Learner-item cognitive modeling plays a central role in the web-based online intelligent education system by enabling cognitive diagnosis (CD) across diverse online educational scenarios. Although ID embedding remains the mainstream approach in cognitive modeling due to its effectiveness and flexibility, recent advances in language models (LMs) have introduced new possibilities for incorporating rich semantic representations to enhance CD performance. This highlights the need for a comprehensive analysis of how LMs enhance embeddings through semantic integration across mainstream CD tasks. This paper identifies two key challenges in fully leveraging LMs in existing work: Misalignment between the training objectives of LMs and CD models creates a distribution gap in feature spaces; A unified framework is essential for integrating textual embeddings across varied CD tasks while preserving the strengths of existing cognitive modeling paradigms to ensure the robustness of embedding enhancement. To address these challenges, this paper introduces EduEmbed, a unified embedding enhancement framework that leverages fine-tuned LMs to enrich learner-item cognitive modeling across diverse CD tasks. EduEmbed operates in two stages. In the first stage, we fine-tune LMs based on role-specific representations and an interaction diagnoser to bridge the semantic gap of CD models. In the second stage, we employ a textual adapter to extract task-relevant semantics and integrate them with existing modeling paradigms to improve generalization. We evaluate the proposed framework on four CD tasks and computerized adaptive testing (CAT) task, achieving robust performance. Further analysis reveals the impact of semantic information across diverse tasks, offering key insights for future research on the application of LMs in CD for online intelligent education systems.

Computerized Adaptive Testing (CAT) is a testing technique that accurately infers students' proficiency levels using a relatively small number of questions.Most existing CAT systems operate on a question-level adaptive paradigm, which is suitable for practice scenarios.However, in computerized standardized high-stakes examinations such as the GRE and GMAT, this paradigm faces several challenges: (1) the lack of comparability in exam results, (2) high implementation costs due to the reliance on real-time interactions and the financial burden of maintaining CAT testing system, and (3) the difficulty in balancing multiple factors of diagnosis quality, attribute coverage, and question exposure.To address these challenges, we propose a Paper-level Computerized Adaptive Testing (PCAT) and its corresponding evaluation method.PCAT divides an exam into multiple testing stages, where examinees adaptively receive test papers of varying difficulty based on their performance in previous stages.The paper assembly problem in PCAT is solved using a population-based multi-objective optimization (MOO) approach.PCAT offers several advantages: First, the paper-level adaptive mechanism ensures that the questions faced by examinees depend solely on their performance in the earlier stages, maintaining adaptability while enhancing the comparability of results across different examinees.Second, PCAT replaces the selection strategy module in traditional CAT with an assembly module, allowing computationally intensive tasks such as cognitive diagnosis and paper assembly to be completed offline before the exam, eliminating the need for real-time interactions.Additionally, the population-based MOO approach generates a set of high-quality solutions in one run, meeting the demands of frequent administration of standardized high-stakes exams like the GRE and reducing the financial burden of maintaining a large-scale CAT system.Finally, MOO naturally

Knowledge Tracing (KT) is a crucial task in computer-aided education and intelligent tutoring systems, predicting students’ performance on new questions from their responses to prior ones. An accurate KT model can capture a student’s mastery level of different knowledge topics, as reflected in their predicted performance on different questions. This helps improve the learning efficiency by suggesting appropriate new questions that complement students’ knowledge states. However, current KT models have significant drawbacks that they neglect the imbalanced discrimination of historical responses. A significant proportion of question responses provide limited information for discerning students’ knowledge mastery, such as those that demonstrate uniform performance across different students. Optimizing the prediction of these cases may increase overall KT accuracy, but also negatively impact the model’s ability to trace personalized knowledge states, especially causing a deceptive surge of performance. Towards this end, we propose a framework to reweight the contribution of different responses based on their discrimination in training. Additionally, we introduce an adaptive predictive score fusion technique to maintain accuracy on less discriminative responses, achieving proper balance between student knowledge mastery and question difficulty. Experimental results demonstrate that our framework enhances the performance of three mainstream KT methods on three widely used datasets.

