2. Implementation Current Algorithm
Welcome to the Implementation Page!
The purpose of this algorithm is to automate Greenwood's current matchmaking process between mentors and students to save them time. Since they already have an app under development that they want this algorithm to fit into, and are under the process of digitizing their past records, we have prototyped an algorithm and a rough UI that may help them.
Our algorithm uses three sources of inputs: Greenwood’s Database, a Mentor UI, and a Student UI.
Input 1: Greenwood Database
Greenwood is storing/will store student info that they get from student applications in a database. From there, we are extracting students' technical skills, education level, and industry of preference.
Input 2: Mentor UI
From an easy to use UI on the mentor landing page, we gauge their gender, technical skill, availability, the industry experience that they have, and preferred education level of their student. There are some more features we intend to work with if/when we work with Greenwood in the future such as their past with Greenwood as students/mentors, their current college year, the job they're in, etc. All these features will be thoroughly worked on when we're able to work closely with the members of Greenwood.
Input 3: Student UI
To take into account the comfort level a student can have with different types of people, our UI asks them a couple of simple questions, whose answers alter the algorithm to yield personalized results.
The student UI prototype:
With these features in place, the algorithm gives the students an ordered list of mentors they can choose from. To ensure complete control, students also have filters if they feel the results do not accurately represent their needs.
The algorithm will work as follows: The algorithm was constructed and programmed with the utmost care, taking into consideration a list of criteria provided by greenwood that they consider vital for creating good matches. Each criterion was then weighted appropriately within the algorithm to give certain criteria a higher preference for generating a good match within the algorithm. Applicants rate the importance of each criterion on the preference UI page we designed, the algorithm then tries to match the applicant's preference ratings with the appropriate mentor profile. This generates a percentage match for each applicant with every mentor, thus achieving our goal, generating recommendations that are personalized to each applicant's needs.
Applicants can then view every single mentor present within Greenwoods database, with their best matches displayed in descending order. Applicants also have the option to filter out profiles based on criteria like gender, ethnicity, etc to help them make a decision.