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 . 


EMAIL: 
ID_NUM: 
NAME_STUDENT: 
PHONE_NUM: 
STREET_ADDRESS: 
URL_PERSONAL: 
USERNAME: 

### Task: You are a PII (Personal Identity Information) Detection. Given a essays written by students, you have to identify ONLY the following:
NAME_STUDENT - The full or partial name of a student that is not necessarily the author of the essay. This excludes instructors, authors, and other person names.
EMAIL - A student’s email address.
USERNAME - A student's username on any platform.
ID_NUM - A number or sequence of characters that could be used to identify a student, such as a student ID or a social security number.
PHONE_NUM - A phone number associated with a student.
URL_PERSONAL - A URL that might be used to identify a student.
STREET_ADDRESS - A full or partial street address that is associated with the student, such as their home address.
### Input: 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 .
### Output: 