University of Connecticut University of UC Title Fallback Connecticut

Data Integration Project

The research plan has the goal of predicting student retention in STEM fields at the University of Connecticut. The author over the past year has analyzed grade data obtained for the School of Engineering. Preliminary data analysis using simple regression shows a high correlation between student fourth semester GPA and grades in Math 1131, Math 1132, Chem 1127 and Chem 1128. While the high correlation to GPA is not surprising, it is surprising that student grades in Math 1132 and Chem 1128 show trends of being the strongest predictors of student decision to continue on or to leave engineering disciplines. While there is a need to do a detailed analysis of this data using logistic regression methods, there is no denying the high percentages of students receiving D, F or W (DFW) grades in these freshmen-level courses. Thus the goal of this proposal is to help reduce these high percentages of students that receive D, F, or W grades by tracking student performance from the time that they take a placement exam (ALEKS) to the time they receive a grade in Math 1132 at the end of second or third semester.

This research will focus on the following specific objectives.

  • ALEKS data integration of knowledge slices with student grades.
  • Integration of data from each exam administered during the course.
  • Q Center visits data.

Funded by: 2013 Fund for Innovative Education in Science – “STEM career path management program”- CLAS award ($30262.00).