Knowledge tracing (KT) is a critical component of intelligent tutoring systems, which aim to track and predict students’ evolving knowledge states based on their interaction histories in online learning environments. However, most existing KT models fail to account for the periodic nature of student learning, in which knowledge acquisition occurs in concentrated study sessions, followed by periods of forgetting during breaks. To address this limitation, we propose periodic-aware knowledge tracing (PAKT), a novel model that explicitly captures these periodic learning and forgetting patterns. By leveraging Fourier transforms, PAKT identifies key periodic structures in the time intervals between student interactions, enabling the model to simulate both intraperiod learning gains and interperiod forgetting. Furthermore, we incorporate a causal self-attention mechanism to capture intricate dependencies among interactions, improving the accuracy of knowledge state estimation. Experiments on three public KT datasets demonstrate that PAKT outperforms existing methods in terms of prediction accuracy, highlighting the benefits of modeling periodicity for more effective and interpretable KT. Our results show that PAKT provides a more realistic and robust approach to personalized learning, making it a valuable tool for adaptive educational systems.

A critical component of fine-grained entity typing is the existence of precise relationship between entity types, such as type hierarchy. Previous approaches for fine-grained entity typing typically model the type hierarchy in vector space, which causes it extremely hard to precisely capture the complex relationship between entity types. To overcome the challenge of modeling type hierarchy in vector space, this article proposes for the first time to model the type hierarchy in high-dimensional box space. In addition, previous approaches focus more on the influence of the context of entity mentions, while neglecting the influence of entity mentions themselves. Based on the above challenges, we present a new approach called THBox, which not only successfully boosts the influence of entity mentions but also models the type hierarchy well. To verify the effectiveness of the method presented in this article, experimental results on three publicly available fine-grained entity typing benchmark datasets are provided to verify that the presented method is a new state-of-the-art solution for fine-grained entity typing.

Knowledge Tracing (KT) refers to inferring the students' knowledge mastery and predicting their future performance. KT serves as the foundation for personalized learning and enhances the effectiveness of educational interventions, becoming a crucial technology in intelligent tutoring systems. Recent approaches have demonstrated notable success by harnessing the potent representational capacities of deep learning. However, complex neural networks lead to entangled knowledge state embeddings, where the embedding dimensions are coupled, limiting their expressiveness and interpretability. In addition, the limitations of existing methods in Euclidean space result in distortions when capturing complex relationships among knowledge states. This distortion manifests as the distance and geometric structures among knowledge states being deformed during the embedding process. To address the challenges, in this paper, we propose a hyperbolic hypergraph transformer with knowledge stateDisentanglement forKnowledgeTracing, named DisenKT. We construct the students' response sequences into the hypergraph, projected into the hyperbolic space to alleviate the representation distortion problem of questions and knowledge states. The embeddings of hierarchical knowledge states are refined through message passing between questions and students based on the proposed hyperbolic hypergraph transformer. Moreover, we are the first to disentangle knowledge states via a contrastive clustering auxiliary task, which enhances the expressiveness and interpretability of knowledge state embeddings. Extensive experimental results on three public datasets demonstrate that DisenKT outperforms state-of-the-art methods on student performance prediction and interpretability.

Knowledge tracing (KT) refers to predicting learners' performance in the future according to their historical responses, which has become an essential task in intelligent tutoring systems. Most deep learning-based methods usually model the learners' knowledge states via recurrent neural networks (RNNs) or attention mechanisms. Recently emerging graph neural networks (GNNs) assist the KT model to capture the relationships such as question-skill and question-learner. However, non-pairwise and complex higher-order information among responses is ignored. In addition, a single-channel encoded hidden vector struggles to represent multigranularity knowledge states. To tackle the above problems, we propose a novel KT model named dual-channel adaptive scale hypergraph encoders with cross-view contrastive learning (HyperKT). Specifically, we design an adaptive scale hyperedge distillation component for generating knowledge-aware hyperedges and pattern-aware hyperedges that reflect non-pairwise higher-order features among responses. Then, we propose dual-channel hypergraph encoders to capture multigranularity knowledge states from global and local state hypergraphs. The encoders consist of a simplified hypergraph convolution network and a collaborative hypergraph convolution network. To enhance the supervisory signal in the state hypergraphs, we introduce the cross-view contrastive learning mechanism, which performs among state hypergraph views and their transformed line graph views. Extensive experiments on three real-world datasets demonstrate the superior performance of our HyperKT over the state-of-the-art (SOTA).

