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Görüntü İşleme Teknikleri ve Kelime Benzerliklerinin Ağırlıkları Algoritması Kullanılarak Geleneksel Sınavların Değerlendirilmesi

Year 2022, , 127 - 139, 28.06.2022

Yahya Gedik , Serdar Solak , Mustafa Hikmet Bilgehan Uçar

https://doi.org/10.26650/acin.1009226

Abstract

Teknolojide yaşanan hızlı gelişmeler eğitim alanına da yansımıştır. Eğitim kurumları, bilgisayar destekli olarak verdikleri eğitimleri çevrim içi veya çevrim dışı platformlarla desteklemeye başlamışlardır. Bu platformların kullanılması ile yapılan sınavların değerlendirilmesi sistem tarafından hızlı bir şekilde gerçekleştirilebilmektedir. Diğer taraftan, öğrencilere geleneksel yöntemler kullanılarak sınıf ortamında test, klasik, eşleştirmeli ve doğru yanlış tiplerinde soruları içeren sınavlar da uygulanmaktadır. Özellikle, sınavın sadece çoktan seçmeli sorulardan oluşmadığı durumlarda, değerlendirme işlemi uzun zaman almaktadır. Bu kapsamda yapılacak değerlendirmelerin doğru, güvenilir ve hızlı bir şekilde yürütülmesi tasarlanacak otomatikleşmiş akıllı uzman sistemlerle mümkündür. Bu çalışmada, sınıf ortamında gerçekleştirilen geleneksel sınavların hızlı ve güvenilir bir şekilde değerlendirilmesi için önerilen kelime benzerliklerinin ağırlıkları algoritması (KBAA) ve görüntü işleme tekniklerini kullanan bir yazılım geliştirilmiştir. Bu yazılım ile sınav kâğıtları üzerinde yer alan soru türlerinin ayrıştırılması, soru ve cevap ayrımı, el yazılarının tanımlanması ve cevapların değerlendirilmesi görüntü işleme ve KBAA kullanılarak gerçekleştirilmektedir. Deneysel çalışmalar sonucunda, sadece bir sınav kağıdının değerlendirilmesi saniyeler içerisinde gerçekleştirilirken sınavda yer alan tüm kağıtların toplu bir şekilde değerlendirilmesi ise değerlendirilen kâğıt başına yaklaşık 4 saniyede gerçekleştirilmektedir. Geliştirilen bu yazılım sayesinde geleneksel sınavların değerlendirilmesi hızlı, güvenilir, verimli ve adil bir şekilde gerçekleştirilebilmektedir.

Keywords

El Yazısı Tanıma Görüntü İşleme Optik Karakter Tanıma Sınav Değerlendirme Yapay Zekâ

