Matching Items (2)
- All Subjects: SOLS
- Creators: Harrell, Carita
- Creators: Numani, Asfia
- Creators: Plasencia, Jon
- Member of: Theses and Dissertations
Assessing School of Life Sciences freshmen satisfaction in the Life Sciences Career Paths mentoring program
Abstract The BIO 189 Life Sciences Career Paths course is a seminar course that is intended to acclimate incoming freshmen into the School of Life Sciences (SOLS). While there are instructors who organize and present in the class, upper division undergraduate students are primarily responsible for facilitating lectures and discussions and mentoring the freshmen. Prior research has demonstrated that the mentor-mentee relationship is a very important predictor of success and retention within all university first-year programs. While past studies focused on the student mentor-mentee relationships, there is limited research that measures student satisfaction within freshmen seminar courses, especially in areas of science, technology, engineering, and mathematics (STEM). The purpose of this project is to survey students about their perception of the BIO 189 course. The effort of the project is on pre-health students, as they initiate their undergraduate careers and attempt to achieve acceptance into professional school four years later. Analysis of Likert scale surveys distributed to 561 freshmen revealed that students with an emphasis on "medicine" in their majors preferred a BIO 189 course geared to pre-health interests whereas students seeking an emphasis on research (ecology and cell biology/genetics) sought a BIO 189 course focused on internship and employment opportunities. Assessment of the mentor-mentee relationship revealed that students (n = 561) preferred one-on-one meetings with mentors outside of class (44%) compared to those who preferred interaction in class (30%). A sizable 61.68% of students (n = 548) were most concerned with attaining favorable GPAs, highlighting strong emphasis on academic performance. Overall, 61% of respondents (n = 561) expressed satisfaction with SOLS resources and involvement opportunities, which was hypothesized. These results give substantial insight into the efficacy of a first-year success seminar-mentoring program for college freshmen in STEM.
Fetal Growth Models of Cardiac Size and Function, and Prediction of Congenital Cardiomyopathy in Fetuses with Diabetic Mothers
2D fetal echocardiography (ECHO) can be used for monitoring heart development in utero. This study’s purpose is to empirically model normal fetal heart growth and function changes during development by ECHO and compare these to fetuses diagnosed with and without cardiomyopathy with diabetic mothers. There are existing mathematical models describing fetal heart development but they warrant revalidation and adjustment. 377 normal fetuses with healthy mothers, 98 normal fetuses with diabetic mothers, and 37 fetuses with cardiomyopathy and diabetic mothers had their cardiac structural dimensions, cardiothoracic ratio, valve flow velocities, and heart rates measured by fetal ECHO in a retrospective chart review. Cardiac features were fitted to linear functions, with respect to gestational age, femur length, head circumference, and biparietal diameter and z-scores were created to model normal fetal growth for all parameters. These z-scores were used to assess what metrics had no difference in means between the normal fetuses of both healthy and diabetic mothers, but differed from those diagnosed with cardiomyopathy. It was found that functional metrics like mitral and tricuspid E wave and pulmonary velocity could be important predictors for cardiomyopathy when fitted by gestational age, femur length, head circumference, and biparietal diameter. Additionally, aortic and tricuspid annulus diameters when fitted to estimated gestational age showed potential to be predictors for fetal cardiomyopathy. While the metrics overlapped over their full range, combining them together may have the potential for predicting cardiomyopathy in utero. Future directions of this study will explore creating a classifier model that can predict cardiomyopathy using the metrics assessed in this study.