Attitudes of health care students about computer-aided neuroanatomy instruction.

This study examined students' attitudes toward computeraided instruction (CAI), specifically neuroanatomy learning modules, to assess which components were primary in establishing these attitudes and to discuss the implications of these attitudes for successfully incorporating CAI in the preparation of health care providers. Seventy-seven master's degree, entry-level, health care professional students matriculated in an introductory neuroanatomy course volunteered as subjects for this study. Students independently reviewed the modules as supplements to lecture and completed a survey to evaluate teaching effectiveness. Responses to survey statements were compared across the learning modules to determine if students viewed the modules differently. Responses to individual survey statements were averaged to measure the strength of agreement or disagreement with the statement. Responses to open-ended questions were theme coded, and frequencies and percentages were calculated for each. Students saw no differences between the learning modules. Students perceived the learning modules as valuable; they enjoyed using the modules but did not prefer CAI over traditional lecture format. The modules were useful in learning or reinforcing neuroanatomical concepts and improving clinical problem-solving skills. Students reported that the visual representation of the neuroanatomical systems, computer animation, ability to control the use of the modules, and navigational fidelity were key factors in determining attitudes. The computer-based learning modules examined in this study were effective as adjuncts to lecture in helping entry-level health care students learn and make clinical applications of neuroanatomy information. J Allied Health 2009; 38:189-195. THE TASK FOR STUDENTS preparing to work in neurologic rehabilitation is to understand neuroscience at both the anatomic and behavioral levels. At the anatomic level, students learn the structure and function of the various neurologic systems. At the behavioral level, students learn how motor, sensory, and cognitive abilities are controlled under nomnal and pathologic conditions. For example, at the anatomic level, students learn the structure and function of the motor control system, including the fact that the system is crossed. At the behavioral level, they learn that a stroke involving the motor centers results in hemiplegia on the side opposite the lesion. The task for educators preparing students for clinical practice is how best to help students acquire and integrate information from the anatomic and behavioral levels and bring that information to bear on clinical problems. Computer-aided instruction (CAI) provides a powerful representational tool to portray neuroscience information in a clinical educational environment. Increasingly, CAI resources are being incorporated into existing curricula for educating health care providers.1 Two theories that help account for the use and effectiveness of CAI are the self-efficacy theory (SET) and the Technology Acceptance Model (TAM). SET is a component of social cognitive theory2,3 and states that "human behavior is primarily determined by a person's belief that his or her actions will result in positive outcomes and that he or she is capable of accomplishing the activity."4 During the past two decades, across several academic disciplines, self-efficacy has emerged as a robust predictor of students' motivation and learning.5-1 ' Self-efficacy has accurately predicted students' activity choices, effort, persistence, and emotional reactions and mediates students' academic achievement.12-17 Clinical self-efficacy, the student's perception of whether the information makes him or her better able to treat a patient, is a primary determinant in CAI use and effectiveness in the education of health care providers.18,19 The TAM purports that attitudes toward technology influence technology use when it is presented.20 According to the TAM, two main components of the technology, perceived usefulness and perceived ease of use, are effective predictors of computer use. …