A Study on the Deep Learning-Based Textbook Questionnaires Detection Experiment


KIPS Transactions on Software and Data Engineering, Vol. 10, No. 11, pp. 513-520, Nov. 2021
https://doi.org/10.3745/KTSDE.2021.10.11.513,   PDF Download:
Keywords: Deep Learning, Textbook Questionnaires Detection Model, Detection Model, computer vision
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.


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Cite this article
[IEEE Style]
K. T. Jong, H. T. In, J. S. Park, "A Study on the Deep Learning-Based Textbook Questionnaires Detection Experiment," KIPS Transactions on Software and Data Engineering, vol. 10, no. 11, pp. 513-520, 2021. DOI: https://doi.org/10.3745/KTSDE.2021.10.11.513.

[ACM Style]
Kim Tae Jong, Han Tae In, and Ji Su Park. 2021. A Study on the Deep Learning-Based Textbook Questionnaires Detection Experiment. KIPS Transactions on Software and Data Engineering, 10, 11, (2021), 513-520. DOI: https://doi.org/10.3745/KTSDE.2021.10.11.513.