Automated Essay Grading System
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Universiti Malaysia Sarawak (UNIMAS)
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Grading an essay for pre-university students is not an easy task particularly for student who takes Malaysian University English Test (MUET). The lecturers at Universiti Malaysia Sarawak (UNIMAS) faces significant challenges due to its labor-intensive nature and potential inconsistencies in manual grading. This project proposes an Automated Essay Grading System that leverages Natural Language Processing (NLP) and Artificial Intelligence (AI) technologies to streamline the grading process. The system is developed using Feature-Driven Development (FDD) methodology, incorporating requirements gathered through interviews with a FELC lecturers. The proposed system includes key features such as automated grading based on MUET-specific rubrics, detailed feedback generation, and a user-friendly interface for digital essay submission and result review. Moreover, the analysis of existing systems revealed limitations in current solutions, particularly in providing detailed feedback and maintaining consistency with specific examination rubrics. A survey is conducted, including both lecturers and students, indicated positive reception towards AI integration in essay grading, while highlighting concerns about accuracy and bias. The proposed system can offer a platform for the FELC lecturers to quickly analyse students’ essays, provide more personalized feedback, and also supporting students’ progress in developing their writing skills. Based on testing with a MUET language lecturer and pilot tester, the system was found to be functionally reliable, and the AI-generated feedback was considered useful for formative learning.