学生の成功と留守のための予測モデリングとコホートデータ分析
1Computer Engineering and Computer Science Department, California State University, Long Beach, 90840, CA, USA.
Evaluation and program planning
|September 2, 2025
まとめ
学生の成績は 人口統計によって異なります 少数派やペル資格の学生は 課題に直面しますが 予測モデルにより 対象に絞った支援と 留学率の向上のために リスクのある生徒を特定できます
科学分野:
- 高等教育研究
- 教育データマイニング
- 学生の成績分析
背景:
- 学業成績と留学率は 高等教育の重要な指標です
- 公平な学生支援には 人口格差を理解することが不可欠です
- 予測モデリングは 学生の成功への早期介入の機会を提供します
研究 の 目的:
- 学生の成績の差を分析する
- 学生の成績に影響を与える重要な要因 (GPA,学分蓄積) を特定する.
- 学生の成功を予測するための予測モデルを開発し,検証する.
主な方法:
- 2万3千人の新入生を分析した結果
- 要素の検討:GPA,クレジット蓄積,ペル・グラント資格,マイノリティ・ステータス,親の教育
- 予測モデリングのためのクラスタ分析とディープラーニングの適用
主要な成果:
- マイノリティとペル資格の学生は,より少ない単位を蓄積します.
- 少数民族の生徒の平均成績が低く 平均成績の差が大きくなっています
- クラスタリング分析によって3つの異なる学術的関与プロファイルが特定されました.
結論:
- 異質な生徒の成績は 差別化された支援戦略を必要とします
- 予測モデルによって 2年生の成績と GPAを正確に予測できます
- 学生の留学率と学業の勢いを高めるための 実践的な洞察が得られます
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