Cognitive modeling, as an emerging technology in the field of computer-aided education, aims to explore students’ knowledge levels and learning abilities to achieve various intelligent educational applications. Although some existing work focuses on addressing the problem of student forgetting, it is still a less explored area how to naturally integrate the forgetting effect caused by the time interval between answering exercises into student knowledge state modeling. Additionally, traditional cognitive modeling methods mostly assume that students answer exercises one by one, which often does not align with real answering behavior and cannot be directly extended to diverse learning scenarios. Therefore, in this article, we propose a Continuous Time-based Neural Cognitive (CT-NC) framework and several implemented models (CT-NCM and two extensions) to effectively integrate the dynamic and continuous characteristics of knowledge forgetting into student learning process modeling, making it more natural. Specifically, we adopt a specially designed learning event encoding method to adjust the neural Hawkes process to capture the relationship between knowledge learning and forgetting over continuous time. Furthermore, we propose a customizable learning function to jointly model the changes in different knowledge states and their interaction with each practice moment. In the end, we demonstrate an extension CT-NCM+ that can adapt well to diverse learning scenarios, indicating that CT-NCM can solve real-world problems by flexibly adjusting its structure. Extensive experimental results on real datasets clearly demonstrate that CT-NCM and CT-NCM+ outperform the current state-of-the-art KT methods in student performance prediction, while our work points out a realistic research direction for KT and demonstrates its interpretability in knowledge learning visualization.

With the rise of web-based technologies, online tutoring platforms have emerged to provide personalized learning services by modeling learners' engagement behaviors, improving both convenience and efficiency in academic progress. Cognitive diagnosis has been always recognized a essential learner modeling task in personalized education, which aims to infer learners' mastery in specific knowledge concepts by mining and analyzing their practice behavior. However, most existing studies fail to explicitly disentangling the multiple interdependent factors that influence learner's response feedback during the problem-solving process, both in web-based environments and real-world contexts. To address this issue, we propose DISCD, a feedback-centric DIS entangled Cognitive Diagnosis framework for enhancing effective and interpretable learner modeling. Specifically, we first introduce a feedback-centric disentangled encoder grounded in variational inference to effectively characterize learners' cognitive traits by modeling their practice responses. To achieve this, we fully leverage the interaction matrix and the exercise-concept correlation matrix to extract implicit signals in the disentanglement process, employing three dedicated sub-encoders to efficiently and comprehensively capture these attributes. Next, we develop a multi-level cognitive coordination module to systematically model the disentangled cognitive factors, ensuring their seamless integration into the diagnosis decoding process. Finally, we design a cognitive interaction decoder to reconstruct and refine learners' engagement trajectories in exercises. Extensive experiments on four educational datasets validate the effectiveness of the proposed DISCD model in learner modeling for cognitive diagnosis.

Knowledge tracing (KT), which is instrumental in monitoring and forecasting students' knowledge states throughout their learning trajectory in online learning environments, has over the past decade garnered widespread attention due to its pivotal role in facilitating personalized education. Existing KT approaches were mainly invented from the model-centric perspective to overcome the sequence modeling difficulty while not exploiting the potential information of sparsity, thereby limiting their performance. To make full use of the information in the dataset, this paper proposes a data-centric knowledge tracing paradigm, termed LPDG, aiming to generate interaction data between students and exercises by revealing students' Learning Patterns and facilitating the Generation of ideal training Data. Specifically, we propose a learning patterns-guided exercise sequence regenerator, which incorporates Transformer and a tailor-made pattern enhancer, thereby aiding in the extraction of valuable information for generating high-quality training data. Moreover, we devise a learning patterns-guided pseudo-label generator, which leverages the diffusion process to construct pseudo-labels for the regenerated sequences. Afterwards, the fully generated ideal data is incorporated into the training data, and we integrate this framework with various model-centric approaches in KT. Finally, experimental results across datasets clearly demonstrate the efficacy of our proposed LPDG framework.

