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Abstract Spatial voting models are widely used in political science to analyze legislators’ preferences and voting behavior. Traditional models assume that legislators’ ideal points are static across different types of votes. This article extends the Bayesian spatial voting model to incorporate hierarchical Bayesian methods, allowing for the identification of covariates that explain differences in legislators’ ideal points across voting domains. We apply this model to procedural and final passage votes in the U.S. House of Representatives from the 93rd through 113th Congresses. Our findings indicate that legislators in the minority party and those representing moderate constituencies are more likely to exhibit different ideal points between procedural and final passage votes. This research advances the methodology of ideal point estimation by simultaneously scaling ideal points and explaining variation in these points, providing a more nuanced understanding of legislative voting behavior. 

Financial exploitation of older adults is rising significantly, necessitating effective interventions. This qualitative study examined perceptions and experiences of technology use among 40 adults over age 60 through rural and urban focus groups. Three primary themes emerged: technology knowledge gaps, trust and privacy concerns dependent on source credibility, and reactive rather than proactive approaches to combating financial exploitation. Urban participants demonstrated greater technology comfort and more sophisticated protective strategies than their rural counterparts. Findings suggest that effective interventions should provide in-person, step-by-step guidance from trusted institutions, simplify technical terminology, and promote proactive security measures. 

A goal of the AIED community is to create equitable systems; yet, we lack a cohesive viewpoint on how to do so. In the present work, we propose power as this organizing principle. We utilize the data feminism framework to showcase how we might balance power, focusing on learner engagement. We utilize multimodal data from ten middle school girls in a virtual computer science camp to discuss how the AIED community might create systems of equity that support all learners. 

While existing student modeling methods focus on predicting students’ knowledge states, they often overlook the underlying cognitive processes contributing to learning. In this work, we integrate cognitive processes, specifically phases of rule learning, into student modeling, drawing inspiration from cognitive science. Rule learning involves rule search, discovery, and following, providing a systematic framework for understanding how individuals acquire and apply knowledge. We conduct two studies to explore rule learning phases in a real-world learning context. Moreover, we present a two-step approach to first predict the phases of rule learning students experience during problem solving with an intelligent tutoring system and then estimate the time spent on each predicted phase. Furthermore, we identify the relationships between the time spent on specific phases of rule learning and student performance. Our findings underscore the importance of integrating cognitive processes into student modeling for more targeted interventions and personalized support. 

Abstract Cas9 is a metal-dependent nuclease that has revolutionized gene editing across diverse cells and organisms exhibiting varying ion uptake, metabolism, and concentrations. However, how divalent metals impact its catalytic function, and consequently its editing efficiency in different cells, remains unclear. Here, extensive molecular simulations, Markov State Models, biochemical and NMR experiments, demonstrate that divalent metals – Mg²⁺, Ca²⁺, and Co²⁺– promote activation of the catalytic HNH domain by binding within a dynamically forming divalent metal binding pocket (DBP) at the HNH-RuvC interface. Mutations in DBP residues disrupt HNH activation and impair the coupled catalytic activity of both nucleases, identifying this cryptic DBP as a key regulator of Cas9’s metal-dependent activity. The ionic strength thereby promotes Cas9’s conformational activation, while its catalytic activity is metal-specific. These findings are critical to improving the metal-dependent function of Cas9 and its use for genome editing in different cells and organisms.