iGrade: an automated short answer grading system
Dina H. Alhamed, A. Al-Ajmi, Yasminah Hani Alali, Tayma Abdulrahman Alqahtani, Maryam Riyadh Alnassar, Dina A. Alabbad · 2022
During the COVID-19 pandemic, most countries rely on E-Learning to apply social distance policy which affects the exams evaluation process. This project aimed to assist instructors in grading the short answer questions for CCSIT courses. By implanting a website application that the instructors could use to upload the students' answers and the ‘iGrade” software model will grade it. Moreover, the system will reduce the workload on the facilities members by saving time and effort as well as guarantee an objective grading for students. The model used in this project is a state-of-the-art BERT Neural Network model along with layers of BiLSTM that was trained using a dataset that has been collected from previous midterm and final exams of the CIS 211 course. The dataset consists of three categories which are (0, 0.5, 1) with around 1,128 instances. The "iGrade" test obtained an accuracy score of 85,4%, demonstrating BERT's superiority and independence from features during short answer grading as a default method in NLP.