Using neurocognitive profiles and common personality traits to predict academic success in written examination and growth trajectory in undergraduate medical education

Introduction: A central challenge in academic remediation is identifying struggling learners prior to a major academic failure. Standardized assessments may fail to capture contributions to score more aligned with humanistic theories of learning such as motivation, psychological needs, and response to prior failure. Thus, the current study sought to understand how neurocognitive and social/personality data predict academic success and academic trajectory during the pre-clerkship period of medical school. Specifically, this study utilized non-academic individual data along with cognitive assessments to help identify or predict at risk learners. Method: Sixty learners at an allopathic medical school were recruited during pre-clerkship and completed an abbreviated neurocognitive battery (problem solving, memory, attention, and processing speed) and measures of normative personality traits, grit, and test anxiety. Scores were analyzed in comparison to both their exam performance as well as their exam performance trajectory over time. Results: A stepwise multiple linear regression model was found to be significant, F (13, 46) = 1.98, p <.05 with cognitive, personality, and social measures as predictors of exam average. Working memory was the only significant individual predictor in the model, β = 0.30, p =.03. Learners whose trajectory of academic performance on written exams declined compared to those with improving trajectories had higher openness to experience as measured by the NEO-FFI-3^™, t(58) = 2.06, p =.02. Discussion: Results suggest that identification of at-risk learners can be enhanced by including additional non-academic data, e.g. lower openness to experience. However, at-risk learner performance is difficult to identify solely on individual factors; thus, interventions should be sensitive to situational variation and systemic in nature.