2024 ASEE Annual Conference & Exposition

Successes and Challenges of College-Wide Mentorship Programs

Presented at First-Year Programs Division Technical Session 10: Curricular & Program Design

This Complete Evidence-based Practice paper presents the analysis of data collected over a three-year period from a mentorship program and provides inferences and insights about its effectiveness. Mentorship programs have been adopted by colleges of engineering in either formal or informal formats to support student success and transition to careers. The College of Engineering at the University of ___ initiated formal mentoring programs in fall 2020 to support student retention and career readiness. The program has two components, a peer mentorship program and a career mentorship program. The peer mentorship program is designed for students entering the university as incoming first year or transfer students and pairs them with current student mentors (usually sophomores or juniors). The career mentorship program pairs current juniors and seniors with alumni or industry mentors. Both mentorship programs use the _____ platform which includes web-based dashboards, mentor/mentee training and matching support, and tools for tracking various metrics of program engagement. Initial results of the peer mentorship program published after the first year suggested a higher retention rate for students choosing to participate in the program than for those who did not opt-in to the program. Additionally, female and Black students who participated in the peer mentorship program had significantly stronger retention than white males. This paper analyzes long term data to determine if the original observations are sustained. Insights obtained through mid-year participant surveys are also discussed. Participation in the career mentorship program declined significantly over the years. The reasons for this decline based on student survey responses are discussed. The observations from our mentorship programs should be of interest to other universities that have or are thinking of similar mentoring efforts.

Authors

Dr. Nadiye O. Erdil University of New Haven [biography]

Nadiye O. Erdil is an associate professor of industrial and systems engineering and engineering and operations management at the University of New Haven. She teaches at the undergraduate and graduate level and has held several academic positions including administrative appointments. In addition to her work in engineering education, her background and research interests are in quality and productivity improvement using statistical tools, lean methods and use of information technology in operations management. Her work is primarily in manufacturing and healthcare delivery operations. She worked in sheet metal manufacturing and pipe fabrication industry as a process engineer for several years. She obtained her Ph.D. in Industrial and Systems Engineering and M.S. in Industrial Engineering from Binghamton University (SUNY); and her Bachelor’s is in Computer Engineering from Bogazici University.
Dr. Ronald S Harichandran P.E. http://orcid.org/0000-0003-3293-1523 University of New Haven [biography]

Ron Harichandran is Dean of the Tagliatela College of Engineering and leads several curricular and student support efforts in the college including the mentorship programs described in this paper.
Dr. Stephanie M Gillespie University of New Haven [biography]

Stephanie Gillespie is the Associate Dean at the Tagliatela College of Engineering at the University of New Haven in West Haven, CT. Since entering academia, she has been passionate about preparing the next generation of engineers with real-life skills, specifically by teaching courses in the area of engineering service learning, first-year engineering courses, and the Grand Challenges of Engineering. Her current research interests span multiple areas of engineering education including makerspaces, multidisciplinary teams, gender diversity and minority retention, and entrepreneurial mindset. Her PhD from Georgia Tech focused on machine learning and signal processing for affective computing, specifically detecting stress and depression in adults with communication disorders. She is actively involved in the Society of Women Engineers and EPICS in IEEE.

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