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Using Virtual Simulation To Increase Deep Learning in Radiography Students
1Assistant professor for California State University, Northridge. She has been a practicing radiologic technologist for more than 17 years, specializing in magnetic resonance imaging for 14 of those 17 years. She teaches medical imaging, pathophysiology, and research courses, specializing in radiographic and advanced imaging pathology. She also is the vice president of the California Society of Radiologic Technologists, teaches venipuncture certificate courses, and is involved with starting the first positron emission tomography-computed tomography programs on the West Coast.
Purpose:
To discuss recent studies that validate the combination of traditional teaching and virtual simulation training in reducing common errors, enhancing students' confidence, improving their performance, and increasing deep learning.
Methods:
Multiple electronic databases were searched for learning environment concepts such as deep- vs surface-learning approaches, online vs face-to-face instruction, and the usefulness of virtual simulation laboratories between 1999 to the present.
Results:
Deep-learning approaches allow students to engage in higher-quality learning (eg, understanding of the discipline and thinking critically) than do surface-learning approaches. Instructors are shifting from traditional face-to-face learning environments to online environments, including virtual simulation. Virtual simulation alone does not guarantee deep learning; instructional design and guidelines determine whether students use deep- or surface-learning approaches.
Discussion:
Most radiologic technology programs currently use a traditional x-ray laboratory to teach students positioning and radiation dose techniques. Virtual simulation offers a harmless and convenient learning environment that permits students to practice techniques without the risks of irradiating patients. Instructors can foster deep learning in virtual simulation laboratory environments by designing the software around particular course outcomes (eg, cognitive and psychomotor skills) and engaging with sound educational strategies and theory.
Conclusion:
By understanding deep learning that is taking place in radiologic science laboratory learning environments, educators will be able to design virtual simulation courses that foster deeper learning.
