Development of an Offline Android-based Test Paper Checker Application for Northwestern Visayan Colleges
DOI:
https://doi.org/10.69478/JITC2024v6n3a04Keywords:
Automated Grading, Optical Character Recognition (OCR), Item Analysis, Offline Test Paper CheckerAbstract
This research introduces an offline Android-based Test Paper Checker application designed to automate and streamline the grading process in institutions like Northwestern Visayan Colleges. Using optical character recognition (OCR) and machine learning, the app accurately digitizes and scores handwritten test responses, improving grading speed and accuracy. Key features include student registration, exam creation, paper scanning, correction key generation, and item analysis for question performance. The app functions offline, ensuring accessibility without internet connectivity. By reducing manual errors and processing times, it supports educators in delivering faster feedback and aligns with UNSDGs Goal 4 (Quality Education) and Goal 9 (Innovation).
References
H. E. Ascencio, C. F. Peña, K. R. Vásquez, M. Cardona, S. Gutiérrez, “Automatic Multiple Choice Test Grader using Computer Vision,” in Proc. IEEE Mexican Humanitarian Technology Conference (MHTC), Puebla, Mexico, April 21-22, 2021, https://doi.org/10.1109/MHTC52069.2021.9419920.
L. D. Largo, J. Guillermo, A. R. Jancinal, M. Wata, “Bubble Sheet Multiple Choice Mobile Checker with Test Grader using Optical Mark Recognition (OMR) Algorithm,” In Proc. 5th International Conference on Electronics and Electrical Engineering Technology (EEET), Beijing, China, December 2-4, 2022, https://doi.org/10.1109/EEET58130.2022.00013.
M. G. Wata and J. F. Villaverde, “Bubble-Sheet Assessment Checker with Test Grader Using Computer Vision Through Raspberry Pi,” in Proc. IEEE 6th International Conference on Computer and Communication Engineering Technology (CCET), Beijing, China, August 4-6, 2023, https://doi.org/10.1109/CCET59170.2023.10335127.
H. A. Alhamad, M. Shehab, M. K. Y. Shambour, M. A. Abu-Hashem, A. Abuthawabeh, H. Al-Aqrabi, M. S. Daoud, F. B. Shannaq, “Handwritten Recognition Techniques: A Comprehensive Review,” Symmetry, vol. 16, no. 6, June 2024, https://doi.org/10.3390/sym16060681.
S. Mondal, P. De, S. Malakar, R. Sarkar, “OMRNet: A lightweight Deep Learning Model for Optical Mark Recognition,” Multimedia Tools and Applications, vol. 83, July 2023, pp. 14011-14045, https://doi.org/10.1007/s11042-023-15408-8.
Y. Weng and C. Xia, “A New Deep Learning-Based Handwritten Character Recognition System on Mobile Computing Devices,” Mobile Networks and Applications, vol. 25, no. 2, March 2019, pp. 402-411, https://doi.org/10.1007/s11036-019-01243-5.
Downloads
Published
Issue
Section
Categories
License
Copyright (c) 2024 Joseph Z. Masula, Cybelle Justin Arsenio, Naomi J. Flores, Elamie Z. Nacasabug, Mariamie L. Tamboong, Krystaly N. Geralde
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.