Teaching neuroanatomy using computer-aided learning: What makes for successful outcomes?

Area of Science:

• Medical Education
• Neuroscience Education
• Educational Technology

Background:

• Computer-aided learning (CAL) is widely used in medical education.
• Neuroanatomy requires complex 3D visualization, making it suitable for CAL.
• Evaluating CAL effectiveness using learning approach concepts is crucial for optimizing medical training.

Purpose of the Study:

• To assess the relationship between students' learning approach and their engagement with a neuroanatomy CAL course.
• To determine if a deep learning approach correlates with academic performance in neuroanatomy.
• To identify factors influencing deep learning engagement in CAL for medical students.

Main Methods:

• Administered learning approach scales and engagement questionnaires to 869 medical students.
• Collected neuroanatomy assessment scores and university examination results.
• Analyzed correlations between learning approach, CAL engagement, and academic performance.

Main Results:

• Students reported lower deep approach use for CAL compared to traditional methods (p < 0.001).
• Deep approach for CAL positively correlated with neuroanatomy assessment performance (r = 0.12, p < 0.001).
• Time spent, enjoyment, videos watched, and quizzes completed were linked to a deep approach.

Conclusions:

• A deep learning approach, even if less utilized in CAL, is beneficial for neuroanatomy performance.
• Enhancing CAL enjoyment and deep learning may be achieved by integrating more clinical content.
• CAL can be an effective tool in medical education when aligned with deep learning principles.