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From the 1 of 10 linked papers with an AI index.

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10 papers

cs.LG2026

Disentangling Knowledge States with Ability and Proficiency Modeling for Knowledge Tracing

Duantengchuan Li, Yingqian Bi, Jinsong Chen +2

The paper introduces Phase-Aware Knowledge Tracing (PAKT), a method that separates student interaction data into ability-building and proficiency phases to better predict future pe…

cs.AI2026

MBP-KT: Learning Global Collaborative Information from Meta-Behavioral Pattern for Enhanced Knowledge Tracing

Yuhao Jia, Duantengchuan Li, Jinsong Chen +4

The emerging collaborative information-based knowledge tracing (KT) has been a promising way to enhance modeling of learners' knowledge states. The core idea is to extract the coll…

cs.CL2025

Confident RAG: Enhancing the Performance of LLMs for Mathematics Question Answering through Multi-Embedding and Confidence Scoring

Shiting Chen, Zijian Zhao, Jinsong Chen

Large Language Models (LLMs) hold significant promise for mathematics education, yet they often struggle with complex mathematical reasoning. While Retrieval-Augmented Generation (…

cs.LG2025

Mixture of Message Passing Experts with Routing Entropy Regularization for Node Classification

Xuanze Chen, Jiajun Zhou, Yadong Li +3

Graph neural networks (GNNs) have achieved significant progress in graph-based learning tasks, yet their performance often deteriorates when facing heterophilous structures where c…

cs.CL2025

Documents Are People and Words Are Items: A Psychometric Approach to Textual Data with Contextual Embeddings

Jinsong Chen

This research introduces a novel psychometric method for analyzing textual data using large language models. By leveraging contextual embeddings to create contextual scores, we tra…

cs.LG2025

DAM-GT: Dual Positional Encoding-Based Attention Masking Graph Transformer for Node Classification

Chenyang Li, Jinsong Chen, John E. Hopcroft +1

Neighborhood-aware tokenized graph Transformers have recently shown great potential for node classification tasks. Despite their effectiveness, our in-depth analysis of neighborhoo…