Literature DB >> 28098483

Academic Performance on First-Year Medical School Exams: How Well Does It Predict Later Performance on Knowledge-Based and Clinical Assessments?

Edward Krupat1, Stephen R Pelletier1, Jules L Dienstag2.   

Abstract

Number of appearances in the bottom quartile of 1st-year medical school exams were used to represent the extent to which students were having academic difficulties. Medical educators have long expressed a desire to have indicators of medical student performance that have strong predictive validity. Predictors traditionally used fell into 4 general categories: demographic (e.g., gender), other background factors (e.g., college major), performance/aptitude (e.g., medical college admission test scores), and noncognitive factors (e.g., curiosity). These factors, however, have an inconsistent record of predicting student performance. In comparison to traditional predictive factors, we sought to determine the extent to which academic performance in the 1st-year of medical school, as measured by examination performance in the bottom quartile of the class in 7 required courses, predicted later performance on a variety of assessments, both knowledge based (e.g., United States Medical Licensing Examination Step 1 and Step IICK) and clinical skills based (e.g., clerkship grades and objective structured clinical exam performance). Of all predictors measured, number of appearances in the bottom quartile in Year 1 was the most strongly related to performance in knowledge-based assessments, as well as clinically related outcomes, and, for each outcome, bottom-quartile performance accounted for additional variance beyond that of the traditional predictors. Low academic performance in the 1st year of medical school is a meaningful risk factor with both predictive validity and predictive utility for low performance later in medical school. The question remains as to how we can incorporate this indicator into a system of formative assessment that effectively addresses the challenges of medical students once they have been identified.

Keywords:  Performance predictors; exam performance; student assessment

Mesh:

Year:  2017        PMID: 28098483     DOI: 10.1080/10401334.2016.1259109

Source DB:  PubMed          Journal:  Teach Learn Med        ISSN: 1040-1334            Impact factor:   2.414


  3 in total

1.  Can we predict failure in licensure exams from medical students' undergraduate academic performance?

Authors:  Janeve Desy; Sylvain Coderre; Pamela Veale; Kevin Busche; Wayne Woloschuk; Kevin McLaughlin
Journal:  Can Med Educ J       Date:  2021-12-29

2.  Predicting students' performance in English and Mathematics using data mining techniques.

Authors:  Muhammad Haziq Bin Roslan; Chwen Jen Chen
Journal:  Educ Inf Technol (Dordr)       Date:  2022-07-29

3.  'Early identification of struggling pre-clerkship learners using formative clinical skills OSCEs: an assessment for learning program.'

Authors:  Ilene Rosenberg; Listy Thomas; Gabbriel Ceccolini; Richard Feinn
Journal:  Med Educ Online       Date:  2022-12
  3 in total

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