Instructional and Motivational Factors Predicting Deep Learning Approaches in Pre-Clinical Medical Students

Pre-clinical medical students predominantly employ surface learning approaches, focusing on memorization and exam preparation rather than developing a deep understanding of medical knowledge. This study employed a correlational design comparing two courses with different instructional approaches using expectancy-value theory to examine how instructional methods and motivational factors predict deep learning approaches among 148 pre-clinical medical students at a College of Medicine in the Southeastern United States. Initial results showed that the three separate constructs in task motivation (expectancy beliefs, values, and cost) did not mediate the relationship between active instructional methods and deep learning strategies. Instead, active learning was a statistically significant predictor of deeper learning strategies (β = 0.25, p = .002). While active learning significantly impacted students' perceptions of cost (β = -0.46, p < .001) and value (β = 0.19, p = .02), the paths from cost and value to deep learning were not significant (cost: β = -0.03, p = .79; value: β = 0.12, p = .16). Thus, while active learning predicted lower perceived cost and increased value, these changes did not predict greater use of deep learning strategies. However, post-hoc analysis revealed that a linear combination of expectancy, value, and cost fully mediated the relationship between active instruction and learning approaches (β = .15, z(143) = 3.48, p = < .001). In this model, the direct effect of active instruction on learning strategies was statistically non-significant (β = .06, z(143) = .83, p = .41). These findings suggest that active instructional methods enhance pre-clinical medical students' task motivation, which in turn increases students' adoption of deep learning approaches. Medical educators can use these findings to design and implement instructional activities and materials that promote student task motivation and a deeper understanding of medical knowledge.