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A Comparative Analysis of Underrepresented Engineering Applicants Admission Practices and their Academic Performance at the University of Illinois at Chicago

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Conference

2016 ASEE Annual Conference & Exposition

Location

New Orleans, Louisiana

Publication Date

June 26, 2016

Start Date

June 26, 2016

End Date

August 28, 2016

ISBN

978-0-692-68565-5

ISSN

2153-5965

Conference Session

Institutional Capacity and Supportive Structures in Engineering Education

Tagged Division

Minorities in Engineering

Tagged Topic

Diversity

Page Count

20

DOI

10.18260/p.26279

Permanent URL

https://peer.asee.org/26279

Download Count

29

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Paper Authors

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Houshang Darabi University of Illinois at Chicago

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Dr. Houshang Darabi is an Associate Professor of Industrial and Systems Engineering in the Department of Mechanical and Industrial Engineering (MIE) at the University of Illinois at Chicago (UIC). Dr. Darabi has been the Director of Undergraduate Studies in the Department of MIE since 2007. He has also served on the College of Engineering (COE) Educational Policy Committee since 2007. Dr. Darabi is the recipient of multiple teaching and advising awards including the COE Excellence in Teaching Award (2008, 2014), UIC Teaching Recognitions Award (2011), and the COE Best Advisor Award (2009, 2010, 2013). Dr. Darabi has been the Technical Chair for the UIC Annual Engineering Expo for the past 5 years. The Annual Engineering Expo is a COE’s flagship event where all senior students showcase their Design projects and products. More than 600 participants from public, industry and academia attend this event annually.

Dr. Darabi is an ABET IDEAL Scholar and has led the MIE Department ABET team in two successful accreditations (2008 and 2014) of Mechanical Engineering and Industrial Engineering programs. Dr. Darabi has been the lead developer of several educational software systems as well as the author of multiple educational reports and papers. Some of these products/reports have already been launched/completed and are now in use. Others are in their development stages. Dr. Darabi’s research group uses Big Data, process mining, data mining, Operations Research, high performance computing, and visualization techniques to achieve its research and educational goals.

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Fazle Shahnawaz Muhibul Karim University of Illinois at Chicago

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Fazle Karim is an aspiring data scientist who is completing his PhD in the Mechanical and Industrial Engineering department at University of Illinois at Chicago. He received his BSIE in 2012 from the University of Illinois at Urbana Champaign. He is currently the lead data scientist at PROMINENT lab, the leading process mining research facility at the university. He has taught courses in Probability & Statistic in Engineering, Work Productivity Analysis, Quality Control & Reliability, and Safety Engineering. His research interest includes education data mining, health care data mining, and time series analysis.

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Samuel Thomas Harford University of Illinois at Chicago

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Samuel Harford is a senior undergraduate researcher in the University of Illinois at Chicago's Mechanical and Industrial Engineering Department. He will receive his BSIE in May 2016 from UIC. Since 2015 he has done multiple projects in education data mining, some in collaboration with the Dean of Engineering. His research interests include healthcare and education data mining.

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Elnaz Douzali University of Illinois at Chicago

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Elnaz Douzali is a senior undergraduate researcher at the University of Illinois at Chicago. She's a part of the Mechanical and Industrial Engineering Department and will receive her Bachelors of Science in Industrial Engineering in May 2016. Since 2015 Elnaz has participated in multiple projects in Educational Data Mining. Her research interests include Educational Data Mining, Process Mining, and Healthcare. Elnaz will begin her Masters of Science in Industrial Engineering at the University of Illinois at Chicago in the fall of 2016.

