Cognitive Diagnosis is a critical task in computer-assisted education, aimed at assessing students' mastery of knowledge concepts and analyzing exercise indices. In fact, this direction has received a lot of research attention in the past few decades. However, the inherent heterogeneity in students' abilities introduces significant challenges to accurate exercise indices estimation, resulting biases that lead to inaccurate diagnostics within student groups and undermining the generalizability of exercise indices across diverse groups. To address these challenges, we propose a Counterfactual Adaptive-Debiasing Framework (CADF) for Cognitive Diagnosis, which employs a causal graph to model the intricate relationships among key variables influencing student performance and knowledge mastery. Specifically, by introducing exercise adjustment factors, we capture both the intrinsic attributes of exercises and their dynamic adaptability to individual students. Then, to disentangle the direct and indirect effects of these factors, we adopt a counterfactual inference approach to answer the critical question:How would the diagnostic feedback from a cognitive diagnosis model change if it were only directly influenced by exercise adjustment factors?This allows CADF to retain the beneficial indirect effects while neutralizing the direct effects that introduce bias, thereby achieving debiased exercise indices estimation. Finally, Extensive experiments on three real-world datasets demonstrate that CADF significantly reduces bias in exercise indices estimation and enhances the accuracy of diagnostic feedback.

Cognitive diagnosis is a key task in computer-aided education, aimed at assessing a students' proficiency in specific knowledge concepts based on their responses to exercises. However, existing cognitive diagnosis models often overlook anomalies in students and exercises. For instance, some students might incorrectly response exercises despite having a strong grasp of the knowledge concept, or they might response correctly despite a lack of understanding. Such subtle anomalies can adversely affect the diagnostic results of the models. To address these anomalies, we conduct a qualitative analysis of how anomalous student states and exercise properties impact response outcomes using causal diagrams. We propose a framework named Anomaly Detection for Cognitive Diagnosis (AD4CD) to enhance the ability of Learning-to-Detect-Anomalous. AD4CD approaches the problem from a causal perspective, analyzing confounding paths that affect the true causal relationship between student ability and response outcomes, and designing an anomaly detection mechanism suitable for cognitive diagnostic models. Specifically, we first account for anomalous student behaviors and exercise properties and introduce response times from both students and exercises as modeling factors. By quantifying the response time distributions in high-dimensional features, we identify anomalies within skewed distributions, including both left-tail and right-tail anomalies. Using the detected anomaly scores, we comprehensively model the students' anomalous behaviors and exercise anomalies. Additionally, we reconstruct unbiased true abilities under natural conditions and use reconstruction loss as an anomaly score to assist in modeling guessing and slipping features. Lastly, AD4CD leverages a general cognitive diagnosis model as its backbone, optimizing the guessing and slipping features to provide unbiased and accurate feedback. Extensive experimental results demonstrate that AD4CD effectively captures anomalous data in the diagnostic process across three real-world datasets, enhancing the accuracy of the diagnostic results.

Computerized adaptive testing(CAT) is a crucial task in computer-aided education, which aims to adaptively select suitable question to diagnose examinees' ability status. Existing CAT approaches enhance selection performance by exploring examinee-question(E-Q) relation. These approaches either exclusively utilize explicit E-Q relation. For instance, policy-based approaches determine question selection based on predefined criteria. While effective in adapting to changes in question banks, these methods often entail significant computational costs in searching for suitable questions. Conversely, some studies focus solely on implicit E-Q relation. For example, learning-based approaches train agents to efficiently select questions by learning from large-scale datasets. However, they may struggle with newly introduced questions. Additionally, most of these existing question selectors are based on greedy strategies, which potentially overlooks promising quuestions. To bridge the above two types of approaches, we propose a novel framework named Relation Exploiting-based CAT(RECAT) by exploring and exploiting the implicit and explicit examinee-question relation. Specifically, we first define an examinee true ability-oriented selection objective to select more suitable questions. Then, to learn the implicit E-Q relation, we design a question selector, which explores the examinee ability and generates best-fitting questions for specific examinee ability from two aspects, including generation consistency and knowledge matching. The former aims to maximize the likelihood estimation of the implicit E-Q relation learning process, while the latter is employed to fit the distribution of real questions. To fully exploit explicit E-Q relation, we generate a high-quality candidate set for the given examinee's ability using implicit E-Q relation, which streamlines the search process, minimizing selection latency. We demonstrate the effectiveness and efficiency of our framework through comprehensive experiments on real-world datasets.

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.

The state space models, employing recursively propagated features, demonstrate strong representation capabilities comparable to Transformer models and superior efficiency. However, constrained by the inherent geometric constraints of sequences, it still falls short in modeling long-range dependencies. To address this issue, we propose the MambaTree network, which first dynamically generates a tree topology based on spatial relationships and input features. Then, feature propagation is performed based on this graph, thereby breaking the original sequence constraints to achieve stronger representation capabilities. Additionally, we introduce a linear complexity dynamic programming algorithm to enhance long-range interactions without increasing computational cost. MambaTree is a versatile multimodal framework that can be applied to both visual and textual tasks. Extensive experiments demonstrate that our method significantly outperforms existing structured state space models on image classification, object detection and segmentation. Besides, by fine-tuning large language models, our approach achieves consistent improvements in multiple textual tasks at minor training cost.