Lessons learned from conducting assessments in radioanatomy courses on learning management systems

Area of Science:

• Medical Education
• Radiology
• Anatomy

Background:

• Radioanatomy, the study of anatomy via medical imaging, is increasingly integrated into medical curricula.
• Medical education is shifting towards blended learning models, incorporating e-assessment platforms like Moodle.
• Current radioanatomy assessments often rely on multiple-choice questions due to e-assessment challenges.

Purpose of the Study:

• To share lessons learned from three academic years of summative e-assessments in radioanatomy.
• To encourage the use of diverse question types beyond multiple-choice in radioanatomy.
• To provide insights for improving e-assessment in radioanatomy and similar medical courses.

Main Methods:

• Consolidated practical experience over three academic years.
• Discussed technical aspects, including image optimization for assessments.
• Implemented a lexicon for standardized answers to facilitate automatic grading.
• Utilized a student survey to gather feedback on assessment methods.

Main Results:

• Standardized answers and image optimization are crucial for effective automatic grading.
• Stack visualizations are supported by students for enhanced image interpretation.
• Collaboration among stakeholders is vital for seamless assessment processes.

Conclusions:

• Diverse question types and standardized answers can improve radioanatomy e-assessments.
• Technical considerations like image optimization are important for computer-based evaluation.
• Effective e-assessment requires collaboration and can benefit other medical subjects.