Date

1-4-2016 12:00 AM

Major

Computer Engineering

Department

Electrical & Computer Engineering

College

College of Engineering

Project Advisor

Amy Kaleita

Project Advisor's Department

Agricultural and Biosystems Engineering

Description

Historical data from multiple institutions show that students who achieve a first-semester grade point average (GPA) below 2.0 are at substantially greater risk of leaving engineering programs before graduating with a degree than are those who achieved above 2.0. Identifying these “at risk” students prior to the start of their first semester could enable improved strategies to enhance their academic success and likelihood of graduation. This study analyzes why students who may have been “at risk” coming in did not end up getting below a 2.0 GPA in their first semester. The data from the MapWorks survey taken by all first-year students was compared for the different risk level groups. From this data, there are some statistically significant answers that differentiated those students who over-performed their “at risk” status and those who did not. These answers can be used when advising “at risk” students in engineering in the future.

File Format

application/pdf

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Apr 1st, 12:00 AM

Engineering GPA Achievement Analysis

Historical data from multiple institutions show that students who achieve a first-semester grade point average (GPA) below 2.0 are at substantially greater risk of leaving engineering programs before graduating with a degree than are those who achieved above 2.0. Identifying these “at risk” students prior to the start of their first semester could enable improved strategies to enhance their academic success and likelihood of graduation. This study analyzes why students who may have been “at risk” coming in did not end up getting below a 2.0 GPA in their first semester. The data from the MapWorks survey taken by all first-year students was compared for the different risk level groups. From this data, there are some statistically significant answers that differentiated those students who over-performed their “at risk” status and those who did not. These answers can be used when advising “at risk” students in engineering in the future.