Shasha Yang

Online learning environments generate vast amounts of student interaction data. While these records capture observable behaviors, they do not directly reveal students’ underlying knowledge states, which are essential for tracking learning progress. Knowledge tracing (KT) addresses this gap by predicting students’ future performance on exercises related to specific concepts, thereby enabling personalized learning and intelligent tutoring. Existing deep learning-based KT methods achieve promising results, but they often overemphasize either the sequential evolution of knowledge or the static structural relationships, which does not reflect the dynamic evolution of student learning. Moreover, they fail to model students’ knowledge state accurately under sparse interactions. To overcome these limitations, we propose DyGAS, a dynamic graph-augmented sequence modeling framework for knowledge tracing. The sequential module captures the dynamics pattern of knowledge acquisition and forgetting, while the structural module employs graph convolutional networks (GCN) to model inter-concept dependencies and knowledge transfer. Additionally, we propose that static knowledge modeling provides semantic priors to stabilize the representation of sparse concepts. Empirical results on three benchmark datasets demonstrate that DyGAS achieves superior performance compared to state-of-the-art methods, offering accurate and robust knowledge tracing across diverse learning scenarios.

Peer assessment has emerged as a crucial approach for scaling evaluation in educational scenarios, fostering learner engagement, critical thinking, and collaborative learning. Nevertheless, traditional aggregation-based and probabilistic methods often fail to capture the intricate relational dependencies among students and submissions, thereby limiting their capacity to ensure reliable and equitable outcomes. Recent advances in graph neural networks (GNNs) offer promising avenues for representing peer-assessment data as graphs. However, most existing approaches treat all relations uniformly, overlooking variations in the reliability of evaluative interactions. To bridge this gap, we accordingly propose ReSAN (Relation-Sensitive Assessment Network), a novel framework that integrates relation-sensitive attention into the message-passing process. ReSAN dynamically evaluates and weights relationships, enabling the model to distinguish informative signals from noisy or biased assessments. Comprehensive experiments on both synthetic and real-world datasets demonstrate that ReSAN consistently surpasses strong baselines in prediction accuracy and robustness. These findings underscore the importance of explicitly modeling evaluator reliability for effectively capturing the dynamics of peer-assessment networks. Overall, this work advances reliable graph-based evaluation methods and provides new insights into leveraging representation learning techniques for educational analytics.

The ability to communicate effectively in writing and produce clear and cohesive text is a necessary skill in both educational settings and the workplace, yet many young students struggle to organize their thoughts and engage in deep thinking. To address these challenges, an augmented reality (AR) application titled “Explore Wild Animals” has been used to help students organize information; however, it may not accommodate different cognitive styles. Integrating formative peer assessment (FPA) strategy into AR-based instruction can enhance knowledge construction and address diverse cognitive needs. This study, conducted from May to June 2023, empirically investigates the effects of FPA in an AR environment on the writing performance of learners with field-independent (FI) and field-dependent (FD) cognitive styles. A total of 89 fifth-grade pupils from China were randomly assigned to two groups: one group adopting FPA in an AR environment (AR-FPA), and the other group adopting FPA in a conventional PowerPoint (PPT) version 2410 environment (FPA). The results of a two-way analysis of covariance (ANCOVA) indicate that the AR-FPA group outperformed the FPA group in writing performance. Specifically, FI learners benefitted more from the AR-FPA approach, while FD learners performed better with the FPA approach. However, multiple linear regression analysis reveals that the peer feedback quality and features showed little to no significant correlation with feedback providers’ writing performance, regardless of cognitive style. These results highlight the effectiveness of integrating AR and FPA in enhancing educational outcomes, providing practical insights for promoting the sustainability of technology-enhanced learning and teaching practices.

