’t measure. Applying and refining competency-based assessment coupled with innovative instructional methods in nursing, such as the use of sophisticated physical (manikin) and virtual simulations, is critical in assessing the effectiveness of such instructional methods.
This project will use modern data-science tools to address a series of research questions, in order to understand the correlation between traditional tests and student skills and competence when they enter the workforce, and whether simulations coupled with debriefing sessions translate into improvements in skills and in test outcomes. The research team will examine how well a student’s technical skills and soft skills demonstrated in a simulation correlates with traditional test performance and their job performance, how traditional test performance correlates with job performance, whether physical and virtual simulation are equally effective, and whether student performance in simulations can be evaluated by automated processes instead of human evaluators. Furthermore, the research team will determine whether student characteristics influence the correlations and what changes in the simulation can improve effectiveness. This project will directly impact the quality of nursing instruction and provide a set of tools that can be used to improve the way competency is defined, measured, and delivered in other fields.