Date of Award

6-2021

Degree Name

MS in Industrial Engineering

Department/Program

Industrial and Manufacturing Engineering

College

College of Engineering

Advisor

Lizabeth Thompson

Advisor Department

Industrial and Manufacturing Engineering

Advisor College

College of Engineering

Abstract

Low income, academically talented, underrepresented students within the Central Coast of California face barriers in transferring and completing their technical degree. In order to meet future work needs and improve the quality of public life, the path for transfer students needs to be more accessible. To improve access to a high-quality engineering education for local students, the ENGAGE grant (Engineering Neighbors: Gaining Access, Growing Engineers -NSF Grant numbers 1834128 and 1834154) was created. This initiative strives to support local transfer students pre-transfer, during transfer, and post transfer by providing additional academic and financial resources. Five years of Cal Poly transfer student data was collected for analysis on the factors impactful on academic success as measured by Cal Poly cumulative undergraduate degree GPA. This analysis was divided between engineering and non-engineering transfer students. Regression models were created for each subset of transfer students to identify the predictive traits of historically successful students. For engineering students, the developed model included the factors of CSU Mentor GPA (the student’s application GPA), Extracurricular Activity Points (points awarded based upon the number of extracurricular activities on the application), Father’s Education Code (the level of the education achieved by the student’s father), Major (the major enrolled in by the student), Ethnicity Code (the ethnicity the student identified as), and the CA Resident Flag (if the student resided in California at the time of application). These factors were responsible for about 29.61% of variation within the undergraduate degree GPA. Students who had obtained a higher CSU Mentor GPA were predicted to achieve a higher undergraduate degree GPA. Students who stem from primarily underrepresented ethnicities (such as African American/Black preference and Hispanic) and/or were first generation college students were predicted to achieve a lower undergraduate degree GPA within engineering majors. Those who were California residents were predicted more likely to succeed. For non-engineering transfer students, the factors included within the model were CSU Mentor GPA (the student’s application GPA), Major (the major enrolled in by the student), Ethnicity Code (the ethnicity the student identifies as), Work Hour Range Code (the number of hours worked per week), Gender Code (the gender the student identified as), and Academic Extracurricular Leadership Points (the number of points awarded for extracurricular leadership activities). These factors were responsible for 33.88% of the variation with the undergraduate degree GPA. Students who obtained a higher CSU Mentor GPA were more likely to achieve a higher undergraduate degree GPA. Non-engineering students who identified within underrepresented ethnicities such as American Indian/Alaska Native and African American/Black Preference were predicted to achieve a lower undergraduate degree GPA. Those who engaged in six to twenty hours of work per week were predicted less likely to succeed. Based upon both models, any future initiatives in support of transfer students should consider that background of students who have historically achieved lower undergraduate degree GPAs.

Several dashboard tools utilizing the statistical program R are presented for future implementation to support the ENGAGE faculty team. These tools include a data overview, numerical variable summaries, categorical variable summaries, variable summary and plots, factor investigation, and regression model creation. These dashboards will be implemented within an interactive data sandbox that will allow users of varying data skill levels to investigate the transfer student data. Thus, through ENGAGE, further analysis of the factors that impact the success of transfer students will be possible within the data sandbox. Then, transfer student programs and resources can be directed to students who would benefit from additional support.

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