Maximum deviation global discrimination index (MDGDI) is a new item selection method for cognitive diagnostic computerized adaptive testing that allows for attribute coverage balance. We developed the maximum limitation global discrimination index (MLGDI) from MDGDI, which allows for both attribute coverage balance and item exposure control. MLGDI can realize the attribute coverage balance and exposure control of the item. Our simulation study aimed to evaluate the performance of our new method against maximum global discrimination index (GDI), modified maximum GDI (MMGDI), standardized weighted deviation GDI (SWDGDI), and constraint progressive with SWDGDI (CP_SWDGDI). The results indicated that (1a) under the condition of realizing the attribute coverage balance, MDGDI had the highest attribute classification accuracy; (1b) when the selection strategy accommodated the practical constraints of the attribute coverage balance and item exposure control, MLGDI had the highest attribute classification accuracy; (2) adding the item exposure control mechanism to the item selection method reduces the classification accuracy of the attributes of the item selection method; and (3) compared with GDI, MMGDI, SWDGDI, CP_SWDGDI, and MDGDI, MLGDI can better achieve the attribute-coverage requirement, control item exposure rate, and attribute correct classification rate.

This paper explores Knowledge Tracing (KT) and its role in modeling students’ knowledge progression over time. While traditional approaches such as Bayesian Knowledge Tracing and Deep Knowledge Tracing have established a solid foundation in this field, previous research often overlooked the complex interactions between problem features and learner behaviors. To address this gap, this paper introduces the Knowledge Tracking Model for Integrating Question Information and Learning Behaviour (IQILB-KT), a novel framework that enhances the precision of knowledge tracing in online educational systems. By integrating the effects of question characteristics and student behaviour via a channel attention mechanism, the model captures deeper insights into both question information and the learning process. Furthermore, the IQILB-KT model employs a Bottleneck Attention Module (BAM) to calculate learning behaviour synergies, replacing the previous reliance on covariance statistics. This enhances the stability and accuracy of the model when dealing with learning behaviour synergy data. Finally, the efficacy of the IQILB-KT model was validated through rigorous testing on two real-world datasets from the ASSISTments educational platform. The results demonstrate that the model outperforms all existing methods in predicting students’ future performance and tracking their knowledge states in real time.

Given the continued relevance of perceived scarcity, understanding how a scarcity mindset influences human cooperation remains critical. However, previous research has yielded mixed results regarding this relationship. To clarify these inconsistencies, this study examined the impact of a scarcity mindset on cooperation within various social normative contexts. Participants were randomly assigned to either a scarcity or abundance mindset and engaged in a Public Goods Game under descriptive or injunctive normative conditions, each with high or low prosocial expectations. The results revealed that in both normative contexts, individuals with a scarcity mindset exhibited greater cooperation than those with an abundance mindset. Individuals also cooperated more under high prosocial norms compared to low ones. Importantly, the interaction effect revealed that while cooperation did not differ between the two mindsets under low prosocial norms, the scarcity mindset led to significantly greater cooperation under high prosocial norms. These findings provide new insights into the scarcity–cooperation dynamic and suggest that robust prosocial norms can amplify the cooperative tendencies associated with a scarcity mindset, highlighting the importance of leveraging social norms to enhance cooperation in resource-scarce situations.

This paper proposes a novel framework, HyperGAT-BERT-RAS, that integrates (1) a HyperGraph Attention Network (HyperGAT) with BERT for enhanced semantic representation; (2) a Reference Answer Set (RAS) constructed via clustering of full-score answers; and (3) Siamese Neural Networks (SNNs) for similarity-based scoring. Experiments on the Ohsumed and ASAP-5 datasets demonstrate that (i) HyperGAT-BERT achieves 72.95% accuracy on Ohsumed text classification, outperforming baseline HyperGAT by 3.28%, and (ii) the full HyperGAT-BERT-RAS achieves 78.66% accuracy and 0.7806 F1-score, with RAS contributing the most to performance gains. These improvements suggest the potential for more reliable scoring of diverse student answers, reduced teacher grading burden, and enhanced feasibility of AI-assisted formative assessment in real classrooms, although empirical validation with teachers and students is needed.

Suicidal ideation tends to be hidden, limiting the ability of current assessment tools to reliably identify at-risk individuals. Based on the spreading act