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This study examines the relationship between epistemic curiosity (EC) and perceived uncertainty, which is posited to form an inverted U-shape, in the context of uncertainty-driven science learning (UDSL). Using path analysis, survey data from 181 middle school students collected at multiple timepoints across a three-week photosynthesis unit designed to facilitate UDSL were analyzed. Results show that the inverted U-shaped relationship between EC and uncertainty does not consistently hold over time. Instead, prior uncertainty negatively affects this relationship unless mediated by EC or sustained uncertainty in subsequent activities. At the trait level, the study highlights the mediating role of epistemic orientation toward uncertainty in fostering trait EC in UDSL. The findings underscore the importance of supporting students' productive uncertainty navigation and signify the positive impacts of UDSL in fostering EC. 

Students’ Dispositions toward Scientific Uncertainty Navigation (DSUN) can play a pivotal role in impacting student learning achievement. However, understanding of the underlying mechanism of DSUN influencing learning achievement is limited. Drawing from related research, this study investigates the roles of epistemic curiosity and learning engagement in mediating the relationship between DSUN and learning achievement. A survey study design was employed, involving 1,137 middle school students who participated in an uncertainty-driven learning environment when learning solar energy. A sequential mediation model was tested using data collected through validated measures assessing DSUN, epistemic curiosity, learning engagement, and learning achievement. Results revealed that while DSUN positively predicts learning achievement, this relationship is sequentially mediated by epistemic curiosity and learning engagement, with epistemic curiosity not serving as a significant mediator on its own. These findings underscore the importance of fostering both epistemic curiosity and engagement in science classroom activities when students encounter scientific uncertainty. 

Abstract Laser ablation is a process that bears both fundamental physics interest and has wide industrial applications. For decades, the lack of probes on the relevant time and length scales has prevented access to the highly nonequilibrium phase decomposition processes triggered by laser excitation. In this study, a close integration of time-resolved probing by intense femtosecond X-ray pulses with large-scale atomistic modeling has yielded unique insights into the ablation dynamics of thin gold films irradiated by femtosecond laser pulses. The emergence and growth of nanoscale density heterogeneities in the expanding ablation plume, predicted in the simulations, are mapped to the rapid evolution of distinct small angle diffraction features. This mapping enables identification of the characteristic signatures of different phase decomposition processes occurring simultaneously in the plume, which are driven by photomechanical and thermodynamic driving forces. Beyond the specific insights into the ablation phenomenon, this study demonstrates the power of joint X-ray probing and atomistic modeling of material dynamics under extreme conditions of thermal and mechanical nonequilibrium. 

In a traditional lecturing environment, students possess limited agency in accepting or rejecting information provided by teachers. A higher level of student agency involves opportunities and actively identifying uncertainties and collaborating with peers to deepen understanding within the classroom community. Teachers play a crucial role in guiding students through sensemaking by addressing uncertainties and assisting in solution development. Student uncertainty is recognized as a pedagogical resource, engaging them in sensemaking and enhancing agency levels. This study analyzed 28 whole-class discussions led by seven science teachers, identifying three phases: problematization, coherence negotiation, and new understanding enactment. The teachers employed eight strategies leveraging student scientific uncertainty as pedagogical resources within three sequential methods: eliciting awareness of uncertainty (e.g., creating hybrid, ambiguous, and problem-solving spaces), seeking a coherent understanding (e.g., connecting students’ lived experiences to empirical data for coherence, juxtaposing different interpretations to build consensus, and weaving together student ideas for coherence), and demonstrating new understanding (e.g., transference, translation). By embracing uncertainty, students become agents of sensemaking, contributing to a collaborative learning environment. 

This article presents an innovative instructional approach that assists teachers in designing and implementing their science unit: The SUPeR (Student Uncertainty as Pedagogical Resources) approach. The SUPeR approach suggests four phases of student learning in scientific practices and posits that student uncertainties drive the trajectory of learning. By applying the SUPeR approach, teachers can foster student curiosity and ensure a student-centered science learning environment. A sixth-grade solar energy unit is described to show how a science unit can be designed and implemented using the SUPeR approach. The article elaborates on teacher guidance for applying the SUPeR approach, how student uncertainty is used to foster student curiosity and drive the learning trajectory, how student learning can be assessed from the SUPeR perspective, and how the SUPeR science unit aligns with the Next Generation Science Standards. 

A large fraction of total healthcare expenditure occurs due to end-of-life (EOL) care, which means it is important to study the problem of more carefully incentivizing necessary versus unnecessary EOL care because this has the potential to reduce overall healthcare spending. This paper introduces a principal-agent model that integrates a mixed payment system of fee-for-service and pay-for-performance in order to analyze whether it is possible to better align healthcare provider incentives with patient outcomes and cost-efficiency in EOL care. The primary contributions are to derive optimal contracts for EOL care payments using a principal-agent framework under three separate models for the healthcare provider, where each model considers a different level of risk tolerance for the provider. We derive these optimal contracts by converting the underlying principal-agent models from a bilevel optimization problem into a single-level optimization problem that can be analytically solved. Our results are demonstrated using a simulation where an optimal contract is used to price intracranial pressure monitoring for traumatic brain injuries.