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PubMed · 9683136

Learning style influences student examination performance.

Abstract

BACKGROUND: The Kolb Learning Style Inventory (LSI) measures preference for each of four learning orientations: abstract conceptualization, concrete experience, active experimentation, and reflective observation. These orientations define four learning styles: convergence, divergence, assimilation, and accommodation. METHODS: To determine if learning style correlates with objective multiple-choice and clinical measures of performance, the learning styles of third-year medical students (n = 227) were evaluated using the LSI. Performance was assessed using the United States Medical Licensing Examination step 1 (USMLE 1), the National Board of Medical Examiners (NBME) multiple-choice surgical subject examination (MCQ), and NBME computer-based case simulations (CBX). RESULTS: The data showed a significant (P < or = 0.05) relationship between learning style and performance on the USMLE 1. There was a significant (P < or = 0.05) and direct correlation between an abstract orientation and performance on the USMLE 1 (r = 0.33) and MCQ (r = 0.20). There was no relationship between learning style and clinical performance measured using the CBX. CONCLUSIONS: These data demonstrate that performance on objective measures of academic achievement is influenced by learning style, while application of that knowledge in the management of clinical situations may require additional skills beyond those measured.

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BibTeXRIS

T G Lynch, N N Woelfl, D J Steele, C S Hanssen. 1998. Learning style influences student examination performance.. https://doi.org/10.1016/s0002-9610(98)00107-x

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Conditional reliability of admissions interview ratings: extreme ratings are the most informative.

CONTEXT: Admissions interviews are unreliable and have poor predictive validity, yet are the sole measures of non-cognitive skills used by most medical school admissions departments. The low reliability may be due in part to variation in conditional reliability across the rating scale. OBJECTIVES: To describe an empirically derived estimate of conditional reliability and use it to improve the predictive validity of interview ratings. METHODS: A set of medical school interview ratings was compared to a Monte Carlo simulated set to estimate conditional reliability controlling for range restriction, response scale bias and other artefacts. This estimate was used as a weighting function to improve the predictive validity of a second set of interview ratings for predicting non-cognitive measures (USMLE Step II residuals from Step I scores). RESULTS: Compared with the simulated set, both observed sets showed more reliability at low and high rating levels than at moderate levels. Raw interview scores did not predict USMLE Step II scores after controlling for Step I performance (additional r2 = 0.001, not significant). Weighting interview ratings by estimated conditional reliability improved predictive validity (additional r2 = 0.121, P < 0.01). CONCLUSIONS: Conditional reliability is important for understanding the psychometric properties of subjective rating scales. Weighting these measures during the admissions process would improve admissions decisions.

Education, Medical, Undergraduate↗