Knowledge tracing aims to model learners’ cognitive state dynamically from interaction sequences to support personalized instructional decisions. While existing methods achieve good prediction accuracy, they often overlook the transfer effects between knowledge concepts (KCs) and their propagation, limiting fine-grained, structured modeling of mastery and overall performance. Although some studies incorporate knowledge transfer using predefined KC similarity graphs, they assume static transfer structures for all learners, neglecting the continuous evolution of transfer abilities due to interventions and self-regulation, and focus solely on KC-hierarchy relations. To overcome these limitations, we propose MHAKT. MHAKT operates across multiple hierarchies of knowledge components through three modules: (1) Transfer perception module utilizes a masked attention mechanism to identify the contribution of the Top- \( K \) most relevant historical interactions for the target knowledge component, dynamically updating the learner-specific transfer structure; (2) Knowledge transfer module employs hypergraph neural networks to comprehensively model many-to-many transfer processes among knowledge components; (3) Cognition update module consolidates new knowledge while applying forgetting mechanisms to update the learner’s cognitive state. Extensive experiments on benchmark datasets demonstrate that MHAKT significantly outperforms thirteen baseline models. In particular, under data sparsity and generalization settings designed to simulate cold-start knowledge components, MHAKT shows strong robustness and maintains superior predictive accuracy. Ablation studies and exploratory experiments further validate the essential contribution of each module, and visualization analyses further reveal MHAKT’s potential for explainable modeling.

The rapid advancement of multimedia technologies and their increasing integration in education have underscored the importance of multimedia learning. Knowledge Tracing (KT) plays a crucial role in enabling adaptive multimedia learning by continuously monitoring students' progress and forecasting their performance throughout the learning process. Question lies at the heart of the KT process, making its representation crucial for building efficient KT models. However, the sparsity and complexity of question data pose significant challenges for existing methods to capture the underlying features of questions, thereby affecting the accuracy of knowledge state predictions. To address this issue, this paper attempts to introduce the diffusion model to the KT field, proposing a novel knowledge tracing model, DiffuQKT. The model presents a diffusion-based generative approach for question representation and enhances the stability of knowledge states through contrastive learning. Specifically, DiffuQKT first constructs question representations based on their concepts, difficulty, and variations, and then, during the forward phase, progressively adds noise to the question representations, disrupting them into a Gaussian distribution. In the reverse phase, DiffuQKT gradually recovers the representations from noise, generating higher-quality question representations for knowledge tracing. Furthermore, to guide more meaningful question generation, we incorporate question concepts and difficulty as conditions during the denoising process. In addition, to improve the robustness of knowledge states against subtle variations in question representations, we employ contrastive learning to stabilize knowledge states across both original and denoised question representations. We conduct extensive experiments on four public datasets, comparing DiffuQKT with 15 baseline methods. The results demonstrate that DiffuQKT significantly outperforms existing models. Moreover, we find that the diffusion-based generative approach for question representation proposed in this paper has the ability to significantly improve the performance of baseline models. The code can be found at https://github.com/lilstrawberry/DiffuQKT.

Mining learner preferences and needs from individual learning behavior data is a critical task in course recommendation systems. While graph-based models have shown efficacy in capturing pairwise relationships between learners and courses, they often overlook the complex higher-order interactions involving learners, courses and teachers that are essential for accurate recommendations. To address this limitation, we propose a novel Hypergraph Convolutional Network for Course Recommendation (HCNCR) framework, designed to model these higher-order interactions effectively. Our approach constructs course and learner hypergraphs based on course attributes and learner similarity relations, respectively. By employing hypergraph convolution, we capture the intrinsic higher-order relationships within these hypergraphs. Additionally, we utilize graph convolutional layers on the learner-course bipartite graph to integrate embeddings derived from hypergraphs, achieving comprehensive representations of both learners and courses. Extensive experiments conducted on real-world datasets demonstrate that HCNCR significantly outperforms existing state-of-the-art methods in course recommendation tasks.

Knowledge Tracing (KT) is vital for education, continuously monitoring students' knowledge states (mastery of knowledge) as they interact with online education materials. Despite significant advancements in deep learning-based KT models, existing approaches often struggle to strike the right balance in granularity, leading to either overly coarse or excessively fine tracing and representation of students' knowledge states, thereby limiting their performance. Additionally, achieving a high-performing model while ensuring interpretability presents a challenge. Therefore, in this paper, we propose a novel approach called Multiscale-state-based Interpretable Knowledge Tracing (MIKT). Specifically, MIKT traces students' knowledge states on two scales: a coarse-grained representation to trace students' domain knowledge state, and a fine-grained representation to monitor their conceptual knowledge state. Furthermore, the classical psychological measurement model, IRT (Item Response Theory), is introduced to explain the prediction process of MIKT, enhancing its interpretability without sacrificing performance. Additionally, we extended the Rasch representation method to effectively handle scenarios where questions are associated with multiple concepts, making it more applicable to real-world situations. We extensively compared MIKT with 20 state-of-the-art KT models on four widely-used public datasets. Experimental results demonstrate that MIKT outperforms other models while maintaining its interpretability. Moreover, experimental observations have revealed that our proposed extended Rasch representation method not only benefits MIKT but also significantly improves the performance of other KT baseline models. The code can be found at https://github.com/lilstrawberry/MIKT.

