5 papers
Calibration and Transformation-Free Weight-Only LLMs Quantization via Dynamic Grouping
Xinzhe Zheng, Zhen-Qun Yang, Zishan Liu +4
Large Language Models (LLMs) deliver strong performance but are difficult to deploy under tight memory and compute constraints. Low-bit post-training quantization (PTQ) is a promis…
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…
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…
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…
DMP_AI: An AI-Aided K-12 System for Teaching and Learning in Diverse Schools
Zhen-Qun Yang, Jiannong Cao, Xiaoyin Li +4
The use of Artificial Intelligence (AI) has gained momentum in education. However, the use of AI in K-12 education is still in its nascent stages, and further research and developm…