References

  • Bloomfield, A. (2010, October). Evolution of a digital paper exam grading system. In 2010 IEEE Frontiers in Education Conference (FIE) (pp. T1G-1). IEEE. google scholar
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  • Chi, Z., & Zhang, B. (2018). A sentence similarity estimation method based on improved siamese network. Journal of Intelligent Learning Systems and Applications, 10(4), 121-134. google scholar
  • Clark, E., Celikyilmaz, A., & Smith, N. A. (2019, July). Sentence mover’s similarity: Automatic evaluation for multi-sentence texts. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (pp. 2748-2760). google scholar
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  • Gao, Y., Jin, L., He, C., & Zhou, G. (2011, September). Handwriting character recognition as a service: A new handwriting recognition system based on cloud computing. In 2011 International Conference on Document Analysis and Recognition (pp. 885-889). IEEE. google scholar
  • Fowler, M., Chen, B., Azad, S., West, M., & Zilles, C. (2021, March). Autograding” Explain in Plain English” questions using NLP. In Proceedings of the 52nd ACM Technical Symposium on Computer Science Education (pp. 1163-1169). google scholar
  • Gomes Rocha F., Rodriguez G., Andrade E.E.F., Guimaraes A., Gonçalves V., Sabino R.F. (2021) Supervised Machine Learning for Automatic Assessment of Free-Text Answers. In: Batyrshin I., Gelbukh A., Sidorov G. (eds) Advances in Soft Computing. MICAI 2021. Lecture Notes in Computer Science, vol 13068. Springer, Cham. https://doi.org/10.1007/978-3-030-89820-5_1. google scholar
  • Ha, L., Yaneva, V., Harik, P., Pandian, R., Morales, A., & Clauser, B. (2020). Automated prediction of examinee proficiency from short-answer questions. Proceedings of the 28th International Conference on Computational Linguistics, pages 893-903 Barcelona, Spain (Online), December 8-13, 2020. google scholar
  • Lagler, K., Schindelegger, M., Böhm, J., Krasna, H., & Nilsson, T. (2013). GPT2: Empirical slant delay model for radio space geodetic techniques. Geophysical research letters, 40(6), 1069-1073. google scholar Lee, J. S., & Hsiang, J. (2020). Patent claim generation by fine-tuning OpenAI GPT-2. World Patent Information, 62, 101983. google scholar
  • Liu, T., Ding, W., Wang, Z., Tang, J., Huang, G. Y., & Liu, Z. (2019, June). Automatic short answer grading via multiway attention networks. In International conference on artificial intelligence in education (pp. 169-173). Springer, Cham. google scholar
  • Liu, Z., Xu, Z., Escalera, S., Guyon, I., Junior, J. C. J., Madadi, M., ... & Tu, W. W. (2020). Towards automated computer vision: analysis of the AutoCV challenges 2019. Pattern Recognition Letters, 135, 196-203. google scholar
  • Llamas-Nistal, M., Fernandez-Iglesias, M. J., Gonzalez-Tato, J., & Mikic-Fonte, F. A. (2013). Blended e-assessment: Migrating classical exams to the digital world. Computers & Education, 62, 72-87. google scholar
  • Marti, U. V., & Bunke, H. (2002). The IAM-database: an English sentence database for offline handwriting recognition. International Journal on Document Analysis and Recognition, 5(1), 39-46. google scholar
  • McGill, T. J., & Klobas, J. E. (2009). A task-technology fit view of learning management system impact. Computers & Education, 52(2), 496-508. google scholar
  • Nemeth, B., & Tejfel, M. (2016, May). markfactory: Translation-based automatic exam evaluation for mass education. Proceedings of the 11th Joint Conference on Mathematics and Computer Science, Eger, Hungary google scholar
  • Plamondon, R., & Srihari, S. N. (2000). Online and off-line handwriting recognition: a comprehensive survey. IEEE Transactions on pattern analysis and machine intelligence, 22(1), 63-84. google scholar
  • Plötz, T., & Fink, G. A. (2009). Markov models for offline handwriting recognition: a survey. International Journal on Document Analysis and Recognition (IJDAR), 12(4), 269. google scholar
  • Rajala, T., Kaila, E., Linden, R., Kurvinen, E., Lokkila, E., Laakso, M. J., & Salakoski, T. (2016, February). Automatically assessed electronic exams in programming courses. In Proceedings of the Australasian computer science week multiconference (pp. 1-8). google scholar
  • Sanuvala, G., & Fatima, S. S. (2021, February). A Study of Automated Evaluation of Student’s Examination Paper using Machine Learning Techniques. In 2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) (pp. 1049-1054). google scholar
  • Shaukat, Z., Ali, S., Xiao, C., Sahiba, S., & Ditta, A. (2020). Cloud-based efficient scheme for handwritten digit recognition. Multimedia Tools and Applications, 79(39), 29537-29549. google scholar
  • Smith, R. (2007, September). An overview of the Tesseract OCR engine. In Ninth international conference on document analysis and recognition (ICDAR 2007) (Vol. 2, pp. 629-633). IEEE. google scholar
  • Solak, S., Altınışık, U., Yıldız, U., & İnal, M. (2016). Örgün Öğretim Derslerinin Moodle Öğrenme Yönetim Sistemi Kullanılarak Sunulması Deneyimi. Eğitim ve Öğretim Araştırmaları Dergisi, 5(2), 348-360. google scholar
  • Solak, S., Ucar, M. H., & Albadwieh, M. (2020). Computer-based evaluation to assess students’ learning for the multiple-choice question-based exams: CBE-MCQs software tool. Computer Applications in Engineering Education, 28(6), 1406-1420. google scholar
  • Şahin, İ., Uçar, M. H. B., & Solak, S. (2022). Otomatik Türkçe Kartvizit Tanıma için Bulut Tabanlı WEB Uygulama Tasarımı ve Performans Değerlendirmesi. Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji, 10(1), 118-134. google scholar
  • Tashu, T. M., Esclamado, J. P., & Horvath, T. (2019, June). Intelligent on-line exam management and evaluation system. In International Conference on Intelligent Tutoring Systems (pp. 105-111). Springer, Cham. google scholar
  • Tomic, S., Paunovic, V., & Bosnic, I. Computer-based question and exam evaluation in summative knowledge assessment. In 2020 43rd International Convention on Information, Communication and Electronic Technology (MIPRO) (pp. 1520-1525). IEEE. google scholar
  • Villalon, J. (2012, July). An eMarking tool for paper based evaluations. In 2012 IEEE 12th International Conference on Advanced Learning Technologies (pp. 43-45). IEEE. google scholar
  • Vij, S., Tayal, D., & Jain, A. (2020). A machine learning approach for automated evaluation of short answers using text similarity based on WordNet graphs. Wireless Personal Communications, 111(2), 1271-1282. google scholar
  • Walvoord, B. E., & Anderson, V. J. (2011). Effective grading: A tool for learning and assessment in college. John Wiley & Sons. google scholar
  • Yorke, M. (2009). Faulty signals? Inadequacies of grading systems and a possible response. In Assessment, learning and judgement in higher education (pp. 1-20). Springer, Dordrecht. google scholar

