Journal of the Korea Convergence Society (한국융합학회논문지)

Korea Convergence Society (한국융합학회)
권호 표지
DOI QR코드

DOI QR Code

Consultation Management Model based on Behavior Classification of Special-Needs Students

특수학생들의 행동 분류 기반의 상담관리 모델

  • Park, Won-Cheol (Division of Computer Engineering, Kongju National University) ;
  • Park, Koo-Rack (Division of Computer Science & Engineering, Kongju National University)
  • Received : 2021.08.31
  • Accepted : 2021.09.20
  • Published : 2021.09.28
Download PDF
⟨ Previous Next ⟩

Abstract

Unlike behaviors that are generally known, information regarding unspecific behaviors is insufficient. For an education or guidance regarding the unspecific behaviors, collection and management of data regarding the unspecific behaviors of special-needs students are needed. In this paper, a consultation management model based on behavior classification of special-needs students using machine learning is proposed. It collects data by photographing the behavior of special students in real time, analyzes the behavior pattern, composes a data set, and trains it in the suggestion system. It is possible to improve the accuracy by comparing the behavior of special students photographed later into the suggestion system and analyzing the results by comparing it with the existing data again. The test has been performed by arbitrarily applying unspecific behaviors that are not stored in the database, and the forecast model has accurately classified and grouped the input data. Also, it has been verified that it is possible to accurately distinguish and classify the behaviors through the feature data of the behaviors even if there are some errors in the input process.

일반적으로 알려져 있는 행동에 비해 일반적으로 알려져 있지 않은 불특정 행동들에 대한 자료들은 부족한 실정이다. 특수학생들의 불특정 행동들에 대한 교육이나 지도를 위한 데이터 수집 및 관리가 필요하다. 본 논문에서는 기계 학습을 이용한 웹 기반의 특수학생 상담관리 모델을 제안한다. 실시간으로 특수학생들의 행동을 촬영하여 데이터를 수집하고, 행동패턴을 분석하여 데이터 셋을 구성하고 제안 시스템에 학습시킨다. 추후에 촬영되는 특수학생들의 행동을 제안시스템에 입력 및 분석을 통하여 기존 데이터와 비교하여 나온 결과를 다시 학습하여 정확도를 향상 시킬 수 있다. 데이터베이스에 저장되어 있지 않은 불특정 행동들을 임의로 행하여 테스트를 진행하였으며 예측 모델은 입력 데이터를 통해 정확하게 분류 및 그룹화 하였다. 또한 진입 과정에서 약간의 오차가 있더라도 행동의 특징 데이터를 통해 행동을 정확하게 구분 및 분류하는 것이 가능하다는 것을 확인 할 수 있었다.

Keywords

References

  1. Zigmond, N. P & Kloo, A. (2017). General and special education are (and should be) different. Handbook of special education, 2nd Edition, 160-172. DOI:10.4324/9781315517698
  2. Anderson, N. A., Barksdale, M. A & Hite, C. E. (2005). Preservice teachers' observations of cooperating teachers and peers while participating in an early field experience. Teacher Education Quarterly, 32(4), 97-117.
  3. J. M. Kim, B. U. Jeon & H. J. Lim. (2014). The Effect of Individualized Positive Behavior Support on Class Engagement Behaviors for the Middle School Student with Severe Autism Spectrum Disorders. Korean Journal of Special Education, 49(2), 45-67. DOI:10.15861/kjse.2014.49.2.45
  4. Pierangelo, R. & Giuliani, G. (2008). Teaching students with autism spectrum disorders: A step-by-step guide for educators. Corwin Press.
  5. Y. J. Chung, S. M. Ahn, J. H. Yang & J. J. Lee. (2017). Comparison of deep learning frameworks: about theano, tensorflow, and cognitive toolkit. Journal of Intelligence and Information Systems, 23(2), 1-17. DOI:10.13088/jiis.2017.23.2.001
  6. Abadi, M. (2016). TensorFlow: learning functions at scale. In Proceedings of the 21st ACM SIGPLAN International Conference on Functional Programming, 1-1. DOI:10.1145/2951913.2976746
  7. Vishnu, A., Siegel, C & Daily, J. (2016). Distributed tensorflow with MPI. arXiv preprint arXiv:1603.02339.
  8. Rampasek, L & Goldenberg, A. (2016). Tensorflow: Biology's gateway to deep learning?. Cell systems, 2(1), 12-14. DOI:10.1016/j.cels.2016.01.009
  9. LeCun, Y., Bengio, Y & Hinton, G. (2015). Deep learning. nature, 521(7553), 436-444. DOI:10.1038/nature14539
  10. Albawi, S., Mohammed, T. A & Al-Zawi, S. (2017). Understanding of a convolutional neural network. 2017 International Conference on Engineering and Technology (ICET), 1-6. DOI:10.1109/ICEngTechnol.2017.8308186
  11. Bluche, T., Ney, H & Kermorvant, C. (2013). Feature extraction with convolutional neural networks for handwritten word recognition. In 2013 12th International Conference on Document Analysis and Recognition. IEEE, 285-289. DOI:10.1109/ICDAR.2013.64
  12. S. H. Seol, I. S. Choi, J. W. Shin & M. S. Chung. (2018). Design of Convolutional Neural Network Structure for the Identification of Warhead and Debris in the Separation Phase. Journal of KIIT, 16(6), 81-89. DOI:10.14801/jkiit.2018.16.6.81
  13. Krizhevsky, A., Sutskever, I & Hinton, G. E. (2017). Imagenet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84-90. DOI: 10.1145/3065386
  14. Lin, M., Chen, Q & Yan, S. (2013). Network in network. arXiv preprint arXiv:1312.4400.
  15. Szegedy, C et al. (2015). Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition, 1-9.