Engineering Commons^™

A Standards-Based Grading Model To Predict Students' Success In A First-Year Engineering Course, Farshid Marbouti

Open Access Dissertations

Using predictive modeling methods, it is possible to identify at-risk students early in the semester and inform both the instructors and the students. While some universities have started to use standards-based grading, which has educational advantages over common score-based grading, at–risk prediction models have not been adapted to reap the benefits of standards-based grading. In this study, seven prediction models were compared to identify at-risk students in a course that used standards-based grading. When identifying at-risk students, it is important to minimize false negative (i.e., type II) errors while not increasing false positive (i.e., type I) errors significantly. To increase …

Student Success: Approaches To Modeling Student Matriculation And Retention, Jien-Jou Lin

Open Access Dissertations

Every year a group of graduates from high schools enter the engineering programs across this country with remarkable academic record. However, as reported in numerous studies, the number of students switching out of engineering majors continues to be an important issue. Previous studies have suggested various factors as predictors for student retention in engineering. To assist the engineering students with timely advising early in their program, an effective prediction model of matriculation and retention in engineering that use available student data are highly desirable.

In the first part of this work, the author developed new prediction models of student retention …