The Development of a Research-Based Application for Effective Mentor-Mentee Matching
Location
SU-216
Start Date
28-4-2023 11:40 AM
Department
Engineering
Abstract
This research explores the development of a research-based application for finding an effective mentor-mentee match. Mentoring is a process defined as a mutually beneficial and collaborative relationship between two individuals, and it is essential to professional success. After many years of research, the mentoring definition is yet to be unified. Successful mentoring occurs when the relationship evolves organically in various formal and informal forms. However, there is no blueprint for a mentoring relationship, so finding the ideal one is challenging. We hypothesize that personality type, demographics, career, and interests contribute to ideal mentoring relationships and that there is a percentage of matching between these four dimensions. Which dimension carries more weight or minimum matching percentage is needed remains unknown. Using a tier-based approach, we designed an experimental algorithm to test which dimensions carry more weight and what matching percentage is needed. The experimental algorithm is implemented to develop an automated application for the mentor-mentee matching process. For the Phase 1 experiment, hundred (100) participants were asked to sign up as a mentor, a mentee, or both. The algorithm matched participants' responses. Mentors and mentees are blindly matched into pairs to prevent bias; the pairs undergo a guided mentoring experience for two months, later asking participants to complete a user experience survey—responses used for algorithm adjustments. Seven matches completed the mentorship process in the experiment's results. Results are preliminary, so the algorithm adjustments are awaiting. The increased sample size is developing to achieve the Phase 1 experiment and adjust the algorithm for Phase 2. We expect the research-based mentor-mentee app to be an effective approach to mentoring, with the wide use of a well-tested algorithm.
Faculty Sponsor
Doris Espiritu, Wilbur Wright College
The Development of a Research-Based Application for Effective Mentor-Mentee Matching
SU-216
This research explores the development of a research-based application for finding an effective mentor-mentee match. Mentoring is a process defined as a mutually beneficial and collaborative relationship between two individuals, and it is essential to professional success. After many years of research, the mentoring definition is yet to be unified. Successful mentoring occurs when the relationship evolves organically in various formal and informal forms. However, there is no blueprint for a mentoring relationship, so finding the ideal one is challenging. We hypothesize that personality type, demographics, career, and interests contribute to ideal mentoring relationships and that there is a percentage of matching between these four dimensions. Which dimension carries more weight or minimum matching percentage is needed remains unknown. Using a tier-based approach, we designed an experimental algorithm to test which dimensions carry more weight and what matching percentage is needed. The experimental algorithm is implemented to develop an automated application for the mentor-mentee matching process. For the Phase 1 experiment, hundred (100) participants were asked to sign up as a mentor, a mentee, or both. The algorithm matched participants' responses. Mentors and mentees are blindly matched into pairs to prevent bias; the pairs undergo a guided mentoring experience for two months, later asking participants to complete a user experience survey—responses used for algorithm adjustments. Seven matches completed the mentorship process in the experiment's results. Results are preliminary, so the algorithm adjustments are awaiting. The increased sample size is developing to achieve the Phase 1 experiment and adjust the algorithm for Phase 2. We expect the research-based mentor-mentee app to be an effective approach to mentoring, with the wide use of a well-tested algorithm.