1. Robin Young, MSN, MSM, RN-BC, CNE
Lisa Gonzalez, MSN, RN, CNE, CCRN-K
Sara Cano, PhD, RN, MSN Ed, BSN
Sheila Levings, MSN, BA, RN, Retention Coordinator
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2. Objectives
By the end of the presentation, the attendee will:
Recognize the need and importance of identifying at-risk nursing students beyond
their first semester to ensure academic and NCLEX success
Understand the limitations of the current success tool and the need for revised
and improved process
Discuss how an updated tool improves the remediation process conducted by the
faculty and the program’s retention coordinator
Learn how to utilize the student success tool
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3. History of Student Success Scale
Purpose of Development
Objective/Subjective Criteria Used
Tool Implementation Process
Need for Retention Coordinator
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6. Literature Review
Affective Predictors:
Confidence, self-control, emotional intelligence, and resilience had a positive
impact on success (Mthimunye & Daniels, 2019; Olsen, 2017)
Students with on-time progression in the nursing program had the highest overall
level of emotional intelligence (Jones-Schenk & Harper, 2014)
Psychosocial risk factors include: loss of confidence and failure to seek help
(Freeman & Anita, 2017)
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7. Literature Review
Environmental Predictors:
Working greater than >16 hours/week correlated with less likely completion of
the nursing program (Mthimunye & Daniels, 2019)
Financial status, family financial support, family emotional support, family
responsibilities, childcare arrangements, employment hours, transportation, living
arrangements greatly influence nursing student retention (Chavan, et. al, 2019 ;
Jeffreys, 2015)
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8. Literature Review
Academic Predictors:
Academic performance in year one predicted academic performance in year two
(Mthimunye & Daniels, 2019)
Higher scores in the science TEAS category is a statistically significant predictor of
success in nursing school followed by reading, written/verbal, and mathematics,
respectively (Wolkowitz & Kelley, 2010)
Overall course grade, failing scores on one or more exam, and a grade of C on
more than 2 assignments (Freeman & Anita, 2017)
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9. Analysis of Objective Data:
1st time NCLEX Pass Rates
Statistical analysis was used to explore outcomes with academic factors hypothesized
to impact NCLEX-RN First Attempt success
TEAS Scores and grades in prerequisite sciences
Grades in nursing courses
Age of prerequisite course completion before nursing courses
No strong correlation found
Association suggested risk factors play a role
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10. Analysis of Subjective Data:
Student-identified Risk Factors
• One-time student survey of general factors linked to risk for academic
failure
• Paper form poorly understood & not completed consistently
• Information manually input to spreadsheet
• Not linked to validated data (Registrar)
• Form modification needed to capture relevant risk
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11. New Form: Part 1
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Developed by CSM Nursing Taskforce 2020. Pending Approval
12. New Form: Part 2
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Developed by CSM Nursing Taskforce 2020. Pending Approval
13. Innovation:
Efficient blending of Objective & Subjective Information
Student data from College-wide information management system
(CROA) exported as spreadsheet
Self-report data by individual students (completion of form) exported
as spreadsheet
Combined spreadsheet facilitates analysis
More rapid identification of risk factors
Quick reporting to Faculty
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14. References
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Chavan, S., Jose, J., D’souza, J., Jovita, M., & Ashly, A. (2019). An explorative study on factors affecting nursing student’s academic
failure. Manipal Journal of Nursing and Health Sciences, 5 (1), 27-31.
Connelly, L., Kathol, L., Peterson, V., Truksa, M., & Stover, A. (2019). The academic coach: A program for nursing student success.
Journal of Nursing Education, 58 (11), 661-664.
Czekanski, K. Mingo, S., & Piper, L. (2018). Coaching to NCLEX-RN success: A postgraduation intervention to improve first-time pass
rates. Journal of Nursing Education, 57(9), 561-565.
Freeman, J., & All, A. (2017). Academic support utilized for nursing students at risk of academic failure: A review of literature.
Nursing Education Perspectives, 38 (2), 69-74.
Hsiu-Chin, C. & Bennett, S. (2016). Decision-tree analysis for predicting first-time pass/fail rates for the NCLEX-RN in associate
degree nursing students. Journal of Nursing Education, 55(8), 454-457.
Jeffreys, M. R. (2015). Jeffrey’s nursing universal retention and success model: Overview and action ideas for optimizing outcomes
A-Z. Nurse Education Today, 35, 425-431.
Jones-Schenk, J. & Harper, M. G. (2014). Emotional intelligence: An admission criterion alternative to cumulative grade point
averages for prelicensure students. Nurse Education Today, 14, 413-420.
15. References (cont.)
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Mthimunye, K. & Daniels, F. M., (2019). Predictors of academic performance, success and retention amongst undergraduate
nursing students: A systematic review. South African Journal of Higher Education. 33(1), 200-220.
Olsen, J. M. (2017). Integrative review of admission factors related to associate degree nursing program success. Journal of Nursing
Education, 56 (2), 85-93.
