@techreport{oai:ipsj.ixsq.nii.ac.jp:00222031,
 author = {渡邊, 健太 and 市原, 大裕 and 杉谷, 賢一 and 中野, 裕司 and 久保田, 真一郎 and Kenta, Watanabe and Daisuke, Ichihara and Kenichi, Sugitani and Hiroshi, Nakano and Shin-ichiro, Kubota},
 issue = {1},
 month = {Oct},
 note = {本研究の目的は大学で連続欠席になりそうな学生の学習傾向についての調査と連続欠席の学生を分類するのに有効な特徴量を抽出することである．連続欠席になりそうな学生を LMS 上の複数の学習コースでの学習ログをもとに，学習習慣やアクセス傾向，アクセス時間などを調査し，特徴量抽出を行なった．作成した特徴量を評価するため，特徴ベクトルと連続欠席のラベルをもとに交差検証を行った結果について報告する．, The purpose of this study was to investigate the learning tendencies of students who are likely to be absent continuously at university and to extract features that are useful for classifying students who are absent continuously. We extracted features from the study logs of students who were likely to be absent continuously, and investigated their study habits, access tendencies, and access times. In order to evaluate the extracted features, we report the results of cross-validation based on the feature vectors and the labels of consecutive absences.},
 title = {連続欠席者の予測を目的とした学習ログからの特徴量抽出},
 year = {2022}
}