Learning path recommendation (LPR) aims to provide individualized and effective learning item routes by modeling learners' learning histories and goals, which has been widely considered a essential task in the field of personalized education. Indeed, considerable research efforts have been dedicated to this direction in recent years, focusing on step-based and sequence-based modeling approaches. However, most of existing studies overlook the complementarity between explicit and implicit relationships among knowledge concepts, while failing to harmonize static knowledge structures with dynamic path generation. To this end, in this paper, we propose LIGHT, a knowLedge topology-aware sequence optImization model for enhancing learninG patH recommendaTion. Specifically, we first construct a composite concept graph that incorporates explicit prerequisite relationships and implicit collaborative relationships, achieved by mining interaction statistics and collaborative signals from learners' learning processes. Next, we design a complementary contrastive fusion module to fully capture the interplay between the two relational views of concepts through graph structure learning and contrastive constraints, which enhances the effectiveness of the learned representations. Following this, we introduce a knowledge topology-aware modeling module that integrates structural semantics clustering with candidate path sampling. Finally, we develop a bidirectional sensing path optimization network to deeply model and optimize the sampled paths from a sequential perspective, thereby enhancing modeling efficiency while preserving structural semantics. Extensive experiments on three real-world educational datasets clearly demonstrate the effectiveness of the proposed LIGHT model in the LPR task.

With the rapid development of intelligent education, Computerized Adaptive Testing(CAT) has garnered significant attention for its ability to tailor exercises to individual examinees. The adaptability of CAT is primarily achieved through the alternating optimization of two core components: the cognitive diagnosis model and the exercise selection module. However, existing CAT approaches, despite their remarkable achievements, often come at the expense of high time costs. Statistical-based approaches incur increased time overhead due to complex computations, while data-driven approaches further exacerbate time inefficiency because of the iterative processes in reinforcement learning, making it challenging to balance evaluation effectiveness and time efficiency. To this end, in this paper, we propose HashCAT, an efficient CAT approach based on learning to hash, aiming to balance efficiency and evaluation effectiveness. Our approach comprises two stages: the hash representation generation and the exercise selection. In the first stage, we design an information alignment module and a novel cognitive diagnosis function to model the interaction between examinees and exercises, generating hash representations with clear physical significance. In the second stage, we propose an uncertainty reduction-based algorithm that utilize information entropy to quantify the uncertainty in student ability estimation and selects exercises that most effectively reduce this uncertainty. Experimental results on four real-world datasets demonstrate that the proposed method significantly improves question selection efficiency while maintaining competitive evaluation performance. The code exists anonymously in https://github.com/sherklock/Intelligent-Education/tree/main/HashCAT-main.

In the realm of education, both independent learning and group learning are esteemed as the most classic paradigms. The former allows learners to self-direct their studies, while the latter is typically characterized by teacher-directed scenarios. Recent studies in the field of intelligent education have leveraged deep temporal models to trace the learning process, capturing the dynamics of students' knowledge states, and have achieved remarkable performance. However, existing approaches have primarily focused on modeling the independent learning process, with the group learning paradigm receiving less attention. Moreover, the reciprocal effect between the two learning processes, especially their combined potential to foster holistic student development, remains inadequately explored. To this end, in this paper, we propose RIGL, a unified Reciprocal model to trace knowledge states at both the individual and group levels, drawing from the Independent and Group Learning processes. Specifically, we first introduce a time frame-aware reciprocal embedding module to concurrently model both student and group response interactions across various time frames. Subsequently, we employ reciprocal enhanced learning modeling to fully exploit the comprehensive and complementary information between the two behaviors. Furthermore, we design a relation-guided temporal attentive network, comprised of dynamic graph modeling coupled with a temporal self-attention mechanism. It is used to delve into the dynamic influence of individual and group interactions throughout the learning processes, which is crafted to explore the dynamic intricacies of both individual and group interactions during the learning sequences. Conclusively, we introduce a bias-aware contrastive learning module to bolster the stability of the model's training. Extensive experiments on four real-world educational datasets clearly demonstrate the effectiveness of the proposed RIGL model. Our codes are available at https://github.com/LabyrinthineLeo/RIGL.

With the rapid development of intelligent education platforms, how to enhance the performance of diagnosing students' knowledge proficiency has become an important issue, e.g., by incorporating the prerequisite relation of knowledge concepts. Unfortunately, the differentiated influence from different predecessor concepts to successor concepts is still underexplored in existing approaches. To this end, we propose a Prerequisite Attention model for Knowledge Proficiency diagnosis of students (PAKP) to learn the attentive weights of precursor concepts on successor concepts and model it for inferring the knowledge proficiency. Specifically, given the student response records and knowledge prerequisite graph, we design an embedding layer to output the representations of students, exercises, and concepts. Influence coefficient among concepts is calculated via an efficient attention mechanism in a fusion layer. Finally, the performance of each student is predicted based on the mined student and exercise factors. Extensive experiments on real-data sets demonstrate that PAKP exhibits great efficiency and interpretability advantages without accuracy loss.