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This research study was conducted to pilot an out-of-school family science program for fifth- and sixth-grade Latina girls and their parents. Program goals included encouraging parents in supporting their Latina daughters in science, increasing the girls’ interest in science and increasing the families’ participation in science experiences together. The 41 families participated in a 7-week Saturday program on either rocketry or gardening. Each week, the parent–daughter dyads engaged in hands-on Family Problem-Based Learning activities together and then the parents and daughters met separately in Conversation Groups. To measure the impact of the program, surveys were administered to the parents and daughters separately at four points: pre-, mid-, post- and delayed-post (three months after the program). Parents reported increases over time for several aspects of their support for their daughters in science and also increases in frequency of science experiences with their daughters. The daughters reported increases over time in their science identity and their discussions with their parents about jobs in science. In addition, the examination of video-recordings of a subset of the parent–daughter interactions during the activities revealed that parental and daughter behaviors evolved over the course of the program. Implications for engaging parents in science education are discussed. 

This study examines the impact of a culturally responsive, garden-based STEM program designed for Latina girls (grades 5–6) and their parents. The “Our Plot of Sunshine” project integrates Family Project-Based Learning with garden education to create meaningful STEM engagement opportunities. Drawing on the science capital, science identity, and community cultural wealth frameworks, the program leverages families’ cultural and linguistic resources while developing science knowledge and identity. Nineteen families from low socioeconomic schools participated in three pilot implementations across two Western U.S. cities. Using a mixed-methods approach with repeated measures over 19 weeks, the study tracked changes in participants’ science identity, interest, and career aspirations. Results showed significant increases in science identity and career aspirations, with effects maintained at three-month follow-up. While interest/enjoyment showed positive trends, changes were not statistically significant. Parent ratings of program elements were consistently higher than daughter ratings, though both groups reported strong engagement. The successful integration of bilingual instruction emerged as a particularly valued program component. These findings suggest that family-centered, culturally responsive garden education can effectively support Latina girls’ STEM identity development and future orientation, while highlighting the potential of leveraging family and cultural resources in STEM education. 

Collaborative learning can improve student learning, student persistence, and the classroom climate. While work has documented the tradeoffs of face-to-face collaboration and asynchronous, online learning, the trade-offs between asynchronous (student-scheduled) and synchronous (instructor-scheduled) collaborative and online learning have not been explored. Structured roles can maximize the effectiveness of collaborative learning by helping all students participate, but structured roles have not been studied in online settings. We performed a quasi-experimental study in two courses—Computer Architecture and Numerical Methods—to compare the effects of asynchronous collaborative learning without structured roles to synchronous collaborative learning with structured roles. We use a data-analytics approach to examine how these approaches affected the student learning experience during formative collaborative learning assessments. Teams in the synchronous offering made higher scoring submissions (5-10% points better on average), finished assessments more efficiently (11-16 minutes faster on average), and had greater equality in the total number of submissions each student made (for example, significant increase of 13% in the mean equality score among all groups). 

Abstract IceCube is a Cherenkov detector instrumenting over a cubic kilometer of glacial ice deep under the surface of the South Pole. The DeepCore sub-detector lowers the detection energy threshold to a few GeV, enabling the precise measurements of neutrino oscillation parameters with atmospheric neutrinos. The reconstruction of neutrino interactions inside the detector is essential in studying neutrino oscillations. It is particularly challenging to reconstruct sub-100 GeV events with the IceCube detectors due to the relatively sparse detection units and detection medium. Convolutional neural networks (CNNs) are broadly used in physics experiments for both classification and regression purposes. This paper discusses the CNNs developed and employed for the latest IceCube-DeepCore oscillation measurements [1]. These CNNs estimate various properties of the detected neutrinos, such as their energy, direction of arrival, interaction vertex position, flavor-related signature, and are also used for background classification. 

We report on a search for weakly interacting massive particle (WIMP) dark matter (DM) via elastic DM-xenon-nucleus interactions in the XENONnT experiment. We combine datasets from the first and second science campaigns resulting in a total exposure of 3.1 tonne-years. In a blind analysis of nuclear recoil events with energies above 3.8  keVNR, we find no significant excess above background. We set new upper limits on the spin-independent WIMP-nucleon scattering cross section for WIMP masses above 10  GeV/𝑐2 with a minimum of 1.7×10−47  cm2 at 90% confidence level for a WIMP mass of 30  GeV/𝑐2. We achieve a best median sensitivity of 1.4×10−47  cm2 for a 41  GeV/𝑐2 WIMP. Compared to the result from the first XENONnT science dataset, we improve our sensitivity by a factor of up to 1.8. 

Abstract In the IceCube Neutrino Observatory, a signal of astrophysical neutrinos is obscured by backgrounds from atmospheric neutrinos and muons produced in cosmic-ray interactions. IceCube event selections used to isolate the astrophysical neutrino signal often focus on the morphology of the light patterns recorded by the detector. The analyses presented here use the new IceCube Enhanced Starting Track Event Selection (ESTES), which identifies events likely generated by muon–neutrino interactions within the detector geometry, focusing on neutrino energies of 1–500 TeV with a median angular resolution of 1.4 $\stackrel{\text{°}}{}$. Selecting for starting-track events filters out not only the atmospheric-muon background but also the atmospheric-neutrino background in the southern sky. This improves IceCube’s muon–neutrino sensitivity to southern-sky neutrino sources, especially for Galactic sources that are not expected to produce a substantial flux of neutrinos above 100 TeV. In this work, the ESTES sample was applied for the first time to search for astrophysical sources of neutrinos, including a search for diffuse neutrino emission from the Galactic plane. No significant excesses were identified from any of the analyses; however, constraining limits are set on the hadronic emission from TeV gamma-ray Galactic plane objects and models of the diffuse Galactic plane neutrino flux.