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Peter C Nelson University of Illinois, Chicago

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Peter Nelson was appointed Dean of the University of Illinois at Chicago’s (UIC) College of Engineering in July of 2008. Prior to assuming his deanship, Professor Nelson was head of the UIC Department of Computer Science. In 1991, Professor Nelson founded UIC's Artificial Intelligence Laboratory, which specializes in applied intelligence systems projects in fields such as transportation, manufacturing, bioinformatics and e-mail spam countermeasures. Professor Nelson has published over 80 scientific peer reviewed papers and has been the principal investigator on over $30 million in research grants and contracts on issues of importance such as computer-enhanced transportation systems, manufacturing, design optimization and bioinformatics. These projects have been funded by organizations such as the National Institutes of Health, the National Science Foundation, the National Academy of Sciences, the U.S. Department of Transportation and Motorola. In 1994-95, his laboratory, sponsored by the Illinois Department of Transportation, developed the first real-time traffic congestion map on the World Wide Web, which now receives over 100 million hits per year. Professor Nelson is also currently serving as principal dean for the UIC Innovation Center, a collaborative effort between the UIC Colleges of Architecture, Design and the Arts; Business Administration; Medicine and Engineering.

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Ashkan Sharabiani Exelon Corporation

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I am a Senior Data Scientist at Exelon Corporation. My area of expertise is to apply Machine Learning and Big Data Analytics methods in real life problems and drive efficient solutions by creating data products. Prior to joining Exelon, I was a PhD student in Industrial Engineering and Operations Research at the University of Illinois at Chicago. During my graduate studies I was involved in several data analytics projects in Healthcare and Education.

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Abstract

Our team conducted a detailed analysis of ABC University’s College of Engineering (COE) students’ admission and academic performance records in their first two years at the university. ABC University is located in a heavily populated city surrounded with many socioeconomically diverse neighborhoods. Our first goal was to measure how underrepresented students were admitted to the COE and how they performed academically in their first two years compared to the rest of the students. Our second goal was to identify and suggest action plans to increase the number of underrepresented students who enter the COE and to improve their retention rates within COE.

We limited our study to students who came to ABC University directly after graduating from high school. The data set included the records of more than 3,000 students who entered the University between 2008 and 2013. Each student’s record included high school Grade Point Average (GPA); ACT score; race; final course grades and the GPA values in the first 4 semesters. Each student in the data set was assigned an UnderRepresentation Score (URS), which was calculated based on the attributes of the high school that the student graduated from. The high school attributes included the College Readiness Index and Economically Disadvantaged Factor. Students who came from high schools with a low College Readiness Index and a high Economically Disadvantaged Factor were assigned a URS close to 1. A URS close to zero was assigned to students who came from high schools that had a very high College Readiness Index and a very low Economically Disadvantaged Factor. We observed that greater than 90% of applicants with very high URS were African Americans. It was also shown that the majority of applicants with low URS were of White or Asian descent. Therefore, we compared the subpopulations of African American with White/Asian Americans.

Our study included extensive data mining of the students’ data, where we chronologically traced each student’s academic performance over their first 4 semesters. In addition to standard performance indices, such as retention and dropout rates, we also defined new performance indices that were fundamental in measuring the academic performance of underrepresented students. For example, we used the expected value of the number of times a student needs to take a given science or math course as a measure of success.

Our analysis showed that by incorporating URS to the admission criteria, the COE could improve admission chances for underrepresented applicants who are normally denied admission but prove to be successful if admitted. We also showed that underrepresented students have higher dropout rates in their first three semesters compared to the rest of the students. However, those underrepresented students who stay and successfully finish their first three semesters, perform equally well, if not better, than the rest of the students. Based on this analysis, we have suggested a revised set of admission criteria for underrepresented applicants. We have also underlined the importance of monitoring and special advising systems for underrepresented students in the first three semester.

Darabi, H., & Karim, F. S. M., & Harford, S. T., & Douzali, E., & Nelson, P. C., & Sharabiani, A. (2016, June), A Comparative Analysis of Underrepresented Engineering Applicants Admission Practices and their Academic Performance at the University of Illinois at Chicago Paper presented at 2016 ASEE Annual Conference & Exposition, New Orleans, Louisiana. 10.18260/p.26279

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