Challenge & Selection:

Twice a year, the club that I’m involved accepts new members. As part of this process, they are

assigned a mentor. The mentor is someone within the club, who has been there for at least six months.  The role of the mentor is to guide the new member through the education process, adjust to the  organization, acquaint them with the traditions and history of the organization, and encourage them.   The challenge that the organization faces is the best way to assign the incoming members to a mentor  because this relationship is so important. We want to make the best matches possible, but this is  difficult if several current members all want to mentor the same new member, or vice versa.  To  approach this challenge, I chose learning launch because it allows you to test ideas quickly and come up  with a solution. This seemed like the best option since many members of the club had good ideas and  we needed a way to come up with the best choice before the next mentor assignment.  Application    The first step of the learning launch is to form a hypothesis. In this situation, the hypotheses

were the different mechanisms for assigning mentors. The mechanisms were the following: the current  method, a marriage matching algorithm, and a mechanism based on the allocation of school districts.    The current method involved a reward system of points within the organization which measured

involvement, attending events, meeting deadlines, holding positions, etc. The person with the most  points would pick the person they wanted to mentor first, and then the person with the second most  points would pick next, and so on until everyone had a mentee. Although this method ensured that  everyone was assigned a mentor, it did not necessarily make good matches.    The marriage matching algorithm was a bit more complicated, but the general idea is that all

potential mentors and new members rank the order of their preference. Then, the mentor “proposes”  to the mentee that is their first choice. All the mentees who have received a proposal tentatively accept  one (in the case that they have more than one offer, they would pick the one that has the highest rank  on their list). This would continue until each of the new members are matched. This system involves  matches in which no partner could make a match that they’re mutually happier with.    The school district prioritized based on distance, but this interpretation of it prioritized based on

seniority. The longer you had been in the organization the earlier you got to choose your mentee in the  process.    The data collected was the mentor lists from previous years, and the experiments involved

simulating the assignment of mentors from the lists for each of the three learning launch ideas above.  Insight    The goal was to find a mechanism that did not favor either the current members or the

incoming members, but rather produced matches that would be beneficial to both. After testing each of  the three suggestions with the data from past years, we determined that the marriage matching  algorithm was the best option. There were too many situations in either the points or school district  method that allowed for one side to be better off than the other, and created several pairs that were  not on the lists of members.  Approach    The learning launch tool is probably the best approach to this particular scenario, and in the

future,  it could be used to test any new selection techniques as the organization comes up with them.

