@techreport{oai:ipsj.ixsq.nii.ac.jp:00222688,
 author = {左座, 祐之助 and 中村, 伊吹 and 増田, 武史 and 広瀬, 啓雄 and 尾崎, 剛 and Yunosuke, Zoza and Ibuki, Nakamura and Takeshi, Masuda and Hiroo, Hirose and Takeshi, Ozaki},
 issue = {8},
 month = {Nov},
 note = {学生のモチベーションを向上・維持させることは学習効果に大きく関連する．本研究では，達成動機理論を参考にした質問紙を作成し，モチベーションを可視化した．また，モチベーションと学習行動から授業についてこられていない学生を機械学習により予測し，早期発見することを目的とした．その結果，成績を予測するのにモチベーションを用いることは有効であることが確認できた．, Increasing and maintaining students' motivation is significantly related to the effectiveness of learning. In this study, a questionnaire based on the achievement motivation theory was developed to visualize motivation. We also used machine learning to predict students who are not keeping up with their classes based on their motivation and learning behavior, with the aim of detecting students who are not keeping up with their classes at an early stage. As a result, it was confirmed that the use of motivation is effective in predicting student performance.},
 title = {学習行動及び学生のモチベーションを加味した学習成果予測モデルの構築},
 year = {2022}
}