3 papers
cs.LG2025
Predicting Multi-Type Talented Students in Secondary School Using Semi-Supervised Machine Learning
Xinzhe Zheng, Zhen-Qun Yang, Jiannong Cao +1
Talent identification plays a critical role in promoting student development. However, traditional approaches often rely on manual processes or focus narrowly on academic achieveme…
cs.AI2025
Predicting Student Dropout Risk With A Dual-Modal Abrupt Behavioral Changes Approach
Jiabei Cheng, Zhen-Qun Yang, Jiannong Cao +2
Timely prediction of students at high risk of dropout is critical for early intervention and improving educational outcomes. However, in offline educational settings, poor data qua…
cs.CY2025
Modeling Behavior Change for Multi-model At-Risk Students Early Prediction (extended version)
Jiabei Cheng, Zhen-Qun Yang, Jiannong Cao +3
In the educational domain, identifying students at risk of dropping out is essential for allowing educators to intervene effectively, improving both academic outcomes and overall s…