用半监督模型提前识别中学生七类天赋，提升早期培养效率
Predicting Multi-Type Talented Students in Secondary School Using Semi-Supervised Machine Learning
- 融合Transformer、LSTM和ANN的多模态神经网络，结合奖状与学习行为数据
- 在1041名学生上实现0.908准确率和0.908 ROCAUC，七类天赋预测精准
- 适合教育机构开展早期人才发掘，尤其关注非学术特长的学生
人才识别对促进学生发展至关重要。传统方法常依赖人工或仅关注学业成绩，且干预时机滞后至高等教育阶段，忽视多样化的非学术才能，错失早期干预机会。为此，本研究提出TalentPredictor——一种新型半监督多模态神经网络，结合Transformer、LSTM与ANN架构，旨在预测中学生七类天赋（学术、体育、艺术、领导力、服务、科技及其他）在离线教育环境中的表现。基于1,041名本地中学生的现有离线数据，该模型通过聚类各类奖项记录并提取学生多样化学习行为特征，实现了0.908的分类准确率和0.908的ROCAUC。结果表明，机器学习具备在学生发展早期识别多元才能的潜力。
原文摘要 · Abstract (English)
Talent identification plays a critical role in promoting student development. However, traditional approaches often rely on manual processes or focus narrowly on academic achievement, and typically delaying intervention until the higher education stage. This oversight overlooks diverse non-academic talents and misses opportunities for early intervention. To address this gap, this study introduces TalentPredictor, a novel semi-supervised multi-modal neural network that combines Transformer, LSTM, and ANN architectures. This model is designed to predict seven different talent types--academic, sport, art, leadership, service, technology, and others--in secondary school students within an offline educational setting. Drawing on existing offline educational data from 1,041 local secondary students, TalentPredictor overcomes the limitations of traditional talent identification methods. By clustering various award records into talent categories and extracting features from students' diverse learning behaviors, it achieves high prediction accuracy (0.908 classification accuracy, 0.908 ROCAUC). This demonstrates the potential of machine learning to identify diverse talents early in student development.
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