Xinjie Sun

Cognitive diagnosis (CD) is a central assessment approach in intelligent education that aims to uncover learners' knowledge mastery and latent cognitive abilities under examination conditions. However, existing models primarily infer learners' knowledge state based on their final performance, thus failing to capture the reasoning trajectories during problem-solving and struggling to address the dissociation between ability and performance caused by short-term affective fluctuations. To address these challenges, we propose a generative agent for CD (Agent4CD) in intelligent education that enhances interpretability and provides greater personalization and adaptability. Specifically, the framework constructs learner profiles and incorporates a memory module to integrate historical responses. It leverages the reasoning capabilities of large language models (LLMs) to dynamically model cognitive paths and knowledge evolution. The affect perception module utilizes LLMs' contextual understanding to identify students' transient affective state, helping to mitigate misjudgments of ability caused by affective fluctuations. Meanwhile, a dual-reflection mechanism supports both real-time intervention and long-term ability development. Experiments demonstrate that Agent4CD outperforms existing methods in diagnostic accuracy, interpretability, and personalization. To facilitate further research, data and code are publicly available at https://github.com/xinjiesun-ustc/Agent4CD.

Cognitive diagnosis (CD) models assess students’ knowledge state by analyzing test performance and identifying specific knowledge concepts mastered. However, traditional CD models often overlook dynamic changes in knowledge state during exercise and exam. To address this, we propose tracking knowledge state transitions in cognitive diagnosis (TKT-CD). The TKT-CD model introduces two key modules: the time-driven knowledge state transition (TDKT) module and the cognition-driven knowledge state transition (CDKT) module. TDKT captures temporal transitions in knowledge state during exercise, while CDKT analyzes relationships between knowledge concepts during exercise and exam to enhance reasoning accuracy. Combining neural networks with traditional static CD methods, TKT-CD resolves inconsistencies in knowledge state and provides more accurate representations of student interactions. Empirical studies show that TKT-CD significantly outperforms existing CD methods in predicting student performance, particularly in exercise and exam-based learning modes, offering a more effective tool for personalized education.

Transmission Control Protocol (TCP) congestion control is a fundamental mechanism in the Internet that maintains network stability and performance by adjusting the sending rate of connections. Recently, Deep Reinforcement Learning (DRL) methods have shown superior performance over traditional expert-designed solutions. However, the DRL policies are often represented by black-box neural networks, they lack interpretability, making verification challenging and requiring excessive floating-point computation. This work introduces a novel approach, Programmatic reinforcement learning for Congestion Control (ProCC), designed to autonomously discover a program as a control policy from scratch. Programs in ProCC include branching structures (e.g., if blocks and if-else blocks), conditions and actions. However, directly optimizing such program structures is challenging due to their discrete non-differentiable nature, and the program space grows exponentially as the depth increases. To address this issue, ProCC defines a Domain-Specific Language (DSL) and program transformation rules, enabling the construction of a program search graph where similar programs are closer in proximity. Subsequently, ProCC employs Monte Carlo Tree Search (MCTS) to efficiently explore the discrete space and obtain promising programs. Extensive experiments conducted in multiple simulated environments demonstrate that ProCC is adaptive and consistently performs well under varying network conditions. The learned program's performance surpasses that of state-of-the-art DRL agents, and more importantly, the generated policies are concise, transparent, and computationally efficient.

Knowledge Tracing (KT) predicts future performance by modeling students’ historical interactions, and understanding students’ affective states can enhance the effectiveness of KT, thereby improving the quality of education. Although traditional KT values students’ cognition and learning behaviors, efficient evaluation of students’ affective states and their application in KT still require further exploration due to the non-affect-oriented nature of the data and budget constraints. To address this issue, we propose a computation-driven approach, Dynamic Affect Simulation Knowledge Tracing (DASKT), to explore the impact of various student affective states (such as frustration, concentration, boredom, and confusion) on their knowledge states. In this model, we first extract affective factors from students’ non-affect-oriented behavioral data, then use clustering and spatiotemporal sequence modeling to accurately simulate students’ dynamic affect changes when dealing with different problems. Subsequently, we incorporate affect with time-series analysis to improve the model's ability to infer knowledge states over time and space. Extensive experimental results on two public real-world educational datasets show that DASKT can achieve more reasonable knowledge states under the effect of students’ affective states. Moreover, DASKT outperforms the most advanced KT methods in predicting student performance. Our research highlights a promising avenue for future KT studies, focusing on achieving high interpretability and accuracy.