Designing project-based learning (PBL) demands managing highly interdependent components, a task that both traditional linear tools and purely conversational AI struggle with. Traditional tools fail to capture the non-linear nature of creative design, while conversational systems lack the persistent, shared context necessary for reflective collaboration. Grounded in theories of distributed cognition, we introduce CoMAP, a system that embodies a graph-based collaboration paradigm. By providing a shared visual workspace with dual-modality AI support, CoMAP transforms the human-AI relationship from a prompt-and-response loop into a transparent and equitable partnership. Our study with 30 educators shows CoMAP significantly improves teachers’ design expression, divergent thinking, and iterative practice compared to a dialogue-only baseline. These findings demonstrate how a nonlinear, artifact-centric approach can foster trust, reduce cognitive load, and support educators to take control of their creative process. Our contributions are available at: https://comap2025.github.io/.

Assessment literacy (AL) is essential for personalized education, yet difficult to cultivate in pre-service teachers. Conventional teacher preparation programs focus on theoretical knowledge, while digital assessment tools commonly provide opaque scores or parameters. These limitations hinder reflection and transfer, leaving AL underdeveloped. We propose XIA, an eXplainable Intelligent Assessment platform that extends statistics-informed support with visualized cognitive diagnostic reasoning, including contrastive and counterfactual explanations. In a pre-post controlled study with 21 pre-service teachers, we combined quantitative tasks and questionnaires with qualitative interviews. The findings offer preliminary evidence that XIA supported reflection, self-regulation, and assessment awareness, and helped reduce assessment errors. Interviews further showed a shift from score-based judgments toward evidence-based reasoning. This work contributes insights into the design of intelligent assessment tools, showing how explanatory scaffolding can bridge assessment theory and classroom practice and support the cultivation of AL in teacher education.

Personalized learning, especially data-based methods, has garnered widespread attention in recent years, aiming to meet individual student needs. However, many works rely on the implicit assumption that benchmarks are high-quality and well-annotated, which limits their practical applicability. In real-world scenarios, these benchmarks often exhibit long-tail distributions, significantly impacting model performance. To address this challenge, we propose a novel method called Neural-Collapse-Advanced personalized Learning (NCAL), designed to learn features that conform to the same simplex equiangular tight frame (ETF) structure. NCAL introduces Text-modality Collapse (TC) regularization to optimize the distribution of text embeddings within the large language model (LLM) representation space. Notably, NCAL is model-agnostic, making it compatible with various architectures and approaches, thereby ensuring broad applicability. Extensive experiments demonstrate that NCAL effectively enhances existing works, achieving new state-of-the-art performance. Additionally, NCAL mitigates class imbalance, significantly improving the model’s generalization ability.

Model merging aims to integrate the strengths of multiple fine-tuned models into a unified model while preserving task-specific capabilities. Existing methods, represented by task arithmetic, are typically classified into global- and local-aware methods. However, global-aware methods inevitably cause parameter interference, while local-aware methods struggle to maintain the effectiveness of task-specific details in the merged model. To address these limitations, we propose a Consensus Aware Localized Merging (CALM) method which incorporates localized information aligned with global task consensus, ensuring its effectiveness post-merging. CALM consists of three key components: (1) class-balanced entropy minimization sampling, providing a more flexible and reliable way to leverage unsupervised data; (2) an efficient-aware framework, selecting a small set of tasks for sequential merging with high scalability; (3) a consensus-aware mask optimization, aligning localized binary masks with global task consensus and merging them conflict-free. Experiments demonstrate the superiority and robustness of our CALM, significantly outperforming existing methods and achieving performance close to traditional MTL.

Large language models (LLMs) have obtained promising results in mathematical reasoning, a foundational human intelligence skill. Most previous studies focus on improving or measuring the performance of LLMs via textual math datasets (e.g., MATH, GSM8K). In this paper, we release a Chinese multimodal math (CMM-Math) dataset, including benchmark and training parts, to evaluate and enhance the mathematical reasoning of LMMs. CMM-Math contains over 28,000 high-quality samples, featuring a variety of problem types (e.g., choice, fill-in-the-blank, analysis) with detailed solutions across 12 grade levels from elementary to high school in China. The problem may contain multiple images, and the visual context may be present in the questions or opinions, which makes this dataset more challenging. Our comprehensive analysis reveals that state-of-the-art LMMs on the CMM-Math face challenges, emphasizing the necessity for further improvements in LMM development. We also propose a Multimodal Mathematical LMM (Math-LMM) to handle the problems with mixed input of multiple images and text segments. The Math-LMM is trained using three stages: foundational pre-training, foundational fine-tuning, and mathematical fine-tuning. The extensive experiments indicate that our model effectively improves math reasoning performance by comparing it with the SOTA LMMs over three multimodal mathematical datasets. We release the datasets on GitHub (https://github.com/ECNU-ICALK/EduChat-Math) and Huggingface (https://huggingface.co/datasets/ecnu-icalk/cmm-math).

