Predicting and improving first year engineering student retention through lean thinking and quality management concepts

Date of Award


Degree Name

M.S. in Management Science


Department of Engineering Management, Systems, and Technology


Advisor: Kellie R. Schneider


While the percentage of undergraduate engineering degrees awarded has increased over the past decade, it has been outpaced by the overall growth in bachelor degree attainment. With this, the amount of enrollment in engineering programs has increased, but still a significant number of engineering students choose to drop out or pursue other educational paths. Universities and policy makers are motivated to increase the retention of engineering students to graduation. This thesis explores the quantitative data that makes up a first year engineering student's profile. The data is used to develop an ordinal logistic regression model to predict 2nd year student retention. Ideas to improve retention are discussed with a focus of applying Lean Manufacturing techniques in conjunction with the proposed prediction model. Data from a college of engineering within a public land-grant research university is used to test for significance as indicators for freshman retention. Data used in this study is from 2010 and 2011 freshman engineering cohorts. Using collected student data, a prediction model is developed that assesses the probability of a first year engineering student either i) returning to engineering in their second year, ii) leaving engineering but remaining at the university, or iii) leaving the university altogether. Then, using concepts from lean manufacturing and quality management this prediction model is incorporated in a proposed engineering education quality system.This study creates a prediction model to identify students that are likely to be: retained in engineering, switch majors out of engineering, and drop out of the university. This prediction model is then incorporated into the proposed engineering education quality management system to assist with identifying; where and when students may not persist in engineering curriculum, and ideas to promote student persistence using the prediction results.


Engineering students Statistics, Engineering students Education (Higher), Dropouts Prevention, Engineering, Education, Engineering Education, First Year Retention, Quality Management, Lean Concepts

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