著者

宮崎 和光 吉田 望 森 利枝

出版者

一般社団法人 電気学会

雑誌

電気学会論文誌C(電子・情報・システム部門誌) (ISSN:03854221)

巻号頁・発行日

vol.142, no.2, pp.117-128, 2022

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<p>In 2017, it became mandatory for universities in Japan to disclose their policies in degree granting (Diploma Policy: DP, hereafter) that state standards to confer degrees. Meanwhile, since 1991, nomenclature of major fields that appear in diplomas has been the responsibility of individual universities, instead of the national regulation. This study examines whether the former reasonably evokes the latter, given that both of them are deemed to represent the learning outcomes that the graduate has obtained. In order to do so, we compared the ability of humans and that of a deep-learning system (which is based on the Character-level CNN), to match DPs and major fields that are randomly given. In the examination of human ability, which was implemented with a large enough number of participants to obtain statistically significant results, we found there were a certain number of DPs that the majority of people failed to match with major fields. Given this fact, we analyzed such DPs to demonstrate that the deep learning system shows a high success rate in sorting out the DPs that poorly evoke major fields.</p>