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Learning management systems (LMSs) have become fundamental tools for higher education, and frameworks to leverage digital education data within an LMS have attracted attention. On the other hand, there is strong demand for dealing with various education data provided not only from electronic media but also from non-electronic media, such as handwritten assignments. In addition, it is desirable to reduce time-consuming tasks such as sorting and returning handwritten assignments by lecturers. With this background, this paper describes the development of "KamiRepo1", a system that makes it possible to automatically upload handwritten assignments to an LMS. In this system, a deep-learning-based retrainable optical character recognition (OCR) system is developed to identify scanned handwritten assignments of individual students and read their scores. Then, their scanned files, automatically separated from the entire file of scanned handwritten assignments, are returned to the individual students through the LMS together with their corresponding scores. Compared with a conventional system using a dedicated multifunction printer, our developed system is capable of 1) using general-purpose scanners, 2) using a user interface on a Web browser, and 3) achieving accurate student identification. We launched this system in our university in April 2017 and have evaluated its effectiveness. The experimental results obtained using real data collected for 6 months showed that our system achieved a 99.7% success rate in the automatic upload process, and it was confirmed that the system can greatly reduce the burden of sorting and returning handwritten assignments. |
