Predicting Multi-Type Talented Students in Secondary School Using Semi-Supervised Machine Learning
It addresses the need for early and comprehensive talent identification in secondary education, though it is incremental as it applies existing machine learning techniques to a new educational dataset.
This study tackled the problem of early identification of diverse student talents in secondary schools by developing TalentPredictor, a semi-supervised multi-modal neural network, which achieved high prediction accuracy (0.908 classification accuracy, 0.908 ROCAUC) on data from 1,041 students.
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.