The present paper reports an update on an NSF-funded S-STEM program currently in its last year at the University of Illinois Chicago. Lessons learned during the project implementation are also listed in the paper. A summary of the paper materials will be presented at the ASEE 2023 Annual Conference and Exposition as part of the NSF Grantees Poster Session. The project's objectives are 1) enhancing students' learning by providing access to extra and co-curricular experiences, 2) creating a positive student experience through mentorship, and 3) ensuring successful student placement in the STEM workforce or graduate/professional degree program. As part of this project, students are provided with financial assistance. A total of three Cohorts of students are supported by the project: Engineering students who started as freshmen, including 18 students of Cohort I and 13 students of Cohort II, and 19 students who transferred from various community colleges to Cohort III. More than 60% of the students are classified as minorities. This project has resulted in the creation of several support and intervention programs, including a Summer Bridge Program, an Engineering Success Initiative course, a Service Learning Project course, and an integrated mentoring program that matches each student with an academic mentor (a faculty) and an industry mentor. The paper will summarize the lessons learned from the support programs. Out of the 18 students recruited by this program as Cohort I, all have already graduated, and 16 have started a job. Cohort II students will graduate next semester (Spring 2023), and the majority of students in Cohort III students will graduate in Spring 2023. Two students dropped out of the university in their first year, and one dropped out of the university in the second year. More information is provided in this paper regarding student retention and performance (Grade Point Average).

This Complete Research paper will describe the implementation of an introductory course (ENGR194) for first semester engineering students. The course is meant to improve retention and academic success of engineering first-year students in the College of Engineering at the University of Illinois at Chicago. The implementation of this course is part of an ongoing National Science Foundation (NSF) Scholarships in Science, Technology, Engineering, and Math (S-STEM) project. This paper reports on the impact of combinatorial enrollment in ENGR194 and a previously described two-week Summer Bridge Program (SBP) offered only for entering S-STEM scholars before their first semester. To measure the impact of this course on student retention and academic success, various evaluation metrics are compared for three separate Comparison Groups (C-Groups) of students. The results show that the ENGR194 course had a significant positive impact on the first-year retention rate. The results also revealed that students who participated in both ENGR194 and SBP (C-Group 1) made changes to their declared majors earlier than students who had only taken ENGR 123 or neither of the courses (C-Groups 2 and 3 respectively). Furthermore, students in C-Group 1 received better grades in math and science than their peers, and students in C-Groups 1 and 2 had significantly higher GPAs than their peers in C-Group 3.

Recent research exists that utilizes machine learning techniques to analyze the underlying patterns in the job market. In this paper, Skill Miner System (SMS) is presented. SMS utilizes text mining algorithms to identify these skills and qualifications employers seek for in STEM fields. In addition, SMS generates a skill demand index (SDI), which is used to determine the demand for particular skills. This study focuses on Industrial Engineering but can be easily adaptable to other fields. Data for this study was collected by scrapping various job postings for Industrial Engineers. The data used to develop SMS consisted of more than 5,000 jobs. The underlying pattern of the job market is compared to a public database of various occupational information, O*NET. O*NET, sponsored by the U.S. Department of Labor, is one of the most comprehensive publicly accessible databases of occupational requirements for skills, abilities and knowledge. However, by itself the information in O*NET is not enough to characterize the distribution of occupations required in a given market or region. SMS is different from O*NET as it detects skills required in the job market on a more frequent basis and provides a metric to determine the importance of a skill. This paper shows that SMS is able to detect skills that are required by the job market and are not mentioned in O*NET. SMS sets a weight to portray the demand for each skill. This is beneficial for institutions and organizations to remain competitive in the job market. At the University of Illinois at Chicago, senior engineering undergraduates in the Department of Mechanical and Industrial Engineering are required to take a professional development course (PDC) to assist in their career development. PDC employs SMS routinely to help each student cater to various job positions. In addition, resumes are improved by using additional relevant keywords employers seek, which are detected by SMS. SMS has assisted in increasing the number of students that graduate with a job offers and in the course's goal of helping students obtain careers. The analysis presented in this paper shows that SMS can benefit various stakeholders, such as universities, students, employers, and recruiting firms. Universities will have a better understanding of the job market and will be able to improve the education of their students with the evolving job market. Students will be more qualified and better prepared for the job market. Employers and recruiting firms will be able to remain competitive in the job market.

This paper reports the execution details and the summary assessment of a Summer Bridge Program (SBP) that is a part of an ongoing National Science Foundation (NSF) Scholarships in Science, Technology, Engineering, and Math (S-STEM) project in the College of Engineering at the University of Illinois at Chicago. The project supports 18 Scholars (academically-talented, lowincome engineering students). The primary goals of the SBP are facilitating Scholars’ transition to their first-year and improving their academic success. To achieve these goals, the SBP is implemented as a two-week on-campus intensive experience that occurs in the summer before the student’s first year. The first round of the SBP was completed in Summer 2018 and the current paper offers the details, lessons learned, and a brief evaluation of the SBP. Based on the assessment data, it is concluded that the SBP was successful in achieving its stated goals. The evaluation results and the lessons learned from the SBP execution can be used to build a sustainable Summer Bridge Program for all first-year engineering students in the future.

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.

In this paper, we summarize the poster presented at the NSF Grantees Poster Session that provides an overview of the S-STEM program. The S-STEM program at the University of Illinois at Chicago (UIC) began in 2017 and was developed to provide financial, academic, professional, and social support to incoming engineering students who are low-income and high achieving. The duration of the grant is five years. This paper summarizes the activities in the first 18 months of the project and the activities projected for the remainder of the project. The objectives of this project are to 1) enhance students’ learning by providing access to extra and co-curricular experiences, 2) create a positive student experience through mentorship, and 3) ensure successful student placement in the STEM workforce or graduate school. S-STEM Scholars in this program received financial, academic, professional, and social development via various evidence-based activities integrated throughout four years and starting with the summer prior to starting at the university. These activities include a summer bridge program, a freshman engineering success program, an introduction to engineering design course, a guaranteed paid internship program, a service-learning project, two professional development seminars, and an enhanced capstone experience. In addition, students are supported by peer, faculty, and industry mentors.

This paper proposes a revised approach to the admission process for freshman students entering the minority serving institute, the University of Illinois at Chicago (UIC). The purpose of the revised approach is to better evaluate an extremely diverse population of applicants. The details for the revised approach will be demonstrated through the use of data mining, statistical methods and association rule mining. UIC is located in Chicago, Illinois and enrolls greater than 20,000 students from a wide spectrum of socio-economic neighborhoods. As a minority serving institute, it is of great concern to the University to better assess the capabilities of the diverse population of applicants. Through this paper, the authors propose a process that integrates the socio-economic background of applicants into its admission process which allows for its applicants to be better evaluated. The proposed process increases the population of students with the potential to succeed, thereby increasing university retention rates. An extensive review on success and retention strategies that benefit not only minorities in Science, Technology, Engineering, and Mathematics but all students is provided. This process better assesses applicants from differing background and has the effect of increasing the population of minority students. In addition to the socio-economic based admission metric for such integration, this paper introduces a methodology and framework for a software to promote the success of students by recommending schedules based on their predicted performance. The types of information used in the development of this metric are available in almost every university or higher education institution. Therefore, the process in developing this metric can be implemented by other higher education institutions in the United States that have the potential to benefit from incorporating socio-economic factors into their admission procedure for applicants.