Smart Grading: A Mobile OMR Scanner App for Lecturers in UNIMAS
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Universiti Malaysia Sarawak
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The proposed project aims to simplify and modernise the grading of Optical Mark Recognition (OMR) answer sheets through a mobile application tailored for lecturers. Traditional methods of grading OMR papers often rely on manual processes or expensive hardware, which are time-consuming and inefficient. Smart Grading: A Mobile OMR Scanner App for Lecturers addresses this issue by leveraging mobile technology and image processing techniques to automate the grading process. The system is capable of scanning answer papers, detecting and correcting alignment, and evaluating results in real time. Developed using the Agile methodology, the application includes core features such as quiz creation, answer key setup, and performance analysis. The system was implemented using FlutterFlow for the mobile frontend, Python with OpenCV for backend image processing, and Firebase for data handling and authentication. Extensive functional testing was conducted using detailed test cases to validate individual features, while integration and image processing tests ensured the reliability of the grading workflow. User Acceptance Testing (UAT) was also carried out through SUS, UEQ, and open-ended feedback to assess overall usability. The project concludes with recommendations for future improvements such as full-paper scanning, offline grading, and extended support for subjective questions. Overall, the Smart Grading system offers an efficient, accessible, and scalable solution for automating academic assessments.