Board 47: A Mentor-Mentee Matching Algorithm to Automate Process of Finding an Ideal Mentor for Students

Sweni Shah; Hamid S Timorabadi; Sanjana Dasadia; Samreen Khatib Syed; Doaa Muhammad

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Conference: 2024 ASEE Annual Conference & Exposition
Location: Portland, Oregon
Publication Date: June 23, 2024
Start Date: June 23, 2024
End Date: July 12, 2024
Conference Session: Computers in Education Division (COED) Poster Session
Tagged Division: Computers in Education Division (COED)
Permanent URL: https://peer.asee.org/47043

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Hamid Timorabadi received his B.Sc, M.A.Sc, and Ph.D. degrees in Electrical Engineering from the University of Toronto. He has worked as a project, design, and test engineer as well as a consultant to industry. His research interests include the applicati

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Sanjana Dasadia

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Samreen Khatib Syed

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Doaa Muhammad University of Toronto

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Abstract

Work in Progress: MentorMate - A Mentor-Mentee Matching Algorithm to Automate Process of Finding an Ideal Mentor for Students

Students frequently face challenges due to limited access to resources that support their technical and personal development. Finding a mentor whose expertise perfectly matches a student’s goals and interests is a major challenge due to the lack of resources universities offer. Existing resources, such as mentor databases, require students to manually filter through a list of mentors to find an ideal match. Searching this list can be time consuming and inefficient, thus an automated solution to simplify and expedite the mentorship process is needed. MentorMate is a software application designed with a mentor-mentee matching algorithm to allow students to match with mentors who have similar backgrounds and interests.

The matching system has two main parts, the frontend and the backend. The frontend component is a MERN web application, and serves as an interface for users to register into the database to get matched. Upon registering, users are asked to pick a role (mentor or mentee), based on which they are presented with a questionnaire. The results from this questionnaire are used to determine similarity scores, which aids the matching process. The heart of the backend component is the matching algorithm which leverages the idea of K-means clustering to group mentees with mentors.

In order to ensure that the algorithm produces desirable results, two kinds of testing is conducted. The first involves a set of several dummy mentors and mentees that tests the algorithm and compares the outcomes with predetermined results. The second round of testing is based on real subjects, where people are asked to register as a mentor or mentee on the web application. After the matching process, the users are surveyed to determine how satisfied they are with their match.

MentorMate is at the forefront of using technology to improve education, changing challenges in education and mentorship are approached. By harnessing the power of automation and intelligent algorithms, MentorMate sets a precedent for the integration of technology in fostering supportive learning environments. It not only enables students to connect with mentors who align with their goals but also contributes significantly to the broader educational landscape. Through meaningful mentor-mentee relationships, the platform creates a collaborative learning environment where knowledge and experiences are shared seamlessly. This approach not only enhances students' technical skills but also nurtures their personal development, resulting in a well-rounded educational experience that prepares them to become future professionals and leaders.

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Shah, S., & Timorabadi, H. S., & Dasadia, S., & Khatib Syed, S., & Muhammad, D. (2024, June), Board 47: A Mentor-Mentee Matching Algorithm to Automate Process of Finding an Ideal Mentor for Students Paper presented at 2024 ASEE Annual Conference & Exposition, Portland, Oregon. https://peer.asee.org/47043

TY - CPAPER
AB - Work in Progress: MentorMate - A Mentor-Mentee Matching Algorithm to Automate Process of Finding an Ideal Mentor for Students

Students frequently face challenges due to limited access to resources that support their technical and personal development. Finding a mentor whose expertise perfectly matches a student’s goals and interests is a major challenge due to the lack of resources universities offer. Existing resources, such as mentor databases, require students to manually filter through a list of mentors to find an ideal match. Searching this list can be time consuming and inefficient, thus an automated solution to simplify and expedite the mentorship process is needed. MentorMate is a software application designed with a mentor-mentee matching algorithm to allow students to match with mentors who have similar backgrounds and interests.

The matching system has two main parts, the frontend and the backend. The frontend component is a MERN web application, and serves as an interface for users to register into the database to get matched. Upon registering, users are asked to pick a role (mentor or mentee), based on which they are presented with a questionnaire. The results from this questionnaire are used to determine similarity scores, which aids the matching process. The heart of the backend component is the matching algorithm which leverages the idea of K-means clustering to group mentees with mentors.

In order to ensure that the algorithm produces desirable results, two kinds of testing is conducted. The first involves a set of several dummy mentors and mentees that tests the algorithm and compares the outcomes with predetermined results. The second round of testing is based on real subjects, where people are asked to register as a mentor or mentee on the web application. After the matching process, the users are surveyed to determine how satisfied they are with their match.

MentorMate is at the forefront of using technology to improve education, changing challenges in education and mentorship are approached. By harnessing the power of automation and intelligent algorithms, MentorMate sets a precedent for the integration of technology in fostering supportive learning environments. It not only enables students to connect with mentors who align with their goals but also contributes significantly to the broader educational landscape. Through meaningful mentor-mentee relationships, the platform creates a collaborative learning environment where knowledge and experiences are shared seamlessly. This approach not only enhances students&#39; technical skills but also nurtures their personal development, resulting in a well-rounded educational experience that prepares them to become future professionals and leaders.

AU - Sweni Shah
AU - Hamid S Timorabadi P.Eng.
AU - Sanjana Dasadia
AU - Samreen Khatib Syed
AU - Doaa Muhammad
CY - Portland, Oregon
DA - 2024/06/23
PB - ASEE Conferences
TI - Board 47: A Mentor-Mentee Matching Algorithm to Automate Process of Finding an Ideal Mentor for Students
UR - https://peer.asee.org/47043
ER -