Although Federated Learning (FL) enables collaborative training across multiple decentralized devices, strategic clients may be reluctant to participate in FL unless sufficient incentives are available. Existing researches predominantly emphasize short-term incentives, while FL model training is typically a long-term process, and malicious clients may exhibit dishonest behavior during local training. Although designing a long-term incentive mechanism is crucial for FL, this task is challenging due to the heterogeneous nature of FL and the limitations of imperfect system monitoring. To this end, this paper designs the first sustainable incentive mechanism for FL called RATE, which aims to incentivize clients to continuously contribute more high-quality data. Specifically, by modeling the competition and cooperation relationship between servers and clients as a multi-server multi-client Stackelberg game, we prove the existence and uniqueness of the Stackelberg equilibrium (SE), and derive the unique SE through the cautious calculation of designed algorithms. Since the derived SE may not be optimal, we further utilize reputation to measure the long-term contribution of clients and build a function between their revenues and reputations to ensure the optimal revenue allocation, thereby maximzing the social welfare of RATE. Extensive experiments on both synthetic and real datasets demonstrate the superiority of RATE. Compared to the state-of-the-art baselines, RATE increases the total utility of the servers by up to 20%, reduces the reputation of malicious clients by up to 90%, and improves the average test accuracy overall.

With the growing public attention to data privacy protection, the problem of data silos has been exacerbated, which makes it more difficult for crowd intelligence technologies to get off the ground. Meanwhile, Federated Learning (FL) has received great attention for its ability to break data silos and jointly build machine learning models. To crack the data silo problem in crowd intelligence, we propose a new Crowd Federated Learning (CFL) framework, which is a two-tier architecture consisting of a cloud server, model owners, and data collectors, that enables collaborative model training among individuals without the need for raw data interaction. However, existing work struggles to simultaneously ensure the balance of incentives among data collectors, model owners, and cloud server, which can affect the willingness of sharing and collaboration among subjects. To solve the above problem, we propose a hierarchical incentive mechanism named FedCom , i.e., Crowd Federated Learning for Contribution matching, to match participants’ contributions with rewards. We theoretically prove that FedCom has contribution matching fairness, and conduct extensive comparative experiments with five baselines on one simulated dataset and four real-world datasets. Experimental results validate that FedCom is able to reduce the computation time of contribution evaluation by about 8 times and improve the global model performance by about 2% while ensuring fairness.

In the competitive landscape of online learning, developing robust and effective learning resource recommendation systems is paramount, yet the field faces challenges due to high-dimensional, sparse matrices and intricate user–resource interactions. Our study focuses on geometric matrix completion (GMC) and introduces a novel approach, graph-based truncated norm regularization (GBTNR) for problem solving. GBTNR innovatively incorporates truncated Dirichlet norms for both user and item graphs, enhancing the model’s ability to handle complex data structures. This method synergistically combines the benefits of truncated norm regularization with the insightful analysis of user–user and resource–resource graph relationships, leading to a significant improvement in recommendation performance. Our model’s unique application of truncated Dirichlet norms distinctively positions it to address the inherent complexities in user and item data structures more effectively than existing methods. By bridging the gap between theoretical robustness and practical applicability, the GBTNR approach offers a substantial leap forward in the field of learning resource recommendations. This advancement is particularly critical in the realm of online education, where understanding and adapting to diverse and intricate user–resource interactions is key to developing truly personalized learning experiences. Moreover, our work includes a thorough theoretical analysis, complete with proofs, to establish the convergence property of the GMC-GBTNR model, thus reinforcing its reliability and effectiveness in practical applications. Empirical validation through extensive experiments on diverse real-world datasets affirms the model’s superior performance over existing methods, marking a groundbreaking advancement in personalized education and deepening our understanding of the dynamics in learner–resource interactions.

Cross-IoT infrastructure access frequently occurs when performing tasks in a distributed computing infrastructure of a cyber-physical system (CPS). The access control technology that ensure secure access cross-IoT infrastructure usually automatically establish relationships between user-attribute/role-permission. How to efficiently determine whether an automatic authorization access control state satisfies the safety and availability requirements of a system is a huge challenge. Existing work often focuses on a single aspect of safety or availability, while ignoring the differences between permissions and the differences between users. In this paper, we first propose a fine-grained personalization policy that takes into account the specificity of permissions/users and describes the safety, availability and efficiency requirements of an access control system in CPS. Second, we define a Personalization Policy Checking (PPC) Problem to determine whether a given personalization policy is satisfied in an access control state. We give the computational complexity of the PPC problem in different subcases, and show that it is NP-complete in general. Third, we design a binary genetic search algorithm, whose improvements mainly include continuous update and selection of the best chromosomes in the population for iteration, and exploring and determining the optimal crossover and mutation probabilities, thereby improving the convergence efficiency of the algorithm. Finally, simulation results show the effectiveness of our proposed algorithm, which is especially fit for the case that the computational overhead is even more important than the accuracy in a large-scale CPS system.