Identifying Factors Influencing Engineering Student Graduation and Retention: A Longitudinal and Cross-Institutional Study

In this study, pre-existing factors are quantitatively evaluated as to their influence on student success. This study uses a database of all engineering students in the time period 1987 through 2000 and considers two definitions of success. The first, graduation, is defined as graduation in an engineering degree program as of the latest year in the study. The second, retention, is defined as either graduation or current enrolment in an engineering degree program as of the latest year in the study. A multiple logistic regression model was formulated to test for and estimate the predictive relationships between these measures of success and a set of six background variables that represent student’s pre-existing demographic and academic characteristics (gender, ethnicity, high school GPA, SAT math score, SAT verbal score, and citizenship status). It is found that both graduation and retention in engineering for students who enter in an engineering discipline depends significantly upon high school GPA and math SAT scores, while verbal SAT scores correlated negatively with odds of graduation for five out of eight universities. Gender, ethnicity and citizenship also showed significant effects for some Universities, but these were not consistently positive or negat ive predictors. We also find that gender, verbal SAT scores, ethnicity and citizenship frequently appear as predictors of retention, but not as predictors of graduation. Introduction Identifying those factors that influence retention should be useful in suggesting approaches to improving student success in engineering. The identification of these factors will assist in developing meaningful admission procedures as well as aid the counseling and advising of students seeking an engineering degree. Much research has focused on identifying predictors of success in college and in engineering. Astin’s 1965 study of 36,581 students indicated that the student’s academic record in high school was the best single indicator of how well they would do in college. He also indicated that there was a clear positive relationship between students’ performance on tests of academic ability (e.g. SAT) and performance in college. Astin also listed gender as useful in predicting college freshman GPA. In a more recent study, Seymour and Hewitt reported that the students leaving engineering were academically no different than those that remained. They reported students left for reasons relating to perceptions of the institutional culture and career aspects. Perceptions and attitudes of engineering students have been examined in the literature. Besterfield-Sacre, Moreno, Shuman and Atman developed the Pittsburgh Freshman Engineering P ge 719.1 Attitude Survey (PFEAS). They administered the survey at the beginning of the students first semester and again at the end of the first semester or the end of the first academic year. They report gender differences for female engineering students on the pre-survey. Female engineering students began their engineering programs with lower confidence in background knowledge about engineering, their abilities to succeed in engineering, and their perceptions of how engineers contribute to society than their male counterparts. Those same female students indicated they were more comfortable with their study habits than did the male students. Differences for minority students were reported for African American vs. majority students, Hispanic vs. majority and Asian Pacific vs. majority students. Zhang and RiCharde examined 462 freshmen that matriculated in the fall of 1997. Roughly 32% of these students were engineering majors. They tested several cognitive, affective, and psychomotor variables to see which were significant predictors of college persistence. Their logistic regression identified self-efficacy and physical fitness as positive predictors of freshman retention, while judgment and empathy were negatively associated with persistence. They reported three reasons for freshman attrition: inability to handle stress, mismatch between personal expectations and college reality, and lack of personal commitment to a college education. Levin and Wyckoff gathered data on 1043 entering freshmen in the College of Engineering at Pennsylvania State university. They developed 3 models to predict sophomore persistence and success at the pre-enrollment stage, freshman year, and sophomore year. Eleven intellective and 9 non-intellective variables were measured. For the pre-enrollment model, the variables best predicting success were high school GPA, Algebra score, gender, non-science points, chemistry score, and reason for choosing engineering. The freshman year model identified the best predictors of retention as grades in Physics I, Calculus I and Chemistry I. In the sophomore year model the best predictors of retention were grades in Calculus II, Physics II and Physics I. They noted that