Early identification of struggling learners: using prematriculation and early academic performance data

Layne D. Bennion; Dario Torre; Steven J. Durning; David Mears; Deanna Schreiber-Gregory; Jessica T. Servey; David F. Cruess; Michelle Yoon; Ting Dong

doi:10.1007/s40037-019-00539-2

Early identification of struggling learners: using prematriculation and early academic performance data

Layne D. Bennion, Dario Torre, Steven J. Durning, David Mears, Deanna Schreiber-Gregory, Jessica T. Servey, David F. Cruess, Michelle Yoon, Ting Dong

Research output: Contribution to journal › Article › peer-review

2 Citations (Scopus)

4 Downloads (Pure)

Abstract

Introduction: A perennial difficultly for remediation programmes in medical school is early identification of struggling learners so that resources and assistance can be applied as quickly as is practical. Our study investigated if early academic performance has predictive validity above and beyond pre-matriculation variables. Methods: Using three cohorts of medical students, we used logistic regression modelling and negative binomial regression modelling to assess the strength of the relationships between measures of early academic performance and outcomes—later referral to the academic review and performance committee and total module score. Results: We found performance on National Board of Medical Examiners (NBME) exams at approximately 5 months into the pre-clerkship curriculum was predictive of any referral as well as the total number of referrals to an academic review and performance committee during medical school (MS)1, MS2, MS3 and/or MS4 years. Discussion: NBME exams early in the curriculum may be an additional tool for early identification of struggling learners.

Original language	English
Pages (from-to)	298-304
Number of pages	7
Journal	Perspectives on Medical Education
Volume	8
Issue number	5
DOIs	https://doi.org/10.1007/s40037-019-00539-2
Publication status	Published - 1 Oct 2019
Externally published	Yes

Bibliographical note

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Keywords

Identification of struggling learners
Medical education
Remediation

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abstract = "Introduction: A perennial difficultly for remediation programmes in medical school is early identification of struggling learners so that resources and assistance can be applied as quickly as is practical. Our study investigated if early academic performance has predictive validity above and beyond pre-matriculation variables. Methods: Using three cohorts of medical students, we used logistic regression modelling and negative binomial regression modelling to assess the strength of the relationships between measures of early academic performance and outcomes—later referral to the academic review and performance committee and total module score. Results: We found performance on National Board of Medical Examiners (NBME) exams at approximately 5 months into the pre-clerkship curriculum was predictive of any referral as well as the total number of referrals to an academic review and performance committee during medical school (MS)1, MS2, MS3 and/or MS4 years. Discussion: NBME exams early in the curriculum may be an additional tool for early identification of struggling learners.",

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Early identification of struggling learners : using prematriculation and early academic performance data. / Bennion, Layne D.; Torre, Dario; Durning, Steven J.; Mears, David; Schreiber-Gregory, Deanna; Servey, Jessica T.; Cruess, David F.; Yoon, Michelle; Dong, Ting.

In: Perspectives on Medical Education, Vol. 8, No. 5, 01.10.2019, p. 298-304.

Research output: Contribution to journal › Article › peer-review

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AU - Servey, Jessica T.

AU - Cruess, David F.

AU - Yoon, Michelle

AU - Dong, Ting

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Early identification of struggling learners: using prematriculation and early academic performance data

Abstract

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Keywords

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