クラスでの成績評価における人工知能グラフコンヴォルションネットワークの適用
1Liyuan Foreign Language Primary School in Futian District, Shenzhen, 518000, China. wushuying1234@126.com.
Scientific reports
|September 1, 2025
まとめ
この研究は,教室での成績評価のためのグラフコンボリューションネットワーク (GCN) モデルを導入し,従来の方法よりも客観性と正確性を向上させます. このモデルは,より良い教育評価のために,学生の社会的関係を効果的に利用します.
科学分野:
- 教育技術
- 人工知能
- データサイエンス
背景:
- 伝統的な授業での評価は 主観的で 範囲が限られているので 学生の真の学習状態を捉えることができません
- 既存の教育データ分析方法では 社会的交流が学業成績に与える影響が 見過ごされていることが多いのです
研究 の 目的:
- グラフコンボリューションネットワーク (GCN) を用いた客観的で正確な授業での成績評価モデルを開発する.
- 学生の社会的な関係を活用し 教育的な評価を深めるために インタラクション・グラフを作成します
- インテリジェントでダイナミックなクラスクラス評価システムに 新しい技術的アプローチを提供すること.
主な方法:
- 学生の相互関係グラフを作成し,個々の属性と社会的つながりを統合しました.
- 応用グラフニューラルネットワーク (GNN) テクニック,特にGCNは,マルチソースの教育データを分析します.
- 教育評価シナリオに合わせたGCNモデルアーキテクチャとトレーニングプロセスを設計しました.
主要な成果:
- 提案されたGCNモデルは4つのクラスの教室でのパフォーマンスの予測タスクにおいて従来の機械学習方法を大幅に上回った.
- アブレーション実験は,予測の精度を向上させる上で,社会的関係情報の重要な役割を確認しました.
- 比較分析により,異なるグラフ構築戦略の有効性が確認されました.
結論:
- グラフコンボリューションネットワークは 客観的で正確な教育評価のための強力なツールです
- ソーシャルネットワーク分析をGNNモデルに統合することで 学生の成績の予測が向上します
- この研究により,GNNの教育データマイニングの応用が拡大され,よりスマートな評価システムへの道が開けています.
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