Date of Award

Fall 10-17-2016

Degree Type

Dissertation

Degree Name

PhD Higher Education Leadership, Management, Policy

Department

Education Leadership, Management and Policy

Advisor

Robert Kelchen, Ph.D

Committee Member

Rong Chen, Ph.D

Committee Member

Kristi Stinson, Ph.D

Keywords

NCLEX-RN, Remediation, HESI Exit Exam, General Systems Theory, Online remediation, Standardized tests

Abstract

Nursing schools are operating at full capacity in order to address an impending shortage of registered nurses that may exceed 500,000 by the year 2025. This pressure on scarce resources elevates the importance of NCLEX-RN preparedness for nursing faculty, nursing students, and the public at large. Additionally, the ability to successfully prepare students to sit for the NCLEX-RN exam can affect the reputation of nursing programs throughout the United States. Nursing schools frequently utilize commercially prepared standardized exams to assess student readiness and identify students in need of remediation. The HESI E2 Exit Exam distributed by Elsevier is one such exam. Built into this exam is a student-centered online remediation tool that allows students to customize their study based on exam results. In response to low NCLEX-RN pass rates, a BSN program in the northeastern United States developed a remediation policy requiring students to complete a prescribed number of remediation hours based on their earned score. General systems theory was the framework that guided this analytical policy analysis. Once a policy is created as a result of a systematic assessment of a problem, it is necessary to evaluate the policy for effectiveness. This ex post facto analysis addresses a gap in the literature of high quality quantitative remediation policies that are reproducible throughout multiple programs. Using multiple regression this study explored the relationship between utilization of the Elsevier online remediation resource and scores on the HESI V2 Exit Exam for senior-level nursing students. Variables explored were GPA, HESI V1 scores, gender, cohort (traditional or second degree), semester (spring, summer, or fall), and hours of remediation. GPA significantly predicted 15% to 18% of the variance in scores on the HESI V2 exam. When additional variables are entered into the model, the predictive value of GPA was reduced to 3% to 9%. HESI Version 1 significantly predicted 3% to 18% of the variance in scores on the HESI V2 while controlling for GPA. Completion of online remediation hours did not significantly contribute to scores on the HESI V2 Exit Exam for senior-level nursing students in this northeastern BSN program.

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