Front-End for a Semi-Automated Grading Tool

Abstract

Motivated by the shift towards online teaching, an application which semi-automates the grading of long-form questions would not only save time but could be more accurate. The education sector frequently grapples with the challenge of efficiently grading a large volume of student submissions. Addressing this challenge, this project developed the front-end interface of a semi-automated grading tool that promises to streamline the grading process, thereby enhancing efficiency and reducing potential biases. The foundation of this tool's evaluation mechanism is built upon the utilization of four distinct distance metrics, each meticulously selected to calculate the disparity between student submissions. Questions and submissions can be imported directly into the application, and once the grading has been completed, they can be downloaded and exported. To cater to a dynamic and interactive user experience, the project leverages the capabilities of Next.js, a robust React framework known for its performance and scalability. This framework was selected not only for its rich set of features but also for its server-rendering capabilities, which will facilitate the real-time processing of data, critical for instantaneous feedback during the grading process. This application provides an education tool that can be used by lecturers to help better utilise their time, spending less time marking and more time teaching.