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Caring assessments is an assessment design framework that considers the learner as a whole and can be used to design assessment opportunities that learners find engaging and appropriate for demonstrating what they know and can do. This framework considers learners’ cognitive, meta-cognitive, intra-and inter-personal skills, aspects of the learning context, and cultural and linguistic backgrounds as ways to adapt assessments. Extending previous work on intelligent tutoring systems that “care” from the field of artificial intelligence in education (AIEd), this framework can inform research and development of personalized and socioculturally responsive assessments that support students’ needs. In this article, we (a) describe the caring assessment framework and its unique contributions to the field, (b) summarize current and emerging research on caring assessments related to students’ emotions, individual differences, and cultural contexts, and (c) discuss challenges and opportunities for future research on caring assessments in the service of developing and implementing personalized and socioculturally responsive interactive digital assessments. 

In recent years, large language models (LLMs) have seen rapid advancement and adoption, and are increasingly being used in educational contexts. In this perspective article, we explore the open challenge of leveraging LLMs to create personalized learning environments that support the “whole learner” by modeling and adapting to both cognitive and non-cognitive characteristics. We identify three key challenges toward this vision: (1) improving the interpretability of LLMs' representations of whole learners, (2) implementing adaptive technologies that can leverage such representations to provide tailored pedagogical support, and (3) authoring and evaluating LLM-based educational agents. For interpretability, we discuss approaches for explaining LLM behaviors in terms of their internal representations of learners; for adaptation, we examine how LLMs can be used to provide context-aware feedback and scaffold non-cognitive skills through natural language interactions; and for authoring, we highlight the opportunities and challenges involved in using natural language instructions to specify behaviors of educational agents. Addressing these challenges will enable personalized AI tutors that can enhance learning by accounting for each student's unique background, abilities, motivations, and socioemotional needs. 

Learner models are used to support the implementation of personalization features in Adaptive Instructional Systems (AISs; e.g., adaptive sequencing of activities, adaptive feedback), which are important aspects of Intelligent Adaptive Systems. With the increased computational power, more advanced methodologies, and more available data, learner models include a variety of Artificial Intelligence techniques. These techniques have different levels of complexity, which influence interpretability and explainability of learner models. Interpretable and explainable learner models can facilitate appropriate use of the learner modeling information in AISs, their adoption, and scalability. This chapter elaborates on the definitions of interpretability and explainability, describes interpretability and explainability levels of different models, elaborates on the levels of explainability to produce needed information for teachers and learners, and discusses implications and future work in this area. 

This paper explores the differences between two types of natural language conversations between a student and pedagogical agent(s). Both types of conversations were created for formative assessment purposes. The first type is conversation-based assessment created via knowledge engineering which requires a large amount of human effort. The second type, which is less costly to produce, uses prompt engineering for LLMs based on Evidence-Centered design to create these conversations and glean evidence about students¿½f knowledge, skills and abilities. The current work compares linguistic features of the artificial agent(s) discourse moves in natural language conversations created by the two methodologies. Results indicate that more complex conversations are created by the prompt engineering method which may be more adaptive than the knowledge engineering approach. However, the affordances of prompt engi-neered, LLM generated conversation-based assessment may create more challenges for scoring than the original knowledge engineered conversations. Limitations and implications are dis-cussed. 

Abstract In the third APOKASC catalog, we present data for the complete sample of 15,808 evolved stars with APOGEE spectroscopic parameters and Kepler asteroseismology. We used 10 independent asteroseismic analysis techniques and anchor our system on fundamental radii derived from GaiaLand spectroscopicT_eff. We provide evolutionary state, asteroseismic surface gravity, mass, radius, age, and the data used to derive them for 12,418 stars. This includes 10,036 exceptionally precise measurements, with median fractional uncertainties in ${\nu }_{\max }$, Δν, mass, radius, and age of 0.6%, 0.6%, 3.8%, 1.8%, and 11.1%, respectively. We provide more limited data for 1624 additional stars that either have lower-quality data or are outside of our primary calibration domain. Using lower red giant branch (RGB) stars, we find a median age for the chemical thick disk of 9.14 ± 0.05(ran) ± 0.9(sys) Gyr with an age dispersion of 1.1 Gyr, consistent with our error model. We calibrate our red clump (RC) mass loss to derive an age consistent with the lower RGB and provide asymptotic GB and RGB ages for luminous stars. We also find a sharp upper-age boundary in the chemical thin disk. We find that scaling relations are precise and accurate on the lower RGB and RC, but they become more model dependent for more luminous giants and break down at the tip of the RGB. We recommend the use of multiple methods, calibration to a fundamental scale, and the use of stellar models to interpret frequency spacings. 

