Active before passive tasks improve long-term visual learning in difficult-to-classify skin lesions

Abstract

Background: Visual perceptual learning is essential in many domains, including medicine. Prior research has shown that combining active and passive tasks improves learning efficiency. However, previous studies have only implemented passive before active tasks, contradicting findings on productive failure learning designs. Aims: We aimed to replicate and extend earlier results by comparing 1) combined versus uniform active and passive tasks and 2) active before passive versus passive before active tasks in visual learning. The to-be-learned skill was the detection of pigmented skin cancer. Sample: The sample consisted of 161 university students without professional knowledge about skin lesion classification. Methods: We randomly assigned the participants to four groups: 1) active before passive, 2) passive before active, 3) uniform active, and 4) uniform passive tasks. The students completed the learning intervention, an intermediate and three post-tests (immediate, two days delayed and two weeks delayed) online. Results: In line with our hypotheses, we found that learning with combined active and passive tasks led to higher diagnostic accuracy in difficult skin lesion classification tasks in the two weeks delayed post-test than learning with only one of the two task types. Furthermore, we found that active before passive tasks resulted in higher diagnostic accuracy than passive before active tasks. Conclusion: Our findings suggest that initial active tasks improve long-term visual learning outcomes in difficult melanoma detection tasks, but future research needs to confirm this result and explore the underlying learning mechanisms further.