@techreport{oai:ipsj.ixsq.nii.ac.jp:00195386,
 author = {生田, 寛 and 中野, 裕司 and 杉谷, 賢一 and 久保田, 真一郎 and Kan, Ikuta and Hiroshi, Nakano and Kenichi, Sugitani and Shin-ichiro, Kubota},
 issue = {21},
 month = {Mar},
 note = {LMS のオンライン記述式問題は解答が多様なため自動的に評価を行い，フィードバックを返すことが困難である．記述式問題には大きく分けてエッセイ式と短答式の問題形式があり，自動採点の先行研究には，解答の候補を有しないエッセイ式の問題 （1000 字程度の小論文） を対象にしたものや，解答候補を有する短答式の問題 （1 文または多くとも 2 文） を対象にしたものがある．しかし，解答候補を有しない短答式記述問題に対しての自動採点は解答文の自由度が上がり困難であると考えられる．本研究では，特定のテーマ設定がなされた解答候補を有しない短答式記述問題に対して，潜在的意味解析により評価対象の解答文を自動評価し，その結果をもとに LMS でフィードバックを返す仕組みを提案する．, In short answer question to impliment online on LMS, it is known for difficulties to evaluate and give feedbacks for students automatically because of various answers. Descriptive questions can be roughly divided into question formats of essay style and short-answer style, and prior studies of automatic grading are directed to essay style questions (no less than 1000 letters of essay) without answer candidates And short-answer questions having answer candidates (one sentence or at most two sentences). However, automatic grading for short sentences in response to short-answer questions without answer candidates is considered to be difficult because the degree of freedom of the answer sentence is increased. In this research, we grade short sentence answers automatically by latent semantic analysis for short-answer questions, which does not have answer candidates with specific theme settings, and based on the results, We propose a mechanism to give feedbacks for students on LMS.},
 title = {オンライン短答式記述問題の解答に対する潜在的意味解析を用いた自動フィードバック手法の検討},
 year = {2019}
}