Knowledge Tracing (KT) aims to assess students’ mastery of knowledge concepts and predict their performance from their historical response records. However, most existing KT models only consider the correspondence between questions and knowledge concepts given in the dataset when constructing the question-knowledge concept structure. This approach fails to deeply explore the knowledge concepts hidden in the questions and may propagate mislabeled associations. In addition, they overlook the impact of individual differences in students’ learning abilities on the accuracy of KT models. In this paper, we propose EGANKT to solve the above problems by introducing a knowledge concept prediction module and a learning ability prediction module. The knowledge concept prediction module identifies and optimizes potential knowledge concepts within questions, mitigating mislabeling issues. The learning ability prediction module improves the prediction accuracy of the model by calculating students’ learning abilities. To further improve the accurate modeling of knowledge states, we introduce self-supervised tasks to support the KT task through data augmentation and contrastive learning of knowledge states. Experimental results show that the EGANKT model outperforms baseline models on five datasets, demonstrating the effectiveness of our approach.

Named Entity Recognition (NER) is a fundamental task that aids in the completion of other tasks such as text understanding, information retrieval and question answering in Natural Language Processing (NLP). In recent years, the use of a mix of character-word structure and dictionary information for Chinese NER has been demonstrated to be effective. As a representative of hybrid models, Lattice-LSTM has obtained better benchmarking results in several publicly available Chinese NER datasets. However, Lattice-LSTM does not address the issue of long-distance entities or the detection of several entities with the same character. At the same time, the ambiguity of entity boundary information also leads to a decrease in the accuracy of embedding NER. This paper proposes ELCA: Enhanced Boundary Location for Chinese Named Entity Recognition Via Contextual Association, a method that solves the problem of long-distance dependent entities by using sentence-level position information. At the same time, it uses adaptive word convolution to overcome the problem of several entities sharing the same character. ELCA achieves the state-of-the-art outcomes in Chinese Word Segmentation and Chinese NER.

Knowledge tracing (KT) refers to predicting learners’ performance in the future according to their historical learning interactions, which has become an essential task for the computer-aided education (CAE) system. Recent studies alleviate the data sparsity problem by mining higher-order information between questions and skills. However, the effect of multiple skills in the question is not distinguished, and various learning behaviors need to be better modeled. In this article, we propose a knowledge-associated embedding for the memory-aware KT (KMKT) framework. Specifically, we first construct a question-skill bipartite graph with attribute features. A knowledge-associated embedding (KAE) module is proposed to capture the distinctiveness of multiskills via the process of knowledge propagation and knowledge aggregation based on predefined knowledge-paths. Then, to simulate the memory recall phenomenon of the learners in KT, we design a memory-aware module for long short-term memory (MA-LSTM) networks. A temporal attention layer in MA-LSTM is proposed to learn the forgetting mechanism of the human brain. Finally, we introduce a learning-gain (LG) layer to obtain learners’ benefits after each exercise. Extensive experiments on four real-world datasets illustrate that our KMKT model performs better than the other baseline models, which verifies the effectiveness of our work.

Different from traditional vector-based fine-grained entity typing methods, the box-based method is more effective in capturing the complex relationships between entity mentions and entity types. The box-based fine-grained entity typing method projects entity types and entity mentions into high-dimensional box space, where entity types and entity mentions are embedded asd-dimensional hyperrectangles. However, the impacts of entity types are not considered during classification in high-dimensional box space, and the model cannot be optimized precisely when two boxes are completely separated or overlapped in high-dimensional box space. Based on the above shortcomings, anImprovedBoxEmbeddings (IBE) method for fine-grained entity typing is proposed in this work. The IBE not only introduces the impacts of entity types during classification in high-dimensional box space, but also proposes a distance based module to optimize the model precisely when two boxes are completely separated or overlapped in high-dimensional box space. Experimental results on four fine-grained entity typing datasets verify the effectiveness of the proposed IBE, demonstrating that IBE is a state-of-the-art method for fine-grained entity typing.