Skill-Share by ZenWare
Team
- E/23/035, Irusha Bandara, email
- E/23/104, Poorna Gamage, email
- E/23/430, Hiruni Weerasinghe, email
- E/23/317, Sashika Rathnayake, email
Table of Contents
Introduction
In today’s academic environments, students often possess valuable skills but lack a structured way to share them, leading to underutilized peer knowledge. Current methods for finding mentors or study partners are inefficient, relying on manual coordination and failing to solve complex scheduling conflicts. Skill-Share by ZenWare addresses this by providing a campus-oriented, full-stack platform that uses smart algorithms to connect students.
The solution goes beyond simple searching; it identifies multi-user “skill cycles” (where Student A teaches B, and B teaches C) and automatically syncs free time slots to eliminate scheduling headaches. By integrating a Trust Score System and real-time communication, it builds a reliable learning community.
The impact is a more connected university ecosystem where learning is accessible and collaborative. It empowers students to trade knowledge as a resource, reducing reliance on expensive external tutoring and maximizing the collective potential of the student body.
Solution Architecture
The platform is engineered to solve a specific campus problem: the “hidden” skills of university students. The architecture transitions users from an inefficient “As-Is” state (relying on WhatsApp, paid materials, or unreliable AI) to a secure, centralized peer-to-peer network. At a high level, the system utilizes JWT authentication for secure access, connecting users through a live auto-suggest search engine (by user or skill) and managing interactions via an automated availability and notification system.
Software Designs
Our software design centers on empowering the user rather than forcing automated matches. We originally designed a “Skill-Cycle” algorithm, but identified critical edge cases: unacceptable wait times, infinite relationship loops, and the restriction that a user must both teach and learn. To resolve this, we pivoted to a decentralized Credit and Reputation Economy.
Core Mechanics: Users manage their own availability and book individual sessions.
The Economy: To learn a skill, users spend credits. To earn credits, they are incentivized to teach others. This gamifies the peer-to-peer process and completely eliminates the bottleneck of waiting for a perfect “match cycle.”
Two-Way Feedback: Completed sessions use a dual-feedback submission design to calculate a reliable user reputation score.
Testing
Testing focused heavily on our core custom logic and security. We executed comprehensive unit and integration tests to ensure that our JWT security filters, real-time Notification system, and complex Credit Score economy (ensuring credits are correctly deducted or awarded during bookings) functioned flawlessly under various edge cases.
Conclusion
We successfully achieved our MVP goal: providing a functional, trustworthy platform to unlock hidden campus skills.
Achieved: We delivered a fully functioning system complete with secure login/signup, user dashboards, live skill searching, individual session booking, credit/reputation scoring, and a two-way feedback system. We also successfully navigated our biggest technical hurdle by pivoting from a flawed matching algorithm to a robust credit economy.
Future Developments (Semester 4 Plan): We plan to expand the platform’s commercial and community value by introducing Group Sessions, a Course Pool, and a Real-time Chat system. We will also heavily gamify the experience (Experience badges, Newsfeed celebrations, Online store) and improve accessibility via Google integrations (Signup & Calendar), cross-platform web-app capabilities, and a dedicated Admin authorization tier.