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PubMed · 9488852

Effectiveness of problems used in problem-based learning.

Abstract

Where problem-based learning (PBL) is the main method used in medical curricula, the literature suggests that it is crucial that the problems used are effective in facilitating students to identify relevant learning issues. These learning issues guide the students' studying. The present investigation explores the extent to which students identify relevant issues following exposure to prepared paper problems. In the preparatory year, in an Introduction to Medicine module, four groups of students were exposed to six themes (Health Care System, Environment and Health, Alternative and Islamic Medicine, Chronic Illness, Infectious Diseases, and Prevention and Health Promotion). Each group had two facilitators per theme. Having discussed the prepared problems, the students identified learning issues which were collected for the purpose of the study. Two content experts, using a Likert scale, analysed learning issues for their concordance to staff objectives per theme. Kappa coefficients were computed for the six PBL themes in order to assess inter-rater agreement. Learning issues identified as having no relationship to theme objectives were further analysed for their relevance to theme objectives. No objective was totally omitted by any student group. There was a 100% concordance of objectives to learning issues demonstrated over four themes. The relationship of learning issues to theme objectives ranged from 55-85% in the theme on health care system, and 73-94% in the theme on environment and health. Irrelevant learning issues were identified in the first two PBL themes addressed. Kappa coefficients over the six PBL themes varied from 0.49 to 0.82.

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BibTeXRIS

D J Mpofu, M Das, J C Murdoch, J H Lanphear. 1997. Effectiveness of problems used in problem-based learning.. https://doi.org/10.1046/j.1365-2923.1997.00672.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↗