Grading Traditional Exams Using Image Processing Techniques and the Word Similarity Weights Algorithm

Year 2022, , 127 - 139, 28.06.2022

Yahya Gedik , Serdar Solak , Mustafa Hikmet Bilgehan Uçar

https://doi.org/10.26650/acin.1009226

Abstract

The rapid developments in technology have also been reflected in education. Educational institutions have started to support their computer-aided trainings with online and offline platforms. Exam evaluations can be carried out quickly by the system using these platforms. Meanwhile, exams containing test, classical, matching, and true-false types of questions are also applied to students in the classroom environment using traditional methods. The evaluation process for these takes a long time, especially when an exam has more than just multiple-choice questions. In this context, carrying out evaluations to be made accurately, reliably, and quickly becomes possible with the intelligent expert systems being designed. Using the word similarity weights algorithm (WSWA) and image processing techniques, this study develops software for quickly and reliably evaluating the traditional exams held in classrooms. By means of the proposed software, question types are separated, answers are distinguished from questions, handwriting is identified, and answers are evaluated on the exam papers using image processing techniques and WSWA. As a result of the experimental studies, the evaluation of just one exam paper is carried out in seconds, while the collective evaluation of all the papers in the exam is carried out in approximately 4 seconds per paper being evaluated. Thanks to this software, traditional exams can be evaluated quickly, efficiently, and accurately.

Keywords

Image Processing Artificial Intelligence Handwriting Recognition Optical Character Recognition Exam Assessment

