| Graduates from all registered nurse education programs in the United States are required to pass the National Council Licensure Exam (NCLEX) for Registered Nurses in order to legally practice. Passing the NCLEX on the first attempt is of high importance to those taking the exam and to nurse faculty and accrediting bodies as a benchmark for the quality of a nursing program. The phenomenon of NCLEX failure while successfully completing an undergraduate degree has been present for decades and is related to a complex set of academic and non-academic factors. The NCLEX measures baseline competency in decision making through the application, analysis, evaluation, and synthesis of nursing knowledge. Therefore, the cognitive abilities of a student are an important factor in passing the NCLEX. Cognitive abilities evidenced through self-regulated learning (SRL) behaviors have been identified as contributing to academic achievement. The purpose of the study was to investigate the prediction of NCLEX Readiness from the strength of metacognitive self-regulated learning behaviors in senior baccalaureate nursing students. Using the Metacognitive Awareness Inventory (MAI) and preexisting academic and demographic data, a non-experimental, correlational study was conducted. The results demonstrated that while metacognitive behaviors had a positive relationship to NCLEX readiness, the relationship was not statistically significant (p = 0.314), and thus not predictive of NCLEX readiness in the sample studied. The correlation between NCLEX Readiness and ACT was relatively large (r= 0.247) and statistically significant (p = 0.019). When considering all items in the MAI, only 11 of 52 items or 21% achieved a level of ≥ 80%, which does not indicate a strong use of metacognitive behaviors in the sample studied. The current study represents an initial investigation of the relationship of metacognitive self-regulated learning behaviors and NCLEX readiness. Future research should focus on exploring these relationships in greater detail, with larger sample sizes, and additional independent variables in the regression analysis. |
