Academic predictor models for engineering technology students

The purpose of the study was to identify variables that can predict the academic success of freshman engineering technology students at Ferris State University in Big Rapids, Michigan. Academic predictor models were created to predict the students academic success. Academic success was measured by the student\u27s first semester grade point average (GPA$\sb-$1), second semester grade point average (GPA$\sb-$2), whether the student returned for a second semester of college (RET$\sb-$1), and whether the student returned for a third semester of college (RET$\sb-$2). The source of variables used in the academic predictor models was the student\u27s high school transcripts, ACT score sheet, and admission application. These documents were chosen because they were easily obtained by faculty and educational counselors. The subjects used for the study were freshman engineering technology students enrolled at Ferris State University in the College of Technology between the academic years of 1990 to 1994. The freshman students were limited to those who had not previously enrolled in college. There were 533 subjects used in the study. A factor analysis was used to create factors that were used in various multiple regression analyses to create academic predictor models. The best academic predictor model for GPA$\sb-$1 had a correlation coefficient of 0.3055. The best academic predictor model for GPA$\sb-$2 had a correlation coefficient of 0.3550. An accurate academic predictor model was not found to predict either RET$\sb-$1 or RET$\sb-$2. When the variables were analyzed in the best three academic predictor models for GPA$\sb-$1, there were five common variables. The common variables were the student\u27s second semester high school Biology grade, second semester high school Chemistry grade, average high school Art grade and the Athletic Score from the ACT score sheet. When the variables were analyzed in the best three academic predictor models for GPA$\sb-$2, there were two common variables. The common variables were the Athletic Score and the Service Organization score from the ACT score sheet. The researcher suggests the common variables in the academic predictor models may help predict an engineering technology student\u27s academic success. The researcher suggests that it is how the student learns, and the student\u27s ability to matriculate into the university setting and the engineering technology programs that may predict academic success