Knowledge tracing (KT) is a crucial task in intelligent education, focusing on predicting students' performance on given questions to trace their evolving knowledge. The advancement of deep learning in this field has led to deep-learning knowledge tracing (DLKT) models that prioritize high predictive accuracy. However, many existing DLKT methods overlook the fundamental goal of tracking students' dynamical knowledge mastery. These models do not explicitly model knowledge mastery tracing processes or yield unreasonable results that educators find difficulty to comprehend and apply in real teaching scenarios. In response, our research conducts a preliminary analysis of mainstream KT approaches to highlight and explain such unreasonableness. We introduce GRKT, a graph-based reasonable knowledge tracing method to address these issues. By leveraging graph neural networks, our approach delves into the mutual influences of knowledge concepts, offering a more accurate representation of how the knowledge mastery evolves throughout the learning process. Additionally, we propose a fine-grained and psychological three-stage modeling process as knowledge retrieval, memory strengthening, and knowledge learning/forgetting, to conduct a more reasonable knowledge tracing process. Comprehensive experiments demonstrate that GRKT outperforms eleven baselines across three datasets, not only enhancing predictive accuracy but also generating more reasonable knowledge tracing results. This makes our model a promising advancement for practical implementation in educational settings. The source code is available at https://github.com/JJCui96/GRKT.

Customer segmentation plays a crucial role in credit risk assessment by dividing users into specific risk levels based on their credit scores. Previous methods fail to comprehensively consider the stability in the segmentation process, resulting in frequent changes and inconsistencies in users' risk levels over time. This increases potential risks to a company. To this end, this paper at first introduces and formalizes the concept of stability regret in the segmentation process. However, evaluating stability is challenging due to its black-box nature and the computational burden posed by vast user data sets. To address these challenges, this paper proposes a large-scale stable customer segmentation approach named LASCA. LASCA consists of two phases: high-quality dataset construction (HDC) and reliable data-driven optimization (RDO). Specifically, HDC utilizes an evolutionary algorithm to collect high-quality binning solutions. RDO subsequently builds a reliable surrogate model to search for the most stable binning solution based on the collected dataset. Extensive experiments conducted on real-world large-scale datasets (up to 0.8 billion) show that LASCA surpasses the state-of-the-art binning methods in finding the most stable binning solution. Notably, HDC greatly enhances data quality by 50%. RDO efficiently discovers more stable binning solutions with a 36% improvement in stability, accelerating the optimization process by 25 times via data-driven evaluation. Currently, LASCA has been successfully deployed in the large-scale credit risk assessment system of Alipay.

Cognitive diagnosis models (CDMs) are designed to learn students' mastery levels using their response logs.CDMs play a fundamental role in online education systems since they significantly influence downstream applications such as teachers' guidance and computerized adaptive testing.Despite the success achieved by existing CDMs, we find that they suffer from a thorny issue that the learned students' mastery levels are too similar.This issue, which we refer to as oversmoothing, could diminish the CDMs' effectiveness in downstream tasks.CDMs comprise two core parts: learning students' mastery levels and assessing mastery levels by fitting the response logs.This paper contends that the oversmoothing issue arises from that existing CDMs seldom utilize response signals on exercises in the learning part but only use them as labels in the assessing part.To this end, this paper proposes an oversmoothing-resistant cognitive diagnosis framework (ORCDF) to enhance existing CDMs by utilizing response signals in the learning part.Specifically, OR-CDF introduces a novel response graph to inherently incorporate response signals as types of edges.Then, ORCDF designs a tailored response-aware graph convolution network (RGC) that effectively captures the crucial response signals within the response graph.Via ORCDF, existing CDMs are enhanced by replacing the input embeddings with the outcome of RGC, allowing for the consideration of response signals on exercises in the learning part.Extensive experiments on real-world datasets show that ORCDF not only *

