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ADN student approaches to learning in relation to institutional NCLEX-RN pass rates

Posted on:2014-07-05Degree:Ph.DType:Dissertation
University:Capella UniversityCandidate:Yearick, Norma GFull Text:PDF
GTID:1457390005494921Subject:Education
Abstract/Summary:
Prompted by the national nursing shortage and less-than-optimal institutional National Council Licensure Examination for Registered Nurses (NCLEX-RN) pass rates, this correlational comparative study examined relationships between Associate Degree Nursing (ADN) student factors (age, number of nursing courses completed, nursing course failure, previous college degrees), ADN students' approaches to learning (deep, strategic, and surface), and institutional NCLEX-RN pass rates. Student Approaches to Learning Theory was used as the foundation for identifying the concepts and relationships. A purposeful sample of 68 ADN students from three education programs with NCLEX-RN pass rates, including 94.32%, 91.99%, and 78.38%, completed an online survey with demographic questions and items from the Approaches to Study Skills Inventory for Students (ASSIST). Cronbach's alpha levels established reliability of the ASSIST. Correlational statistics found no significant relationships between student factors and learning approaches or between the learning approaches and NCLEX-RN pass rates. A significant relationship was discovered between achieving, a subcategory for strategic approaches, and NCLEX-RN pass rates. Students from the program with the lowest NCLEX-RN pass rate had the highest scores in the achieving subcategory and in deep and strategic approaches. Students who have failed nursing courses revealed higher levels of deep learning approaches. Although no relationships were discovered between student factors, learning approaches, and institutional NCLEX-RN pass rates, further consideration of student approaches to learning theory in relation to ADN students is recommended due to the unexpected findings.
Keywords/Search Tags:NCLEX-RN pass rates, Approaches, ADN, Nursing
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