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.

The rapid development of online recruitment platforms has created unprecedented opportunities for job seekers while concurrently posing the significant challenge of quickly and accurately pinpointing positions that align with their skills and preferences. Job recommendation systems have significantly alleviated the extensive search burden for job seekers by optimizing user engagement metrics, such as clicks and applications, thus achieving notable success. In recent years, a substantial amount of research has been devoted to developing effective job recommendation models, primarily focusing on text-matching based and behavior modeling based methods. While these approaches have realized impressive outcomes, it is imperative to note that research on the explainability of recruitment recommendations remains profoundly unexplored. To this end, in this paper, we propose DISCO, a hierarchical Disentanglement based Cognitive diagnosis framework, aimed at flexibly accommodating the underlying representation learning model for effective and interpretable job recommendations. Specifically, we first design a hierarchical representation disentangling module to explicitly mine the hierarchical skill-related factors implied in hidden representations of job seekers and jobs. Subsequently, we propose level-aware association modeling to enhance information communication and robust representation learning both inter- and intra-level, which consists of the inter-level knowledge influence module and the level-wise contrastive learning. Finally, we devise an interaction diagnosis module incorporating a neural diagnosis function for effectively modeling the multi-level recruitment interaction process between job seekers and jobs, which introduces the cognitive measurement theory. Extensive experiments on two real-world recruitment recommendation datasets and an educational recommendation dataset clearly demonstrate the effectiveness and interpretability of our proposed DISCO framework. Our codes are available at https://github.com/LabyrinthineLeo/DISCO.

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.

Cognitive diagnosis has been widely recognized as a crucial task in the field of computational education, which is capable of learning the knowledge profiles of students and predicting their future exercise performance. Indeed, considerable research efforts have been made in this direction over the past decades. However, most of the existing studies only focus on individual-level diagnostic modeling, while the group-level cognitive diagnosis still lacks an in-depth exploration, which is more compatible with realistic collaborative learning environments. To this end, in this paper, we propose aRelation-guidedDual-sideGraphTransformer (RDGT) model for achieving effective group-level cognitive diagnosis. Specifically, we first construct the dual-side relation graphs (i.e., student-side and exercise-side) from the group-student-exercise heterogeneous interaction data for explicitly modeling associations between students and exercises, respectively. In particular, the edge weight between two nodes is defined based on the similarity of corresponding student-exercise interactions. Then, we introduce two relation-guided graph transformers to learn the representations of students and exercises by integrating the whole graph information, including both nodes and edge weights. Meanwhile, the inter-group information has been incorporated into the student-side relation graph to further enhance the representations of students. Along this line, we design a cognitive diagnosis module for learning the groups' proficiency in specific knowledge concepts, which includes an attention-based aggregation strategy to obtain the final group representation and a hybrid loss for optimizing the performance prediction of both group and student. Finally, extensive experiments on 5 real-world datasets clearly demonstrate the effectiveness of our model as well as some interesting findings (e.g., the representative groups and potential collaborations among students).

Cognitive Diagnosis has been widely investigated as a fundamental task in the field of education, aiming at effectively assessing the students' knowledge proficiency level by mining their exercise records. Recently, group-level cognitive diagnosis is also attracting attention, which measures the group-level knowledge proficiency on specific concepts by modeling the response behaviors of all students within the classes. However, existing work tends to explore group characteristics with a coarse-grained perspective while ignoring the inter-individual variability within groups, which is prone to unstable diagnosis results. To this end, in this paper, we propose a novel Homogeneous cohort-aware Group Cognitive Diagnosis model, namely HomoGCD, to effectively model the group's knowledge proficiency level from a multi-grained modeling perspective. Specifically, we first design a homogeneous cohort mining module to explore subgroups of students with similar ability status within a class by modeling their routine exercising performance. Then, we construct the mined cohorts into fine-grained organizations for exploring stable and uniformly distributed features of groups. Subsequently, we develop a multi-grained modeling module to comprehensively learn the cohort and group ability status, which jointly trains both interactions with the exercises. In particular, an extensible diagnosis module is introduced to support the incorporation of different diagnosis functions. Finally, extensive experiments on two real-world datasets clearly demonstrate the generality and effectiveness of our HomoGCD in group as well as cohort~assessments.