References

  • Bloomfield, A. (2010, October). Evolution of a digital paper exam grading system. In 2010 IEEE Frontiers in Education Conference (FIE) (pp. T1G-1). IEEE. google scholar
  • Bradski, G., & Kaehler, A. (2008). Learning OpenCV: Computer vision with the OpenCV library. “ O’Reilly Media, Inc.”. google scholar
  • Chi, Z., & Zhang, B. (2018). A sentence similarity estimation method based on improved siamese network. Journal of Intelligent Learning Systems and Applications, 10(4), 121-134. google scholar
  • Clark, E., Celikyilmaz, A., & Smith, N. A. (2019, July). Sentence mover’s similarity: Automatic evaluation for multi-sentence texts. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (pp. 2748-2760). google scholar
  • Çelik, A., (2020). Optik karakter tanımada hata yayılım algoritmalarının performans kıyaslaması. Journal of the Institute of Science and Technology, 10(4), 2328-2340. google scholar
  • Çelik, A., (2021). Eğik Karakter Tanıma Başarısını Arttırmak için Yeni Bir Yöntemin Kullanılması. Harran Üniversitesi Mühendislik Dergisi, 6(1), 1-11. google scholar
  • Gao, Y., Jin, L., He, C., & Zhou, G. (2011, September). Handwriting character recognition as a service: A new handwriting recognition system based on cloud computing. In 2011 International Conference on Document Analysis and Recognition (pp. 885-889). IEEE. google scholar
  • Fowler, M., Chen, B., Azad, S., West, M., & Zilles, C. (2021, March). Autograding” Explain in Plain English” questions using NLP. In Proceedings of the 52nd ACM Technical Symposium on Computer Science Education (pp. 1163-1169). google scholar
  • Gomes Rocha F., Rodriguez G., Andrade E.E.F., Guimaraes A., Gonçalves V., Sabino R.F. (2021) Supervised Machine Learning for Automatic Assessment of Free-Text Answers. In: Batyrshin I., Gelbukh A., Sidorov G. (eds) Advances in Soft Computing. MICAI 2021. Lecture Notes in Computer Science, vol 13068. Springer, Cham. https://doi.org/10.1007/978-3-030-89820-5_1. google scholar
  • Ha, L., Yaneva, V., Harik, P., Pandian, R., Morales, A., & Clauser, B. (2020). Automated prediction of examinee proficiency from short-answer questions. Proceedings of the 28th International Conference on Computational Linguistics, pages 893-903 Barcelona, Spain (Online), December 8-13, 2020. google scholar
  • Lagler, K., Schindelegger, M., Böhm, J., Krasna, H., & Nilsson, T. (2013). GPT2: Empirical slant delay model for radio space geodetic techniques. Geophysical research letters, 40(6), 1069-1073. google scholar Lee, J. S., & Hsiang, J. (2020). Patent claim generation by fine-tuning OpenAI GPT-2. World Patent Information, 62, 101983. google scholar
  • Liu, T., Ding, W., Wang, Z., Tang, J., Huang, G. Y., & Liu, Z. (2019, June). Automatic short answer grading via multiway attention networks. In International conference on artificial intelligence in education (pp. 169-173). Springer, Cham. google scholar
  • Liu, Z., Xu, Z., Escalera, S., Guyon, I., Junior, J. C. J., Madadi, M., ... & Tu, W. W. (2020). Towards automated computer vision: analysis of the AutoCV challenges 2019. Pattern Recognition Letters, 135, 196-203. google scholar
  • Llamas-Nistal, M., Fernandez-Iglesias, M. J., Gonzalez-Tato, J., & Mikic-Fonte, F. A. (2013). Blended e-assessment: Migrating classical exams to the digital world. Computers & Education, 62, 72-87. google scholar
  • Marti, U. V., & Bunke, H. (2002). The IAM-database: an English sentence database for offline handwriting recognition. International Journal on Document Analysis and Recognition, 5(1), 39-46. google scholar
  • McGill, T. J., & Klobas, J. E. (2009). A task-technology fit view of learning management system impact. Computers & Education, 52(2), 496-508. google scholar
  • Nemeth, B., & Tejfel, M. (2016, May). markfactory: Translation-based automatic exam evaluation for mass education. Proceedings of the 11th Joint Conference on Mathematics and Computer Science, Eger, Hungary google scholar
  • Plamondon, R., & Srihari, S. N. (2000). Online and off-line handwriting recognition: a comprehensive survey. IEEE Transactions on pattern analysis and machine intelligence, 22(1), 63-84. google scholar
  • Plötz, T., & Fink, G. A. (2009). Markov models for offline handwriting recognition: a survey. International Journal on Document Analysis and Recognition (IJDAR), 12(4), 269. google scholar
  • Rajala, T., Kaila, E., Linden, R., Kurvinen, E., Lokkila, E., Laakso, M. J., & Salakoski, T. (2016, February). Automatically assessed electronic exams in programming courses. In Proceedings of the Australasian computer science week multiconference (pp. 1-8). google scholar
  • Sanuvala, G., & Fatima, S. S. (2021, February). A Study of Automated Evaluation of Student’s Examination Paper using Machine Learning Techniques. In 2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) (pp. 1049-1054). google scholar
  • Shaukat, Z., Ali, S., Xiao, C., Sahiba, S., & Ditta, A. (2020). Cloud-based efficient scheme for handwritten digit recognition. Multimedia Tools and Applications, 79(39), 29537-29549. google scholar
  • Smith, R. (2007, September). An overview of the Tesseract OCR engine. In Ninth international conference on document analysis and recognition (ICDAR 2007) (Vol. 2, pp. 629-633). IEEE. google scholar
  • Solak, S., Altınışık, U., Yıldız, U., & İnal, M. (2016). Örgün Öğretim Derslerinin Moodle Öğrenme Yönetim Sistemi Kullanılarak Sunulması Deneyimi. Eğitim ve Öğretim Araştırmaları Dergisi, 5(2), 348-360. google scholar
  • Solak, S., Ucar, M. H., & Albadwieh, M. (2020). Computer-based evaluation to assess students’ learning for the multiple-choice question-based exams: CBE-MCQs software tool. Computer Applications in Engineering Education, 28(6), 1406-1420. google scholar
  • Şahin, İ., Uçar, M. H. B., & Solak, S. (2022). Otomatik Türkçe Kartvizit Tanıma için Bulut Tabanlı WEB Uygulama Tasarımı ve Performans Değerlendirmesi. Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji, 10(1), 118-134. google scholar
  • Tashu, T. M., Esclamado, J. P., & Horvath, T. (2019, June). Intelligent on-line exam management and evaluation system. In International Conference on Intelligent Tutoring Systems (pp. 105-111). Springer, Cham. google scholar
  • Tomic, S., Paunovic, V., & Bosnic, I. Computer-based question and exam evaluation in summative knowledge assessment. In 2020 43rd International Convention on Information, Communication and Electronic Technology (MIPRO) (pp. 1520-1525). IEEE. google scholar
  • Villalon, J. (2012, July). An eMarking tool for paper based evaluations. In 2012 IEEE 12th International Conference on Advanced Learning Technologies (pp. 43-45). IEEE. google scholar
  • Vij, S., Tayal, D., & Jain, A. (2020). A machine learning approach for automated evaluation of short answers using text similarity based on WordNet graphs. Wireless Personal Communications, 111(2), 1271-1282. google scholar
  • Walvoord, B. E., & Anderson, V. J. (2011). Effective grading: A tool for learning and assessment in college. John Wiley & Sons. google scholar
  • Yorke, M. (2009). Faulty signals? Inadequacies of grading systems and a possible response. In Assessment, learning and judgement in higher education (pp. 1-20). Springer, Dordrecht. google scholar
There are 32 citations in total.

