Online Mentorship Matching Platform for Students
Budget: ₹600 – ₹1,500 INR
Need to create this college project using chatgpt, should include all the frameworks mentioned here, ui should be simple and professional, chat gpt can be used here to create the project.
Title: Project 4: Online Mentorship Matching Platform
Description: Develop a web-based platform that connects mentors and mentees for knowledge sharing and career or academic guidance. Users can sign up either as a
mentor (offering expertise in certain skills or subjects) or as a mentee (seeking guidance in specific areas). The system will collect profiles and preferences, then use
a matching algorithm to suggest optimal mentor-mentee pairings. Once matched, the platform provides communication tools such as messaging or session scheduling to facilitate the mentoring relationship. This project is software-centric, focusing on
matchmaking logic and social networking features, and addresses a real need for structured mentorship in academic and professional communities. (Building this
involves implementing user profile management, matching algorithms, and communication features – a solid full-stack development challenge.)
Objectives:
Design profile forms for mentors and mentees to capture relevant data: for mentors, their expertise, background, availability, and for mentees, their goals, interests, and
areas where they seek help. Include tags or categories (e.g., “Data Science”, “Career
Advice”, “Web Development”) to assist in matching. Implement a matching
algorithm that pairs mentees with suitable mentors. This could be rule-based
(matching on common tags and availability) and/or use a scoring system (e.g., assign
points for each shared interest or desired skill and match the highest scores).
Optionally, incorporate a collaborative filtering or recommendation approach if
historical mentorship ratings data is available (though likely not for a new system).
Provide a communication channel for mentors and mentees: at minimum, an inplatform messaging system to chat and discuss. Additionally, allow scheduling of
mentorship sessions (with a calendar view or integration to set up meeting times)
and possibly email notifications or reminders for scheduled sessions. Include a
feedback mechanism: after some sessions, mentees can rate or review the
mentorship experience. This helps in refining future matches and giving recognition
to mentors. Administrative dashboard: allow admins or program coordinators to
oversee active mentorships, intervene if someone isn’t engaged, and gather statistics
(number of active pairs, average rating, etc.). Ensure privacy and safety: protect
personal contact info (so initial communication is on the platform), and include
terms of conduct. Possibly implement content moderation on messages or a
reporting system for any issues in the mentor-mentee interactions.
Objectives:
Frameworks/Tools/Technologies:• Backend: Use a web framework like Ruby on Rails, Django, or Express (Node.js) to
handle user accounts, profiles, and the matching logic on the server side. A relational
database (PostgreSQL/MySQL) will store user profiles, messages, and match
information.
• Frontend: Utilize HTML/CSS/JavaScript with a modern library (React or Vue.js) for a
responsive user interface. The interface will include profile pages, match suggestions,
messaging UI, and scheduling interface.
• Matching Algorithm: Implement in backend using either pure code (Python or Node) or
possibly an existing library if using something like Python’s scikit-learn for a more
advanced recommendation approach. However, a custom algorithm tuned to the profile
schema is likely sufficient.
• Messaging: Integrate real-time messaging using WebSockets (Socket.io for Node or
Django Channels for Python) so that messages appear instantly. Alternatively, use a
ready chat service or an email notification fallback if real-time is too complex.
• Scheduling: Implement a simple calendar component (e.g., using a library like
FullCalendar in JavaScript) for mentors to indicate available slots and mentees to request
meetings. Use an email API (like SendGrid) to send confirmation or reminder emails for
scheduled sessions.
• Authentication & Security: Use OAuth if integrating with other systems (or at least
allow sign-in with Google/LinkedIn to import basic profile data). Use HTTPS, secure
password storage, and form validation to protect user data. Possibly integrate Google
Maps API if location is relevant (less likely if mentorship is virtual).
• Deployment: Host on a cloud platform (Heroku or AWS) to allow the matching
algorithm and messaging to be tested with multiple users simultaneously.
Deliverables:
• A fully functional website where users can register as mentor/mentee, complete their
profile, and receive a list of suggested matches. Demonstration should show a sample
mentee account getting one or more mentor recommendations and initiating contact.
• Source code with clear documentation on the matching logic (explanation of how
mentors and mentees are paired – e.g., “mentees seeking skill X are matched with
mentors proficient in X and with overlapping availability”). Include any configuration for
weighting certain criteria more heavily in the match score• Database schema documentation (ER diagram) and example data: e.g., show a few
seeded profiles and what a match output looks like for those cases, to illustrate the
system’s function.
• Screenshot or video walkthrough: showing the user interface for profile creation, the
dashboard showing match suggestions, the messaging interface, and scheduling a
meeting.
• Test log or summary: describe how the system was tested – e.g., creating multiple
dummy users with intentional overlapping interests to ensure matches occur correctly,
and testing that messages and session bookings function as intended.
• User guide and admin guide: how a user should use the platform to find a mentor or
mentee, and instructions for an admin to monitor or adjust the system (like removing a
user or broadcasting announcements to all users about mentorship events).
• Privacy and ethics note in documentation: how the platform ensures mentor/mentee
communications remain professional, and how data (like chat logs or personal info) is
protected or could be deleted if a user leaves the platform.
Team Size: 3 students. One student can handle the frontend design and user
experience (creating intuitive profile forms and messaging UI), another can focus on
the backend logic including the matching algorithm and API endpoints, and the
third can work on features integration such as real-time messaging and scheduling
components (as well as overall testing and deployment). Collaboration is key as
profile data and matching logic will influence the UI design.