Knowledge tracing (KT) has become an increasingly relevant problem in intelligent education services. Deep learning-based knowledge tracing (DLKT) achieves superb performance in terms of prediction accuracy, but it lacks of explainability, which makes us hard to trust or understand models. The previous work on explaining DLKT was mainly based on gradients or attention scores, which is susceptible to spurious correlations, reducing the credibility of the explanation. To address this limitation, in this paper, we propose a causal explanation method based on the genetic algorithm (GA), named Genetic Causal Explainer(GCE), which constructs a causal framework to estimate the attribution of subsequence to the predictions of DLKT models, and a genetic coding system is designed. Further, A multi-strategy initialization method inspired by domain prior knowledge is proposed, and a global empirical matrix is introduced to capture the causal correlation knowledge during the search process across instances, and guiding the mutation operators. The GCE as a post hoc explanation method can generate explanation results without affecting model training, and can be applied to analyze different DLKT models. Experimental results demonstrate the GCE perform better than other explanation methods in terms of accuracy and readability in quantitative assessments. Meanwhile, the GCE also shows good application prospects in mining educational laws and comparing KT models.

Recently, online education has become popular. Many e-learning platforms have been launched with various intelligent services aimed at improving the learning efficiency and effectiveness of learners. Graphs are used to describe the pairwise relations between entities, and the node embedding technique is the foundation of many intelligent services, which have received increasing attention from researchers. However, the graph in the intelligent education scenario has three noteworthy properties, namely, heterogeneity, evolution, and lopsidedness, which makes it challenging to implement ecumenical node embedding methods on it. In this article, an autobalanced multitask node embedding model is proposed, named MNE, and applied to the interaction graph, settling a few actual tasks in intelligent education. More specifically, MNE builds two purpose-built self-supervised node embedding learning tasks for heterogeneous evolutive graphs. Edge-specific reconstruction tasks are built according to the semantic information and properties of the heterogeneous edges, and an evolutive weight regression task is designed, aiding the model to perceive the evolution of learners' implicit cognitive states. Then, both aleatoric and epistemic uncertainty quantification techniques are introduced, achieving both task- and node-level weight estimation and instructing subtask autobalancing. Experimental results on real-world datasets indicate that the proposed model outperforms the state-of-the-art graph embedding methods on two assessment tasks and demonstrates the validity of the proposed multitask framework and subtask balancing mechanism. Our implementations are available at https://github.com/ccnu-mathits/MNE4HEN.

Graph embedding (GE) aims to acquire low-dimensional node representations while maintaining the graph’s structural and semantic attributes. Intelligent tutoring systems (ITS) signify a noteworthy achievement in the fusion of AI and education. Utilizing GE to model ITS can elevate their performance in predictive and annotation tasks. Current GE techniques, whether applied to heterogeneous or dynamic graphs, struggle to efficiently model ITS data. The GEs within ITS should retain their semidynamic, independent, and smooth characteristics. This article introduces a heterogeneous evolution network (HEN) for illustrating entities and relations within an ITS. Additionally, we introduce a temporal extension graph neural network (TEGNN) to model both evolving and static nodes within the HEN. In the TEGNN framework, dynamic nodes are initially improved over time through temporal extension (TE), providing an accurate depiction of each learner’s implicit state at each time step. Subsequently, we propose a stochastic temporal pooling (STP) strategy to estimate the embedding sets of all evolving nodes. This effectively enhances model efficiency and usability. Following this, a heterogeneous aggregation network is devised to proficiently extract heterogeneous features from the HEN. This network employs both node-level and relation-level attention mechanisms to craft aggregated node features. To emphasize the superiority of TEGNN, we perform experiments on several real ITS datasets and show that our method significantly outperforms the state-of-the-art approaches. The experiments validate that TE serves as an efficient framework for modeling temporal information in GE, and STP not only accelerates the training process but also enhances the resultant accuracy.

Technology is gradually being incorporated as an integral part of education. This technology includes the now pervasive presence of the Internet, but also the inclusion of devices that are worn by teachers and learners. Wearable devices have greatly facilitated data acquisition of educational subjects. This article provides an overview of what and how wearable devices have been used in education and in what contexts. Summaries of existing research in this area are organized according to a three-layered data framework. We begin by presenting the technical characteristics of four types of wearable devices, and comparing the data from them with physiological data and behavioral data. Based on this, the preprocessing and analysis methods of these two types of data are discussed. Following that, we identified the six main applications that wearables and data analytics are enabling education. Furthermore, three key challenges of using wearable devices have been identified. We suggest that future research should be enriched from equipment innovation, application scenarios, and feedback methods in order to make wearable devices more widely used in a production environment, and augment the analysis performed to improve teaching, learning, or the educational context where it occurs.