A Multicentre Analysis of Approaches to Learning and Student Experiences of Learning Anatomy Online

Introduction: The approaches to learning students adopt when learning anatomy online could yield important lessons for educators. Dissection room teaching can encourage students to adopt a deep approach to learning anatomy. It was therefore hypothesized that the proportion of students adopting a deep approach to learning would be lower in a population learning anatomy online. This research aims to investigate the experiences of students learning anatomy online during the COVID-19 pandemic and the approaches to learning they adopted.

Methods: A survey was distributed to medical students at 7 universities across the UK and Ireland. The survey included two previously validated questionnaires: Approaches and Study Skills Inventory for Students and Anatomy Learning Experience Questionnaire.

Results: The analysis included 224 unique student responses. Students' approach to learning mirrored reports from previous studies conducted during face-to-face tuition with 44.3% adopting deep, 40.7% strategic, 11.4% surface, and 3.6% combined learning approaches. The university (p = 0.019) and changes to formative (p = 0.016) and summative (p = 0.009) assessments significantly impacted approach to learning. Students reported that online resources were effective but highlighted the need for clearer guidance on how to find and use them successfully.

Conclusion: It is important to highlight that students value in-person opportunities to learn from human cadaveric material and hence dissection room sessions should remain at the forefront of anatomical education. It is recommended that future online and/or blended provisions of anatomy teaching include varied resources that maximize engagement with media featuring cadaveric specimens.

Supplementary information: The online version contains supplementary material available at 10.1007/s40670-022-01633-7.

Keywords: Anatomy education; Approach to learning; Blended learning; COVID-19; Distance learning; Remote learning.