Estimated Effort: Minimum 120 hours per student, encompassing full-stack
development (from database design to polished UI), complex matchmaking
implementation, and thorough user testing to ensure accurate pairings and a
smooth communication experience.
Title: Project 4: Online Mentorship Matching Platform
Description: Develop a web-based platform that connects mentors and mentees for knowledge sharing and career or academic guidance. Users can sign up either as a
mentor (offering expertise in certain skills or subjects) or as a mentee (seeking guidance in specific areas). The system will collect profiles and preferences, then use
a matching algorithm to suggest optimal mentor-mentee pairings. Once matched, the platform provides communication tools such as messaging or session scheduling to facilitate the mentoring relationship. This project is software-centric, focusing on
matchmaking logic and social networking features, and addresses a real need for structured mentorship in academic and professional communities. (Building this
involves implementing user profile management, matching algorithms, and communication features – a solid full-stack development challenge.)
Objectives:
Design profile forms for mentors and mentees to capture relevant data: for mentors, their expertise, background, availability, and for mentees, their goals, interests, and
areas where they seek help. Include tags or categories (e.g., “Data Science”, “Career
Advice”, “Web Development”) to assist in matching. Implement a matching
algorithm that pairs mentees with suitable mentors. This could be rule-based
(matching on common tags and availability) and/or use a scoring system (e.g., assign
points for each shared interest or desired skill and match the highest scores).
Optionally, incorporate a collaborative filtering or recommendation approach if
historical mentorship ratings data is available (though likely not for a new system).
Provide a communication channel for mentors and mentees: at minimum, an inplatform messaging system to chat and discuss. Additionally, allow scheduling of
mentorship sessions (with a calendar view or integration to set up meeting times)
and possibly email notifications or reminders for scheduled sessions. Include a
feedback mechanism: after some sessions, mentees can rate or review the
mentorship experience. This helps in refining future matches and giving recognition
to mentors. Administrative dashboard: allow admins or program coordinators to
oversee active mentorships, intervene if someone isn’t engaged, and gather statistics
(number of active pairs, average rating, etc.). Ensure privacy and safety: protect
personal contact info (so initial communication is on the platform), and include
terms of conduct. Possibly implement content moderation on messages or a
reporting system for any issues in the mentor-mentee interactions.
Objectives:
Frameworks/Tools/Technologies:• Backend: Use a web framework like Ruby on Rails, Django, or Express (Node.js) to
handle user accounts, profiles, and the matching logic on the server side. A relational
database (PostgreSQL/MySQL) will store user profiles, messages, and match
information.
• Frontend: Utilize HTML/CSS/JavaScript with a modern library (React or Vue.js) for a
responsive user interface. The interface will include profile pages, match suggestions,
messaging UI, and scheduling interface.
• Matching Algorithm: Implement in backend using either pure code (Python or Node) or
possibly an existing library if using something like Python’s scikit-learn for a more
advanced recommendation approach. However, a custom algorithm tuned to the profile
schema is likely sufficient.
• Messaging: Integrate real-time messaging using WebSockets (Socket.io for Node or
Django Channels for Python) so that messages appear instantly. Alternatively, use a
ready chat service or an email notification fallback if real-time is too complex.
• Scheduling: Implement a simple calendar component (e.g., using a library like
FullCalendar in JavaScript) for mentors to indicate available slots and mentees to request
meetings. Use an email API (like SendGrid) to send confirmation or reminder emails for
scheduled sessions.
• Authentication & Security: Use OAuth if integrating with other systems (or at least
allow sign-in with Google/LinkedIn to import basic profile data). Use HTTPS, secure
password storage, and form validation to protect user data. Possibly integrate Google
Maps API if location is relevant (less likely if mentorship is virtual).
• Deployment: Host on a cloud platform (Heroku or AWS) to allow the matching
algorithm and messaging to be tested with multiple users simultaneously.
Deliverables:
• A fully functional website where users can register as mentor/mentee, complete their
profile, and receive a list of suggested matches. Demonstration should show a sample
mentee account getting one or more mentor recommendations and initiating contact.
• Source code with clear documentation on the matching logic (explanation of how
mentors and mentees are paired – e.g., “mentees seeking skill X are matched with
mentors proficient in X and with overlapping availability”). Include any configuration for
weighting certain criteria more heavily in the match score• Database schema documentation (ER diagram) and example data: e.g., show a few
seeded profiles and what a match output looks like for those cases, to illustrate the
system’s function.
• Screenshot or video walkthrough: showing the user interface for profile creation, the
dashboard showing match suggestions, the messaging interface, and scheduling a
meeting.
• Test log or summary: describe how the system was tested – e.g., creating multiple
dummy users with intentional overlapping interests to ensure matches occur correctly,
and testing that messages and session bookings function as intended.
• User guide and admin guide: how a user should use the platform to find a mentor or
mentee, and instructions for an admin to monitor or adjust the system (like removing a
user or broadcasting announcements to all users about mentorship events).
• Privacy and ethics note in documentation: how the platform ensures mentor/mentee
communications remain professional, and how data (like chat logs or personal info) is
protected or could be deleted if a user leaves the platform.
Team Size: 3 students. One student can handle the frontend design and user
experience (creating intuitive profile forms and messaging UI), another can focus on
the backend logic including the matching algorithm and API endpoints, and the
third can work on features integration such as real-time messaging and scheduling
components (as well as overall testing and deployment). Collaboration is key as
profile data and matching logic will influence the UI design.
Estimated Effort: Minimum 120 hours per student, encompassing full-stack
development (from database design to polished UI), complex matchmaking
implementation, and thorough user testing to ensure accurate pairings and a
smooth communication experience.