Cognitive diagnosis is a vital upstream task in intelligent education systems. It models the student-exercise interaction, aiming to infer the students' proficiency levels on each knowledge concept. This paper observes that most existing methods can hardly effectively capture the homogeneous influence due to its inherent complexity. That is to say, although students exhibit similar performance on given exercises, their proficiency levels inferred by these methods vary significantly, resulting in shortcomings in interpretability and efficacy. Given the complexity of homogeneous influence, a hypergraph could be a choice due to its flexibility and capability of modeling high-order similarity which aligns with the nature of homogeneous influence. However, before incorporating hypergraph, one at first needs to address the challenges of distorted homogeneous influence, sparsity of response logs, and over-smoothing. To this end, this paper proposes a hypergraph cognitive diagnosis model (HyperCDM) to address these challenges and effectively capture the homogeneous influence. Specifically, to avoid distortion, HyperCDM employs a divide-and-conquer strategy to learn student, exercise and knowledge representations in their own hypergraphs respectively, and interconnects them via a feature-based interaction function. To construct hypergraphs based on sparse response logs, the auto-encoder is utilized to preprocess response logs and K-means is applied to cluster students. To mitigate over-smoothing, momentum hypergraph convolution networks are designed to partially keep previous representations during the message propagation. Extensive experiments on both offline and online real-world datasets show that HyperCDM achieves state-of-the-art performance in terms of interpretability and capturing homogeneous influence effectively, and is competitive in generalization. The ablation study verifies the efficacy of each component, and the case study explicitly showcases the homogeneous influence captured by HyperCDM.

The fraudulent insurance claim is critical for the insurance industry.Insurance companies or agency platforms aim to confidently estimate the fraud risk of claims by gathering data from various sources.Although more data sources can improve the estimation accuracy, they inevitably lead to increased costs.Therefore, a great challenge of fraud risk verification lies in well balancing these two aspects.To this end, this paper proposes a framework named cost-efficient fraud risk optimization with submodularity (CEROS) to optimize the process of fraud risk verification.CEROS efficiently allocates investigation resources across multiple information sources, balancing the trade-off between accuracy and cost.CEROS consists of two parts that we propose: a submodular set-wise classification model * Equal Contribution.

Cognitive diagnosis aims to gauge students' mastery levels based on their response logs. Serving as a pivotal module in web-based online intelligent education systems (WOIESs), it plays an upstream and fundamental role in downstream tasks like learning item recommendation and computerized adaptive testing. WOIESs are open learning environment where numerous new students constantly register and complete exercises. In WOIESs, efficient cognitive diagnosis is crucial to fast feedback and accelerating student learning. However, the existing cognitive diagnosis methods always employ intrinsically transductive student-specific embeddings, which become slow and costly due to retraining when dealing with new students who are unseen during training. To this end, this paper proposes an inductive cognitive diagnosis model (ICDM) for fast new students' mastery levels inference in WOIESs. Specifically, in ICDM, we propose a novel student-centered graph (SCG). Rather than inferring mastery levels through updating student-specific embedding, we derive the inductive mastery levels as the aggregated outcomes of students' neighbors in SCG. Namely, SCG enables to shift the task from finding the most suitable student-specific embedding that fits the response logs to finding the most suitable representations for different node types in SCG, and the latter is more efficient since it no longer requires retraining. To obtain this representation, ICDM consists of a construction-aggregation-generation-transformation process to learn the final representation of students, exercises and concepts. Extensive experiments across real-world datasets show that, compared with the existing cognitive diagnosis methods that are always transductive, ICDM is much more faster while maintains the competitive inference performance for new students.

D numbers has been previously introduced in linguistic decision making due to its effectiveness and flexibility in dealing with uncertain information. The study applies the integration operator of D numbers to obtain the decision result by aggregating different opinions of experts which may be imprecise and uncertain. However, sometimes it would be more reasonable to consider the risk preference of the decision maker. In this paper, we proposed an improved aggregating method for linguistic information based on D numbers and OWAWA operator. The main advantage is that it can integrate the degree of importance that each experts has and the risk preference of the decision maker in the aggregation of linguistic information. An example is used to demonstrate the flexibility and reasonability of the proposed method.