MentorMatch: A Research Based Application for Mentoring Relationships that Matter
Location
SU-216
Department
Computer Science
Abstract
Despite the existence of various mentoring programs, the challenge of identifying an ideal mentor-mentee match persists. Our research is focused on the development of an application for effective mentor-mentee matching. Mentoring is a structured relationship where a mentor imparts knowledge and guidance to a mentee, aiming for their personal and professional development. Various models exist, including one-to-one, situational, group, or peer-based mentoring, within both formal and informal settings. We hypothesize that a successful mentor-mentee relationship requires compatibility across Four Dimensions: personality type, demographics, career aspirations, and interests. The impact of these dimensions on the effectiveness of the mentorship is in question. To explore this, we introduced an experimental Phase 1 algorithm using a tier-distance based approach to facilitate automated matching within an application framework. The initial phase involved 128 participants registering as mentors, mentees, or both. Using a preliminary algorithm based on the Four Dimensions, the application assessed participant profiles to form mentor-mentee pairs blindly to mitigate bias. Matches were then asked to undergo mentoring experiences. 46 participants completed a mentoring experience for 2 months and responded to a post-survey. A majority of participants (93.4%) felt satisfied or better about their mentoring experiences. As for dimensions, 34.8% of participants suggest that all equally impacted the mentoring experience, while 41.2% suggest that career aspiration impacted it the most. Conclusions regarding the optimal algorithm remain undetermined. In future research, we will increase the sample size and adjust the algorithm to further test match satisfaction.
Faculty Sponsor
Doris J. Espiritu
MentorMatch: A Research Based Application for Mentoring Relationships that Matter
SU-216
Despite the existence of various mentoring programs, the challenge of identifying an ideal mentor-mentee match persists. Our research is focused on the development of an application for effective mentor-mentee matching. Mentoring is a structured relationship where a mentor imparts knowledge and guidance to a mentee, aiming for their personal and professional development. Various models exist, including one-to-one, situational, group, or peer-based mentoring, within both formal and informal settings. We hypothesize that a successful mentor-mentee relationship requires compatibility across Four Dimensions: personality type, demographics, career aspirations, and interests. The impact of these dimensions on the effectiveness of the mentorship is in question. To explore this, we introduced an experimental Phase 1 algorithm using a tier-distance based approach to facilitate automated matching within an application framework. The initial phase involved 128 participants registering as mentors, mentees, or both. Using a preliminary algorithm based on the Four Dimensions, the application assessed participant profiles to form mentor-mentee pairs blindly to mitigate bias. Matches were then asked to undergo mentoring experiences. 46 participants completed a mentoring experience for 2 months and responded to a post-survey. A majority of participants (93.4%) felt satisfied or better about their mentoring experiences. As for dimensions, 34.8% of participants suggest that all equally impacted the mentoring experience, while 41.2% suggest that career aspiration impacted it the most. Conclusions regarding the optimal algorithm remain undetermined. In future research, we will increase the sample size and adjust the algorithm to further test match satisfaction.