Details

Primary Language Turkish
Subjects Computer Software
Journal Section Research Article
Authors

Yahya Gedik 0000-0001-9014-251X

Serdar Solak 0000-0003-1081-1598

Mustafa Hikmet Bilgehan Uçar 0000-0002-9023-0023

Publication Date June 28, 2022
Submission Date October 14, 2021
Published in Issue Year 2022

Cite

APA Gedik, Y., Solak, S., & Uçar, M. H. B. (2022). Görüntü İşleme Teknikleri ve Kelime Benzerliklerinin Ağırlıkları Algoritması Kullanılarak Geleneksel Sınavların Değerlendirilmesi. Acta Infologica, 6(1), 127-139. https://doi.org/10.26650/acin.1009226
AMA Gedik Y, Solak S, Uçar MHB. Görüntü İşleme Teknikleri ve Kelime Benzerliklerinin Ağırlıkları Algoritması Kullanılarak Geleneksel Sınavların Değerlendirilmesi. ACIN. June 2022;6(1):127-139. doi:10.26650/acin.1009226
Chicago Gedik, Yahya, Serdar Solak, and Mustafa Hikmet Bilgehan Uçar. “Görüntü İşleme Teknikleri Ve Kelime Benzerliklerinin Ağırlıkları Algoritması Kullanılarak Geleneksel Sınavların Değerlendirilmesi”. Acta Infologica 6, no. 1 (June 2022): 127-39. https://doi.org/10.26650/acin.1009226.
EndNote Gedik Y, Solak S, Uçar MHB (June 1, 2022) Görüntü İşleme Teknikleri ve Kelime Benzerliklerinin Ağırlıkları Algoritması Kullanılarak Geleneksel Sınavların Değerlendirilmesi. Acta Infologica 6 1 127–139.
IEEE Y. Gedik, S. Solak, and M. H. B. Uçar, “Görüntü İşleme Teknikleri ve Kelime Benzerliklerinin Ağırlıkları Algoritması Kullanılarak Geleneksel Sınavların Değerlendirilmesi”, ACIN, vol. 6, no. 1, pp. 127–139, 2022, doi: 10.26650/acin.1009226.
ISNAD Gedik, Yahya et al. “Görüntü İşleme Teknikleri Ve Kelime Benzerliklerinin Ağırlıkları Algoritması Kullanılarak Geleneksel Sınavların Değerlendirilmesi”. Acta Infologica 6/1 (June 2022), 127-139. https://doi.org/10.26650/acin.1009226.
JAMA Gedik Y, Solak S, Uçar MHB. Görüntü İşleme Teknikleri ve Kelime Benzerliklerinin Ağırlıkları Algoritması Kullanılarak Geleneksel Sınavların Değerlendirilmesi. ACIN. 2022;6:127–139.
MLA Gedik, Yahya et al. “Görüntü İşleme Teknikleri Ve Kelime Benzerliklerinin Ağırlıkları Algoritması Kullanılarak Geleneksel Sınavların Değerlendirilmesi”. Acta Infologica, vol. 6, no. 1, 2022, pp. 127-39, doi:10.26650/acin.1009226.
Vancouver Gedik Y, Solak S, Uçar MHB. Görüntü İşleme Teknikleri ve Kelime Benzerliklerinin Ağırlıkları Algoritması Kullanılarak Geleneksel Sınavların Değerlendirilmesi. ACIN. 2022;6(1):127-39.