predictors of retention were dependent on the students’ point of progress through the first 2 years of an engineering program. Other studies indicate the freshman year is critical. Lebold and Ward indicated the best predictors of engineering persistence were the first and second semester college grades and cumulative GPA. They also reported that students’ self-perceptions of math, science and problem-solving abilities were strong predictors of engineering persistence. In this study, over 10 years of data for 8 colleges of engineering in 9 universities were used to evaluate pre-existing factors’ influence on retention. Many studies have examined retention of engineering students for only one or two years. This snapshot approach while immediately informative does not offer the power of examining predictors over time. The cross-institutional nature allows us to compare the results across the universities to find their generalizability. Specifically, the 9 Universities are each public, but exhibit a wide range in other characteristics such as mission, minority and total enrollment, research emphasis, on-campus enrollment, and number of in-state residents enrolled. The longitudinal nature of our data allows us to look at change across time. Multiple logistical regression techniques allow us to examine the effect of each predictor while controlling for the other variables. We measured retention as both P ge 719.2 graduation and persistence in the engineering program. Our study looks at differences in predictors in these two definitions of retention. Data Collection This study uses the Southeastern University and College Coalition for Engineering Education (SUCCEED) longitudinal database (LDB) to identify pre-college entrance demographic and academic factors that predict engineering students’ graduation. The LDB contains data from eight colleges of engineering involving nine universities: Clemson University, Florida A&M University, Florida State University, Georgia Institute of Technology, North Carolina A&T State University, North Carolina State University, University of Florida, University of North Carolina at Charlotte and Virginia Polytechnic Institute and State University. To protect the rights of human subjects, each university is assigned a letter that is only known by the researchers involved in the study. Throughout the paper, we examine the effects of predictors on two definitions of retention. For both definitions, we refer to the period 1987 through 1998, 1999 or 2000, depending on the latest year available in the LDB for a given institution. In one set of analyses, retention refers to graduation in an engineering program during that time period, which we label graduation. Because it typically takes a student a minimum of four years to graduate, students who have entered university after 1995 have not usually had enough time to graduate, and are excluded from these analyses. Therefore, for the graduation analyses, we only include students matriculated in an engineering field between 1987 and 1994. The number of students used in the retention analyses are listed in the header of Table 1.G. University Cohorts Graduation Percentage Graduation Date A 1987-1994 30.49% 1987-1998 B 1987-1994 24.50% 1987-1998 C 1987-1994 28.20% 1987-1998 D 1987-1994 35.54% 1987-1999 E 1987-1994 50.97% 1987-1999 F 1987-1994 54.33% 1987-1998 G 1987-1994 42.83% 1987-2000 H 1987-1994 43.04% 1987-2000 I 1987-1994 32.71% 1987-1999 Table 1.G. Graduation data by university. Number of engineering students included in the analysis in descending order and not correlated to the alphabetic University designation: 11,382, 8,418, 7,072, 5,815, 2,542, 1,737, 1,065, 705, and 541. The second set of analyses defines retention as either graduation within that time period or current enrolment in the last year of the LDB, which we simply label retention. Thus retention analyses include students who have matriculated in an engineering field during any year from P ge 719.3 1987 through 1998, 1999 or 2000. The number of students used in the retention analyses are listed in Table 1.R. University Cohorts Retention Percentage Retention Period A 1987-1998 52.49% 1987-1998 B 1987-1998 36.97% 1987-1998 C 1987-1998 43.89% 1987-1998 D 1987-1999 46.42% 1987-1999 E 1987-1999 57.30% 1987-1999 F 1987-1998 64.68% 1987-1998 G 1987-2000 48.75% 1987-2000 H 1987-2000 58.86% 1987-2000 I 1987-1999 46.14% 1987-1999 Table 1.R. Retention data by university. Number of engineering students included in the analysis in descending order and not correlated to the alphabetic University designation: 15,079, 12,928, 11,842, 7,574, 4,146, 2,619, 1,501, 1,004, and 856. We study the dependence of graduation and retention on six independent variables (or predictors): ethnicity (ETHNIC), gender (GENDER), high school Grade Point Average (HSGPA), SAT math score (SATM), SAT verbal score (SATV), and citizenship status (CITIZEN). HSGPA, SATM, and SATV are continuous numerical variables, while ETHNIC, GENDER, and CITIZEN are categorical variables having seve