Widwell, J., Sanner-Stiehr, E., Allen, K., Records, K., Hsueh, K., Conceptual Model for Predicting Academic Success in Prelicensure
Nursing Programs Through Expanded Cognitive Aptitude Assessment, Nurse Educator, 44 (6), 330-334.
Wolkowitz, A. A. & Kelley, J. A. (2010). Academic predictors of success in a nursing program. Journal of Nursing Education, 49(9),
498-503.
16. Questions?
Here is our contact information
Sara Cano: sicano1@csmd.edu
Lisa Gonzalez: ligonzalez@csmd.edu
Sheila Levings: splevings@csmd.edu
Robin Young: rmyoung@csmd.ed
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Editor's Notes
*Student success in a nursing program is very important to nurse educators.
In the early 2000’s a trend was noted that students with certain risk factors coming into the program were more likely to have difficulty passing one or more nursing courses.
*A taskforce was created in the fall of 2002 that looked at the student population over a 5 year period of (337 students were reviewed) and found these main factors increasing a student’s risk:
GPA less than 2.5
Biology classes with grades of “C” or less.
Math classes with grades of “C” or less.
Student who worked greater than 20 hours per week
*From the data collected and analyzed, a student success scale was developed to identify the “at risk student.”
*Nursing course faculty met with each student and reviewed the tool to map out where they fell within the scale. It was at this point where strategies to enhance their success were shared (time/stress management, note taking, test-taking strategies and such).
*CSM began twice a year admissions in 2002 to assist with the need for more bedside nurses in the southern MD region. The data management became too large and it was recognized that interventions were needed before students’ acceptance into the program to enhance their success. A nursing program outreach coordinator position was created & funded through the NSP II Grant in 2006 to assist with the process.
*In the new position the focus was to meet with pre-clinical students to help them identify their risk factors before coming into the program.
*Here is a version of the original tool with minor revisions since its inception 2002.
*Both objective and subjective data for each student was reviewed. Above the continuum, objective data was reviewed that included:
Teas score. Originally the ACT score was reviewed which was later replaced.
BIO classes
Math courses were deleted as it didn’t show enough of a direct correlation with success in the nursing program
Subjective Data:
Additional course credits taken
Hours of work
Extracurricular Activities
Level of Support System
Results were circled and then a vertical line was drawn down the page. Those who fell to the right of the line or those results that fell in the grey area. were at greater risk.
A full-time committed nursing program retention coordinator position has been added to the staff who assists and provides remediation and guidance to all students throughout the nursing program. The nursing program outreach coordinator role has been replaced with Director, Health Sciences Programs Outreach
As we reviewed the literature we considered findings in light of our student population. It was interesting to find that more than just grades could predict academic success. The main three categories we noticed were 1) affective predictors, environmental predictors, and academic predictors.
Affective predictors included confidence, self-control, emotional intelligence, and resilience. Many of out students have numerous stressors throughout the nursing program, so considering affective factors may help them learn to manage these stressors.
Emotional intelligence is defined as “an array of emotional, personal, and social abilities and skills that influence one’s overall ability to succeed in coping with environmental demands and pressures.” Such an important skill that one study (Jones-Schenk & Harper) considered including emotional intelligence as an admission criteria. They describe the use of the Emotional Quotient Inventory.
Students in overall high levels of emotional intelligence were more likely to progress through their nursing program in a timely fashion.
Consider students who have trouble in their coursework such as low grades or a course failure; they are at risk for losing confidence. Low confidence is a negative predictor for student success. Failure to seek help is also a negative risk factor; this is unfortunate since we may so many resources available to students such as the retention coordinator.
Many of our students work full time jobs, sometimes part time jobs, so the first point was very significant for us. The study found that working greater than 16 hours per week is a risk factor. Many students may not realize the time commitment necessary to be successful in coursework. Not only do you have class time, but you also must make time to study.
We shared that nursing students experience numerous stressors, particularly in our program related to being a non-traditional student. They have family responsibilities and obligations. The second point is exceptional pertinent to our students (go through the bullet).
Now we move onto academic predictors.
How well students perform in year one predicted their success in year two. We found this to be particularly true of our nursing program when we reviewed the data. The first year of a nursing program often sets the foundation for building more advanced concepts found later in the program.
The TEAS test measures Reading, Math, Science, and English skills. The most significant predictor of the TEAS exam in the study above is Science. Our students use the TEAS test currently, but the score is reported as a whole. May consider focusing more on the science and reading scores to predict academic success.
Finally, course grades matter. This final point causes us to reflect on the minimum final grade in order to pass a course. Also, lower grades may indicate less preparation for later courses (courses build on one another at times), and I wonder if failing a course could also connect back with the confidence piece-one of the affective factors we identified earlier.
The whole literature review helped us consider our risk factors that we might want to identify and intervene for our student population.
Sheila Historical data from college-wide system (CROA) was combined with outcomes reported by MBON
Many changes have occurred since the inception of the original student success scale over the last 20 years:
The transition of the “traditional” to “nontraditional student.”
Ease of retrieving objective data from data management systems
Expansion and diversity of the student population
Expansion of the number of graduates our program has produced
Today’s tool now focuses on the student’s personal, socio-economic factors that research predicts student success.