Abstract Objective.In vivoimaging assessments of skeletal muscle structure and function allow for longitudinal quantification of tissue health. Magnetic resonance elastography (MRE) non-invasively quantifies tissue mechanical properties, allowing for evaluation of skeletal muscle biomechanics in response to loading, creating a better understanding of muscle functional health.Approach. In this study, we analyze the anisotropic mechanical response of calf muscles using MRE with a transversely isotropic, nonlinear inversion algorithm (TI-NLI) to investigate the role of muscle fiber stiffening under load. We estimate anisotropic material parameters including fiber shear stiffness ( ${\mu }_1$), substrate shear stiffness ( ${\mu }_2$), shear anisotropy ( $\varphi$), and tensile anisotropy ( $\zeta$) of the gastrocnemius muscle in response to both passive and active tension.Main results. In passive tension, we found a significant increase in ${\mu }_1,$ $\varphi ,$and $\zeta$with increasing muscle length. While in active tension, we observed increasing ${\mu }_2$and decreasing $\varphi$and $\zeta$during active dorsiflexion and plantarflexion—indicating less anisotropy—with greater effects when the muscles act as agonist.Significance. The study demonstrates the ability of this anisotropic MRE method to capture the multifaceted mechanical response of skeletal muscle to tissue loading from muscle lengthening and contraction. 

Abstract We present a search for close, unresolved companions in a subset of spatially resolved Gaia wide binaries containing main-sequence stars within 200 pc of the Sun, utilizing the APOGEE–Gaia Wide Binary Catalog. A catalog of 37 wide binaries was created by selecting pairs of stars with nearly identical Gaia positions, parallaxes, and proper motions, and then confirming candidates to be gravitationally-bound pairs using APOGEE radial velocities. We identify close, unresolved stellar and substellar candidate companions in these multiple systems using (1) the Gaia binary main-sequence and (2) observed periodic radial velocity variations in APOGEE measurements due to the influence of a close substellar-mass companion. The studied wide binary pairs reveal a total of four stellar-mass close companions in four different wide binaries, and four substellar-mass close companion candidates in two wide binaries. The latter are therefore quadruple systems, with one substellar mass companion orbiting each wide binary component in an S-type orbit. Taken at face value, these candidate systems represent an enhancement of an order of magnitude over the expected occurrence rate of ∼2 per cent of stars having substellar companions >2 MJup within ∼100 day orbits; we discuss implications and possible explanations for this result. Finally, we compare chemical differences between the components of the wide binaries and the components of the candidate higher-order systems and find that any chemical influence or correlation due to the presence of close companions to wide binary stars is not discernible. 

Abstract The bright starλSer hosts a hot Neptune with a minimum mass of 13.6M_⊕and a 15.5 day orbit. It also appears to be a solar analog, with a mean rotation period of 25.8 days and surface differential rotation very similar to the Sun. We aim to characterize the fundamental properties of this system and constrain the evolutionary pathway that led to its present configuration. We detect solar-like oscillations in time series photometry from the Transiting Exoplanet Survey Satellite, and we derive precise asteroseismic properties from detailed modeling. We obtain new spectropolarimetric data, and we use them to reconstruct the large-scale magnetic field morphology. We reanalyze the complete time series of chromospheric activity measurements from the Mount Wilson Observatory, and we present new X-ray and ultraviolet observations from the Chandra and Hubble space telescopes. Finally, we use the updated observational constraints to assess the rotational history of the star and estimate the wind braking torque. We conclude that the remaining uncertainty on the stellar age currently prevents an unambiguous interpretation of the properties ofλSer, and that the rate of angular momentum loss appears to be higher than for other stars with a similar Rossby number. Future asteroseismic observations may help to improve the precision of the stellar age. 

ABSTRACT We measure rotational broadening in spectra taken by the Apache Point Observatory Galactic Evolution Experiment (APOGEE) survey to characterize the relationship between stellar multiplicity and rotation. We create a sample of 2786 giants and 24 496 dwarfs with stellar parameters and multiple radial velocities from the APOGEE pipeline, projected rotation speeds vsin i determined from our own pipeline, and distances, masses, and ages measured by Sanders & Das. We use the statistical distribution of the maximum shift in the radial velocities, ΔRVmax, as a proxy for the close binary fraction to explore the interplay between stellar evolution, rotation, and multiplicity. Assuming that the minimum orbital period allowed is the critical period for Roche Lobe overflow and rotational synchronization, we calculate theoretical upper limits on expected vsin i and ΔRVmax values. These expectations agree with the positive correlation between the maximum ΔRVmax and vsin i values observed in our sample as a function of log(g). We find that the fast rotators in our sample have a high occurrence of short-period [log(P/d) ≲ 4] companions. We also find that old, rapidly rotating main-sequence stars have larger completeness-corrected close binary fractions than their younger peers. Furthermore, rapidly rotating stars with large ΔRVmax consistently show differences of 1–10 Gyr between the predicted gyrochronological and measured isochronal ages. These results point towards a link between rapid rotation and close binarity through tidal interactions. We conclude that stellar rotation is strongly correlated with stellar multiplicity in the field, and caution should be taken in the application of gyrochronology relations to cool stars.