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http://dx.doi.org/10.3745/KTSDE.2021.10.11.513

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

Kim, Tae Jong (월드버텍 주식회사)
Han, Tae In (한국열린사이버대학교 디지털비즈니스학과)
Park, Ji Su (전주대학교 컴퓨터공학과)
Publication Information
KIPS Transactions on Software and Data Engineering / v.10, no.11, 2021 , pp. 513-520 More about this Journal
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.
Keywords
Deep Learning; Textbook Questionnaires Detection Model; Detection Model; Computer Vision;
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