Large language models (LLMs) are revolutionizing education, with LLM-based agents playing a key role in simulating student behavior. A major challenge in student simulation is modeling the diverse learning patterns of students at various cognitive levels. However, current LLMs, typically trained as “helpful assistants”, target at generating perfect responses. As a result, they struggle to simulate students with diverse cognitive abilities, as they often produce overly advanced answers, missing the natural imperfections that characterize student learning and resulting in unrealistic simulations. To address this issue, we propose a training-free framework for student simulation. We begin by constructing a cognitive prototype for each student using a knowledge graph, which captures their understanding of concepts from past learning records. This prototype is then mapped to new tasks to predict student performance. Next, we simulate student solutions based on these predictions and iteratively refine them using a beam search method to better replicate realistic mistakes. To validate our approach, we construct the Student_100 dataset, consisting of 100 students working on Python programming and 5,000 learning records. Experimental results show that our method consistently outperforms baseline models, achieving 100% improvement in simulation accuracy and realism.

Datafication, the process where users’ actions online are pervasively recorded, tracked, aggregated, analysed, and exploited by online services in multiple ways, is becoming increasingly common today. However, we know little about how children, especially non-Western children, perceive such practices. Through one-to-one semi-structured interviews with 36 children aged 11–14 from Chinese middle schools, we examined how Chinese children perceive datafication practices. We identified three knowledge gaps in children’s current perceptions of datafication practices online, including their lack of recognition of (i) their data ownership, (ii) data being transmitted across platforms, and (iii) datafication could go beyond video recommendation and include inferences and profiling of their personal aspects. Through contextualising these observations within the Chinese context and its unique online ecosystem, we identified cultural traits in Chinese children’s perceptions of datafication. We drew on education theories to discuss how to support the future digital literacy development and design online platforms for Chinese children.

Knowledge tracing (KT) is a fundamental task in intelligent education aimed at tracking students’ knowledge status and predicting their performance on new questions. The primary challenge in KT is accurately inferring a high-quality representation of students’ knowledge state that effectively captures their understanding of questions. However, existing methods are typically developed under the assumption that students’ behaviors directly reflect their knowledge state, which may not hold true especially in online learning scenarios. Abnormal behaviors exhibited by students, such as guessing and plagiarism, can introduce biases into the data, making it difficult to accurately assess students’ true knowledge state. To address this limitation, we propose a novel DebiAsed Cognition rEpresentation (DACE) modeling approach. This approach introduces a novel adversarial training strategy based on information bottleneck theory to obtain a debiased knowledge state representation that retains only the most reliable information for accurately predicting students’ performance on new questions. Moreover, we design a novel contrastive learning module through embedding-based augmentation to further enhance the robustness and generalizability of the learned knowledge state representation. We conduct extensive experiments on three public KT datasets and the newly released dataset BaiPy to demonstrate the superiority of our model over strong baselines, particularly when confronted with biased data. Our code and datasets are available at https://github.com/lvXiangwei/DACE.git .

Domain generalization for intelligent fault diagnosis is a technology that can acquire diagnostic knowledge from related machines and generalize to the unseen domain. However, the structure and working conditions differences between machines lead to significant variation in data distribution, making it difficult to generalize the trained network directly to unseen machines. This research proposed PhysiCausalNet, a causal- and physics-driven domain generalization network that mines the fault causality and incorporates the physical prior knowledge of the unseen target machine to realize domain-invariant feature extraction and domain-specific knowledge embedding. To form the cross-domain invariant causal mechanism, the progressive consistency causal factorization loss is proposed to separate the fault causal factors from implicit representation. Meanwhile, for the adaptability to a specific domain without involving the target domain data, the Fourier filter demodulation structure is proposed to extract periodic fault components, and the dynamics embedding loss is designed according to prior physical knowledge of the target machine as a physical constraint for the network. The effectiveness of proposed approach is verified in four machines and twelve working conditions including public, laboratory, and industrial datasets.

Anti-spoofing detection has become a necessity for face recognition systems due to the security threat posed by spoofing attacks. Despite great success in traditional attacks, most deep-learning-based methods perform poorly in 3D masks, which can highly simulate real faces in appearance and structure, suffering generalizability insufficiency while focusing only on the spatial domain with single frame input. This has been mitigated by the recent introduction of a biomedical technology called rPPG (remote photoplethysmography). However, rPPG-based methods are sensitive to noisy interference and require at least one second (> 25 frames) of observation time, which induces high computational overhead. To address these challenges, we propose a novel 3D mask detection framework, called FASTEN (Flow-Attention-based Spatio-Temporal aggrEgation Network). We tailor the network for focusing more on fine-grained details in large movements, which can eliminate redundant spatio-temporal feature interference and quickly capture splicing traces of 3D masks in fewer frames. Our proposed network contains three key modules: 1) a facial optical flow network to obtain non-RGB inter-frame flow information; 2) flow attention to assign different significance to each frame; 3) spatio-temporal aggregation to aggregate high-level spatial features and temporal transition features. Through extensive experiments, FASTEN only requires five frames of input and outperforms eight competitors for both intra-dataset and cross-dataset evaluations in terms of multiple detection metrics. Moreover, FASTEN has been deployed in real-world mobile devices for practical 3D mask detection.

Lack of large available datasets fully annotated is a fundamental bottleneck in pulmonary nodule detection, especially when the sensing equipment and the corresponding computed tomography (CT) images obtained are device dependent. This work presents a novel cross modal scheme, pursuing modal alignment, to facilitate our aggregate channel detector training. Named as multi-class cycle-consistent adversarial network (CycleGAN), our proposed framework utilizes a generative adversarial model to transfer nodule morphological characteristics from source modal to target modal, and we propose an end to end objective function to unify the transfer and detection procedures. The outputs of the two parts are combined with a dedicated fusion method for final classification. Extensive experimental results on 1948 scans of the private dataset demonstrate the proposed modal transfer method is very effective in data augmentation.

Firstly, a complex network model of participants in the open source design community OpenIDEO is built, and the network index such as degree and degree distribution of complex network model are analyzed. By clustering analysis based on the network index, participants are divided into three categories, respectively the core users, information dissemination users and ordinary users. In addition, we assume that the proportion and activeness of information transmission users plays a key role for the evolution of the network. Then, by clustering participants of each project and analyzing the performance of each project, the assumption is verified. The study result is helpful to optimize the mechanism construction of the open source design community and to promote the benign development of the community, which lay the foundation of improving the efficiency and quality of group development.