To grade hackathon entries based on coherence, we can define the following set of types:
// Hackathon Entry export type HackathonEntry = { projectId: string; projectName: string; problemStatement: string; solutionDescription: string; implementationDescription: string; technologyStack: string[]; teamMembers: TeamMember[]; coherenceScore: number; evaluationRemarks: string; }
// Team Member type TeamMember = { name: string; email: string; role: string; }
With these types, we can create a database schema to store the hackathon entries. The schema can have a collection/table for Hackathon Entries where each document/row represents an individual entry. Each document/row will contain fields/columns for the properties defined in the HackathonEntry type.
Example MongoDB schema:
// Hackathon Entries Collection const hackathonEntries = [ { projectId: "1", projectName: "Decentralized Health Monitoring", problemStatement: "Lack of access to real-time health monitoring in remote areas", solutionDescription: "A decentralized app that connects IoT devices to monitor health data", technologyStack: ["Blockchain", "IoT", "Cloud Computing"], teamMembers: [ { name: "John Doe", email: "[email protected]", role: "Developer" }, { name: "Jane Smith", email: "[email protected]", role: "Designer" } ], coherenceScore: 85, evaluationRemarks: "Good problem statement, well-defined solution, impressive technology stack" }, // Other entries... ]
With this schema, we can store and query hackathon entries, and perform coherence grading by assigning a coherence score and providing evaluation remarks for each entry.