warm systems have attracted growing interest due to Internet of Things (IoT) advancements and their applications in multi-robot control, environmental mapping, and intelligent transportation. Average consensus protocols are crucial in these scenarios, but current implementations require frequent interagent data exchange, posing significant risks to network security. warm systems have attracted growing interest due to Internet of Things (IoT) advancements and their applications in multi-robot control, environmental mapping, and intelligent transportation. Average consensus protocols are crucial in these scenarios, but current implementations require frequent interagent data exchange, posing significant risks to network security. In this paper, we present a privacy-preserving consensus algorithm designed to ensure the privacy of the initial state while achieving consensus on the precise average of the initial values, even in the presence of denial-of-service (DoS) attacks. First, to protect agent state privacy, we introduce perturbation to the transmitted information among the agents using independently generated random noises drawn from Laplace distributions, in conjunction with the utilization of specifically designed pseudo-random values. Then, by synthesizing the perturbed average consensus algorithm with an event-triggered control strategy, a distributed privacy-preserving consensus protocol is established for the swarm systems under DoS attacks. The presented results indicate that under the given network communication conditions, the event-triggered mechanism can effectively withstand the impact of DoS attacks on the network and ensure the achievement of accurate average consensus. Finally, experimental results on a UAV-swarm board based Wi-Fi network are provided to validate the effectiveness of obtained theoretical results.

With the introduction of large language models (LLMs), automatic math reasoning has seen tremendous success. However, current methods primarily focus on providing solutions or using techniques like Chain-of-Thought to enhance problem-solving accuracy. In this paper, we focus on improving the capability of mathematics teaching via a Socratic teaching-based LLM (SocraticLLM), which guides learners toward profound thinking with clarity and self-discovery via conversation. We collect and release a high-quality mathematical teaching dataset, named SocraticMATH, which provides Socratic-style conversations of problems with extra knowledge. Also, we propose a knowledge-enhanced LLM as a strong baseline to generate reliable responses with review, guidance/heuristic, rectification, and summarization. Experimental results show the great advantages of SocraticLLM by comparing it with several strong generative models. The codes and datasets are available on https://github.com/ECNU-ICALK/SocraticMath.

This PDF file contains the front matter associated with ICCE Proceedings including the Title Page, Copyright information, Table of Contents, Introduction, and Conference Committee listing.

Knowledge tracing (KT) plays a crucial role in computer-aided education and intelligent tutoring systems, aiming to assess students' knowledge proficiency by predicting their future performance on new questions based on their past response records. While existing deep learning knowledge tracing (DLKT) methods have significantly improved prediction accuracy and achieved state-of-the-art results, they often suffer from a lack of interpretability. To address this limitation, current approaches have explored incorporating psychological influences to achieve more explainable predictions, but they tend to overlook the potential influences of historical responses. In fact, understanding how models make predictions based on response influences can enhance the transparency and trustworthiness of the knowledge tracing process, presenting an opportunity for a new paradigm of interpretable KT. However, measuring unobservable response influences is challenging. In this paper, we resort to counterfactual reasoning that intervenes in each response to answer what if a student had answered a question incorrectly that he/she actually answered correctly, and vice versa. Based on this, we propose RCKT, a novel response influence-based counterfactual knowledge tracing framework. RCKT generates response influences by comparing prediction outcomes from factual sequences and constructed counterfactual sequences after interventions. Additionally, we introduce maximization and inference techniques to leverage accumulated influences from different past responses, further improving the model's performance and credibility. Extensive experimental results demonstrate that our RCKT method outperforms state-of-the-art knowledge tracing methods on four datasets against six baselines, and provides credible interpretations of response influences. The source code is available at https://github.com/JJCui96IRCKT.

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.

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.

In some real-world applications, multiple measuring methods are often employed to extract multiple feature groups of data, yielding multi-view data. The main challenge of multiview clustering is to find a suitable way of simultaneously exploiting the complementary information of all views, considering the view conflicts arose by different measures. For perspective of optimization, previous multi-view clustering studies applied weighted sum method to represent degree of conflict and treated it as a weighted sum single-objective optimization problem. In this work, we formatted multi-view clustering as a multi-objective optimization problem, in which each view is regarded as a totally independent feature subset. The clustering objective function in each view is one of the multiple objectives. Five popular multi-objective evolutionary algorithms (MOEAs), i.e., NSGA-II, SPEA2, MOEA/D, SMS-EMOA and NSGA-III, were used to solve the induced multi-objective problem. Six real-world multi-view datasets were used to evaluate the proposed method and the experimental results showed that SPEA2 significantly outperformed the other MOEAs according to three performance evaluation indices.

Many problems in educational data mining involve datasets that come from multiple different views or sources, which make the data mining task more challenging. However, most existing methods rely equally on every view, something lead to performance degradation in the case of incompatible views. In this work, we focus on a typical multi-view problem, the handwritten numerals clustering. In the proposed algorithm, each view is assigned a weight to express its importance and a simple yet efficient dynamical weight updating strategy is given.

Many-objective optimization is a difficulty in the present evolutionary multi-objective optimization community. Integrating decision makers' preferences into multi-objective evolutionary algorithm is considered to be an effective approach. This paper presents a new scheme named bipolar preferences dominance for many-objective optimization problems. In the proposed scheme, the solutions are first sorted by the g-dominance to enhance the efficiency of Pareto sorting, and the non-dominated ones are sorted again based on their similarities to increase the proportion of solutions' comparability in high-dimension space. With bipolar preferences dominance, the race is led to the Pareto optimal area which is close to the positive preference and far away from the negative preference. After combining the proposed scheme with NSGA-II methodology, the effectiveness of 2p-NSGA-II was validated on two to fifteen-objective test problems. Moreover, 2p-NSGA-II provides better result when compared with g-dominance based algorithm g-NSGA-II.