Predicting Final GPA of Graduate School Students: Comparing Artificial Neural Networking and Simultaneous Multiple Regression

Data from graduate student applications at a large Western university were used to determine which factors were the best predictors of success in graduate school, as defined by cumulative graduate grade point average. Two statistical models were employed and compared: artificial neural networking and simultaneous multiple regression. Both models yielded similar results, indicating that the combination of the following factors could predict 10-12 percent of the variance in graduate grade point average: college to which the student was applying, marital status, gender, GRE verbal and analytical scores, and residency region of students. According to Peter Syverson, vice president for research and information services for the Council of Graduate Schools (CGS) in Washington, D.C. (personal communication, February 9, 2001), on the average, approximately 50 percent of students entering graduate school will actually complete the requirements and graduate. This number varies by university, college, and department. Students in the hard sciences, such as biology and chemistry, have higher completion rates than students in disciplines such as the humanities and social sciences. Syverson attributed this difference to issues of support for students in varying disciplines. In addition, master's students tend to have a higher completion rate (70 percent) than Ph.D. students (50 percent). Although there is little empirical data, Syverson said that these figures are based on the experience of deans. Data found on the University of California, San Diego Web site indicated that the percentage of graduate students finishing their degrees within ten years varied from 48 percent in the social sciences to 75 percent in the health sciences. Determining which students applying to graduate school will be most successful is an arduous task at best. An even more difficult task is determining what characteristics of applying students can be used to predict success. Are we asking the right questions, looking at the right statistics, and using the correct procedures? This research was designed to study the potential for the use of artificial neural network (ANN) techniques in the admissions decision-making process. A similar study, conducted by Gorr, Nagin, and Szczypula (1994) used ANN technology to predict graduate student grade point average (GPA). The study focused primarily on the methodology and not so much on the variables that best predict success in graduate school. Other studies have focused on the predictor variables but have utilized traditional statistical methods such as regression. Dawes (1971) called for more research concerning the determination of graduate success, stating that decisions made through the use of computers are more systematic, economical, and human than decisions based on intuition. Artificial neural networking (ANN) is a predictive statistical model that uses historical data and complicated algorithms in large databases to find relationships and trends, previously unknown to the researcher, to promote decision support (Foley and Russell 1998). Through the use of mathematical algorithms, the software accesses historical data and through a training process, learns to make predictions on new data sets based on the lessons learned from the training set. There is potential for a decrease in the amount of time spent on admissions decisions by implementing the use of ANN technology. Sadler and Hammerman (1999) stated that finding ways to save time without compromising the process is a reasonable goal. Several studies have been performed to improve the selection of graduate students. As stated earlier, only one of these studies utilized ANN technology (Gorr, Nagin, and Szczypula 1994). In addition, different methodologies have been utilized to facilitate this process. The results of these studies are inconsistent (see Table e on page 20). Further research is needed in the area of determining which types of models can accurately predict graduate student success. …