TECHNOLOGY-ENHANCED LEARNING IN THE EDUCATION OF HEALTHCARE PROFESSIONALS
In the past fifteen years, there have been numerous calls for reform in the education of healthcare professionals. Many new technologies are now ubiquitously distributed across the healthcare education landscape as schools colleges and universities re-evaluate their teaching and learning paradigm. Frequently however, too little attention is paid to the evaluation of these e-learning innovations prior to their widespread adoption. Given that the adoption of any technology-enhanced, e-learning initiative is a critical investment decision with significant implications for institutional policy and practice, careful consideration and planning is needed prior to its implementation. Far too often, the implementation of these technological initiatives fails to consider the learner’s motives, needs, and priorities and the learner’s perspective is often neglected as new technology-enhanced teaching practices are adopted. In this scenario, student acceptance of the new technology represents a prominent barrier to learning and faculty willing to innovate with this disruptive technology is often frustrated in the absence of an educational impact.
In this pilot study we evaluated the students’ intention to use e-learning content based on their limited exposure to technology-enhanced content using a modified technology acceptance model (TAM). A total of 222 students; 45.5% men and 68.9% underrepresented minority students were enrolled in a hybrid biochemistry course and exposed to e-learning case studies as a supplement to the face-to-face lectures. At the end of the semester, students completed the modified TAM survey. A multiple linear regression analysis was conducted to predict the students’ intention to use the e-learning content. The criterion variable was the score on the intention to use scale from the TAM. The full set of 13 potential predictors included gender, ethnicity, educational background, success in the current program and perceptions of educational technology; however, a condition index of 70 indicated severe multicollinearity among the predictors. Consequently, predictors were eliminated one at a time until all remaining predictors were statistically significant.
Results and Discussion:
Subjective norm, perceived usefulness and student attitude toward technology were all identified as significant predictors of the student’s behavioral intention to use e-learning content, with student attitude being the greatest predictor of intention to use. Interestingly, student educational backgrounds as indicated by their undergraduate GPA and Medical College Admission Test (MCAT) scores were not significant predictors of their intention to the use e-learning content. In addition, student gender and ethnicity was not a significant predictor of their intention to use the e-learning content. Student responses to all five constructs measured using the modified TAM; perceived usefulness, perceived ease of use, subjective norm, behavioral intention to use and attitude toward technology, were generally positive.
The current study has identified subjective norm, perceived usefulness and student attitude toward technology as significant predictors of the student’s behavioral intention to use e-learning content. Only by identifying these predictors and removing barriers to acceptance in advance of e-learning development and dissemination can faculty provide a cost-effective technology-enhanced learning experience.