7 citations · 13 across the 7 of their papers we have counts for
8 papers
Recovering Stranded Discrimination in Knowledge Tracing: Per-Item Bias Correction via Empirical-Bayes Shrinkage
Xiaoran Yan, Cheng Tang, Atsushi Shimada
Deployed knowledge-tracing models are typically frozen after training, yet systematic per-item logit bias arises, from limited per-item expressivity in backbone architectures and f…
Capture-Calibrate-Coach: A Graph-Based Framework for Knowledge Monitoring Estimation and Adaptive Feedback
Gen Li, Li Chen, Cheng Tang +5
Effective learning support requires understanding not only what learners know but also how accurately they perceive their own understanding. This metacognitive dimension, known as…
Three Years with Classroom AI in Introductory Programming: Shifts in Student Awareness, Interaction, and Performance
Boxuan Ma, Huiyong Li, Gen Li +4
Generative AI (GenAI) tools such as ChatGPT now provide novice programmers with instant, personalized support and are reshaping computing education. While a growing body of work ex…
Scaffolding Metacognition in Programming Education: Understanding Student-AI Interactions and Design Implications
Boxuan Ma, Huiyong Li, Gen Li +6
Generative AI tools such as ChatGPT now provide novice programmers with unprecedented access to instant, personalized support. While this holds clear promise, their influence on st…
Attention Mamba: Time Series Modeling with Adaptive Pooling Acceleration and Receptive Field Enhancements
Sijie Xiong, Shuqing Liu, Cheng Tang +3
"This work has been submitted to the lEEE for possible publication. Copyright may be transferred without noticeafter which this version may no longer be accessible." Time series mo…
Single-Agent vs. Multi-Agent LLM Strategies for Automated Student Reflection Assessment
Gen Li, Li Chen, Cheng Tang +4
We explore the use of Large Language Models (LLMs) for automated assessment of open-text student reflections and prediction of academic performance. Traditional methods for evaluat…
Knowledge Distillation in RNN-Attention Models for Early Prediction of Student Performance
Sukrit Leelaluk, Cheng Tang, Valdemar Švábenský +1
Educational data mining (EDM) is a part of applied computing that focuses on automatically analyzing data from learning contexts. Early prediction for identifying at-risk students…
Attention-Seeker: Dynamic Self-Attention Scoring for Unsupervised Keyphrase Extraction
Erwin D. López Z., Cheng Tang, Atsushi Shimada
This paper proposes Attention-Seeker, an unsupervised keyphrase extraction method that leverages self-attention maps from a Large Language Model to estimate the importance of candi…