This project is a machine learning (ML) based assignment management system, written in Python. It aims to solve the problem of assignment dumps in a limited span by introducing a global calendar for teachers with ML generated deadline suggestions based on students' data sets and other subject workloads. It also aims to improve student progress by providing model-generated assessments that are purely for improving core knowledge of the subject.
- Python 3.x
- Required libraries: pandas, numpy, sklearn, flask
Assignment dumps in a limited span are quite taxing on students. The main reason for this is the lack of transparency between subjects and untracked progress, leading to workflow conflicts and a hit on efficiency. This project aims to solve both of these problems by introducing a global calendar for teachers with machine learning (ML) generated deadline suggestions based on students' data sets and other subject workloads. It also aims to improve student progress by providing model-generated assessments that are purely for improving core knowledge of the subject.
This project is designed to be used by both students and teachers. The teacher side of the platform allows teachers to create and upload marked assignments, view the global calendar, and see a home feed with the latest posted assessments, along with ML generated ones. The student side of the platform includes a student home feed with the latest posted assessments, a due date, and estimated completion time, deadline notifications, and a distinct color-coded subject calendar.
- We chose to use Python as the primary programming language for this application because of its popularity and wide range of libraries for machine learning and web development.
- we used the Flask web framework to build the web interface of the application.