GRADING MULTIPLE CHOICE QUESTIONS BASED ON PREPARED QUESTIONS AND OPTIONS BOOKMARKS IN BUBBLE SHEET
The process of evaluating students’ answers is a time consuming and effort for teachers, therefore, based on this, Grading Multiple Choice Questions (G-MCQ) is proposed to auto-marking answer without human interaction. All the human does, is to use digital camera without using expensive ordinary document scanner and machine-read for this purpose, then, evaluating and marking each correct answer is algorithm duty. G-MCQ is based on a prepared bubble sheet that contains (54) questions with four circles options for each question, G-MCO is programmed using Python programming language, passes three main process , the first one, is a preparation of scanned document, then, second one, is to detect bookmarks, First Question Bookmark (FQB), Questions Bookmarks (QB) and Options Bookmarks (OB) positions, based on detecting FQB, QB and OB, the final one is started to detect answers which are circles positions of each question from instructor. The algorithm is tested with input images with PNG and JPG format, the result of detecting of accuracy is about 99%
Barik, D., & Mondal, M. (2010). Object identification for computer vision using image segmentation. 2010 2nd International Conference on Education Technology and Computer, 2, V2-170-V2-172. https://doi.org/10.1109/ICETC.2010.5529412
Cardona, M. (2016). Sugar crystals characterization for quality control inspection using digital image processing. 2016 IEEE 36th Central American and Panama Convention (CONCAPAN XXXVI), 1–6. https://doi.org/10.1109/CONCAPAN.2016.7942378
Chinnasarn, K., & Rangsanseri, Y. (1999). Image-processing-oriented optical mark reader. Applications of Digital Image Processing XXII, 3808, 702–708. https://doi.org/10.1117/12.365883
de Assis Zampirolli, F., Gonzalez, J. A. Q., & de Oliveira Neves, R. P. (2010). Automatic correction of multiple-choice tests using digital cameras and image processing. Universidade Federal Do ABC.
Fisteus, J. A., Pardo, A., & García, N. F. (2013). Grading Multiple Choice Exams with Low-Cost and Portable Computer-Vision Techniques. Journal of Science Education and Technology, 22(4), 560–571. https://doi.org/10.1007/s10956-012-9414-8
Kulkarni, R., Kulkarni, S., Dabhane, S., Lele, N., & Paswan, R. S. (2019). An Automated Computer Vision Based System for Bottle Cap Fitting Inspection. 2019 Twelfth International Conference on Contemporary Computing (IC3), 1–5. https://doi.org/10.1109/IC3.2019.8844942
Sharma, P., Diwakar, M., & Lal, N. (2013). Edge Detection using Moore Neighborhood. International Journal of Computer Applications, 61, 26–30. https://doi.org/10.5120/9910-4506
Spadaccini, A., & Rizzo, V. (2011). A multiple-choice test recognition system based on the gamera framework. ArXiv Preprint ArXiv:1105.3834.
Ugwu, E. M., Taylor, O. E., & Nwiabu, N. D. (2022). An Improved Visual Attention Model for Automated Vehicle License Plate Number Recognition Using Computer Vision. European Journal of Artificial Intelligence and Machine Learning, 1(3), Article 3. https://doi.org/10.24018/ejai.2022.1.3.10
Yimyam, W., & Ketcham, M. (2018). The Grading Multiple Choice Tests System via Mobile Phone using Image Processing Technique. International Journal of Emerging Technologies in Learning (IJET), 13(10), 260–269.
It is the policy of the Journal of Duhok University to own the copyright of the technical contributions. It publishes and facilitates the appropriate re-utilize of the published materials by others. Photocopying is permitted with credit and referring to the source for individuals use.
Copyright © 2017. All Rights Reserved.