Hospitals and healthcare facilities in the United States are facing serious shortages of medical laboratory personnel, which, if not addressed, stand to negatively impact patient care. The problem is compounded by a reduction in the numbers of academic programs and resulting decrease in the number of graduates to keep up with the increase in industry demands. Given these challenges, the purpose of this study was to identify predictors of success for students in a selected 2-year Medical Laboratory Technology Associate in Science Degree Program. This study examined five academic factors (College Placement Test Math and Reading scores, Cumulative GPA, Science GPA, and Professional [first semester laboratory courses] GPA) and, demographic data to see if any of these factors could predict program completion. The researcher examined academic records for a 10-year period (N =158). Using a retrospective model, the correlational analysis between the variables and completion revealed a significant relationship (p < .05) for CGPA, SGPA, CPT Math, and PGPA indicating that students with higher CGPA, SGPA, CPT Math, and PGPA were more likely to complete their degree in 2 years. Binary logistic regression analysis with the same academic variables revealed PGPA was the best predictor of program completion (p < .001). Additionally, the findings in this study are consistent with the academic part of the Bean and Metzner Conceptual Model of Nontraditional Student Attrition which points to academic outcome variables such as GPA as affecting attrition. Thus, the findings in this study are important to students and educators in the field of Medical Laboratory Technology since PGPA is a predictor that can be used to provide early in-program intervention to the at-risk student, thus increasing the chances of successful timely completion.

No description available.

High attrition rates and a nursing shortage across the nation have led schools of nursing to seek out ways to better identify which applicants will be most successful in graduating from the nursing program and passing the National Council Licensure Examination for Registered Nurses (NCLEX-RN). Nursing programs have historically included standardized entrance exam scores and prerequisite scores among their admission criteria but have not used standardized critical thinking assessments (CTA), even though critical thinking is an integral part of being a successful nursing professional. Using Astin's input-environment-output (I-E-O) model, the purpose of this retrospective correlational study was to determine whether a significant relationship exists between prerequisite grade point average (GPA), Test for Essential Academic Skills (TEAS) composite scores, entrance and exit CTA scores, and nursing GPA and the outcome of interest, passing the NCLEX-RN exam. Archival data for 64 students enrolled in a baccalaureate degree program at a Texas university were analyzed using binary logistic regression. A significant positive relationship was found between prerequisite GPA, TEAS composite scores, entrance and exit CTA scores, and nursing GPA, and the outcome of interest, passing the NCLEX-RN exam. However, in looking at each independent variable separately, no significant relationship was revealed between the individual scores of the prerequisite GPA, TEAS composite, entrance and exit critical thinking assessment, nursing GPA, and the outcome of passing the NCLEX-RN exam on the first attempt. These findings have implications for positive social change by illuminating the complexities of nursing program retention and graduation and informing efforts to train the most talented nurses.

No description available.

Early college entrance programs have existed in the United States since the 1950s, but in-depth research on academic success in these programs is lacking. Every year, early college entrance programs utilize a variety of data-gathering and candidate-screening techniques to select hundreds of students for admission into these accelerated programs. However, only a smattering of research articles has discussed the factors that predict academic success in these programs. This exploratory study investigated commonly-relied-upon admissions data points—such as high school GPA and ACT scores—and demographic information—such as sex, ethnicity, and locality—to see if any of these factors predicted academic success: namely, graduation and early college entrance program GPA. Secondary data from nearly 800 students admitted over an 11-year period to a state-supported, residential early college entrance program located at a large Southern university in the United States were utilized for this study. Logistic regression failed to yield a model that could accurately predict whether or not a student would graduate from the program. Multiple regression models showed that high school GPA and ACT scores were predictive of performance, and that factors like locality and ethnicity can have predictive power as well. However, the low variance in performance explained by the variables included in this study demonstrates that high school GPA, standardized test scores, locality, sex, and ethnicity can only tell us so much about a student's likelihood of success in an early college entrance program.

The overall goal of this work is to support student success in computer science. First, I introduce BitFit, an ungraded practice programming tool built to provide students with a pressure-free environment to practice and build confidence working through weekly course material. BitFit was used in an introductory programming course (CSC 110) at the University of Victoria for 5 semesters in 2015 and 2016. The contributions of this work are a number of studies done analyzing the log data collected by BitFit over those years. First, I explore whether patterns can be identified in log data to differentiate successful from unsuccessful students, with a specific focus on identifying students at-risk of failure within the first few weeks of the semester. Next, I separate out only those students who struggle early in the semester, and examine their changes in programming behaviour over time. The goal behind the second study is to differentiate between transient and sustained struggling, in an attempt better understand the reasons successful students are able to overcome early struggles. Finally, I combine survey data with log data to explore whether students understand whether their study habits are likely to lead to success. Overall, this work provides insight into the factors contributing to behavioural change in an introductory programming course. I hope this information can aid educators in providing supportive intervention aimed at guiding struggling students towards more productive learning strategies. / Graduate / 0984 / 0525 / 0710 / aestey@uvic.ca