KIPS Transactions on Software and Data Engineering (정보처리학회논문지:소프트웨어 및 데이터공학)

Korea Information Processing Society (한국정보처리학회)
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A Study on the Deep Learning-Based Textbook Questionnaires Detection Experiment

딥러닝 기반 교재 문항 검출 실험 연구

  • 김태종 (월드버텍 주식회사) ;
  • 한태인 (한국열린사이버대학교 디지털비즈니스학과) ;
  • 박지수 (전주대학교 컴퓨터공학과)
  • Received : 2021.10.06
  • Accepted : 2021.10.12
  • Published : 2021.11.30
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Abstract

Recently, research on edutech, which combines education and technology in the e-learning field called learning, education and training, has been actively conducted, but it is still insufficient to collect and utilize data tailored to individual learners based on learning activity data that can be automatically collected from digital devices. Therefore, this study attempts to detect questions in textbooks or problem papers using artificial intelligence computer vision technology that plays the same role as human eyes. The textbook or questionnaire item detection model proposed in this study can help collect, store, and analyze offline learning activity data in connection with intelligent education services without digital conversion of textbooks or questionnaires to help learners provide personalized learning services even in offline learning.

최근 학습, 교육 및 훈련으로 일컫는 이러닝 분야에서 교육(education)과 기술(technology)이 접목된 에듀테크(edutech)에 대한 연구가 활발하게 진행되고 있다. 그러나 디지털 기기에서 자동으로 수집이 가능한 학습활동 데이터를 기반으로 학습자 개개인에게 맞춤형 학습을 제공하는 연구는 많으나, 오프라인 학습에서 추출하고 활용해야 할 데이터의 수집 연구는 적다. 이에 본 연구는 데이터 수집 연구를 위해 인공지능 컴퓨터 비전 기술을 이용하여 교재 또는 문제지의 문항 검출 방법을 연구한다. 이는 교재 또는 문제지에 대한 디지털로의 변환작업 없이도 오프라인 학습활동 데이터를 수집·저장·분석하여 지능화 교육 서비스와 연계를 통해 오프라인 학습에서도 학습자의 개인 맞춤형 학습 서비스 제공한다.

Keywords

Acknowledgement

본 연구는 과학기술정보통신부 및 정보통신기획평가원의 대학ICT연구센터 육성지원사업의 연구결과로 수행되었음(IITP-2021-2020-0-01789).

References

  1. J. W. Schofield, R. Eurich-Fulcer, and C. L. Britt, "Teachers, computer tutors, and teaching: The artificially intelligent tutor as an agent for classroom change," American Educational Research Journal, Vol.31, No.3, pp.579-607, 1994. https://doi.org/10.3102/00028312031003579
  2. Y. Kong, "Edutech Industry Trends and Implications," Software Policy & Research Institute, Monthly Software Oriented Society, No.70, 2020.
  3. S. Ren, K. He, R. Girshick, and J. Sun, "Faster R-CNN: Towards real-time object detection with region proposal networks," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.39, pp.1137-1149, 2017. https://doi.org/10.1109/TPAMI.2016.2577031
  4. Y. H. Lee and Y. Kim, "Comparison of CNN and YOLO for object detection," Journal of the Semiconductor & Display Technology, Vol.19, No.1, pp.85-92, 2020.
  5. K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Computer Vision and Pattern Recognition, pp.770-778, 2016.
  6. X. Wu, D. Sahoo, and S. C. H. Hoi, "Recent advances in deep learning for object detection," Neurocomputing, Vol.396, pp.39-64, 2020. https://doi.org/10.1016/j.neucom.2020.01.085
  7. M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman, "The Pascal Visual Object Classes (VOC) challenge," International Journal of Computer Vision, Vol.88, No.2, pp.303-338, 2010. https://doi.org/10.1007/s11263-009-0275-4
  8. J. S. Lee, S. K. Lee, D. W. Kim, S. J. Hong, and S. I. Yang, "Trends on object detection techniques based on deep learning," Electronics and Telecommunications Trends, Vol.33, No.4, pp.23-32, 2018. https://doi.org/10.22648/ETRI.2018.J.330403
  9. Alexey Bochkovskiy, Chien-Yao Wang, Hong-Yuan Mark Liao, "YOLOv4: Optimal Speed and Accuracy of Object Detection," Computer Science Engineering ArXiv, 2020, [Internet], https://arxiv.org/abs/2004.10934
  10. J. Solawetz, "Getting started with VoTT annotation tool for computer vision," 2020.07, [Internet], https://blog.roboflow.com/vott/
  11. P. Henderson, and V. Ferrari, "End-to-end training of object class detectors for mean average precision," Computer Science Engineering ArXiv, 2017, [Internet], https://arxiv.org/abs/1607.03476