EnQueue
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2025
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Abstract
This project presents a web-based academic support platform integrated with Canvas via LTI and API, built using FastAPI and PostgreSQL. It provides a unified interface for managing student help queues, TA schedules, course rosters, assignments, and FAQs. With features like role-based access, TA location tracking, in-queue student games, and TA rating systems, the platform aims to streamline academic support in large university courses. By combining structured course data with real-time interactions, it improves responsiveness, reduces redundancy, and enhances the learning experience for students and teaching assistants alike.
Description
This project is a full-stack web application designed to enhance academic support in large-scale university courses by integrating with the Canvas LMS through LTI and the Canvas API. Built with FastAPI and PostgreSQL, the platform allows students to join real-time help queues, interact with teaching assistants (TAs), and receive targeted support based on their enrolled courses and assignments.
The system manages users, courses, rosters, and TA-specific metadata like availability and office hours. Assignments and FAQs are stored and linked to specific courses, giving students quick access to relevant help materials. TAs can view and manage their queue, receive feedback through a rating system, and handle student interactions efficiently. The platform also supports in-queue engagement through optional lightweight games to improve the student experience during wait times.
Overall, the project reduces redundancy in TA responses, improves queue visibility, and enhances communication between students and course staff—all within a Canvas-integrated environment.