Search for: All records

While peer-to-peer conversations can be beneficial for children’s linguistic and mathematical development, the specific conditions needed to support optimal conversations remain elusive. As part of a larger project to infuse peer-to-peer interactions into mathematics instruction for multilingual students, 8- to 11-year-old children in the U.S. were videotaped by their teachers interviewing one another about their solution strategies to equal sharing problems. Partner Interviews were analyzed to determine the quality of the interactions between pairs using Barwell’s (2023) definition of negotiating meaning. This study examines the relationships among the quality of student negotiations, accuracy of strategies, similarities between strategies, and grade levels. Findings indicate that the degree to which students negotiated each other’s ideas varied, and the accuracy of students’ solutions was related to the quality of their negotiations. We provide a framework for assessing the quality of the peer-to-peer negotiations as well as concrete examples of the structures and scaffolds used to elicit these conversations. 

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). 

Characterizing low-energy, keV-range nuclear recoils near the detector threshold is one of the major challenges for large direct dark matter detectors. To that end, we have successfully used an Yttrium-Beryllium photoneutron source that emits 152 keV neutrons for the calibration of the light and charge yields of the XENONnT experiment for the first time. After data selection, we accumulated 474 events from 183 hours of exposure with this source. The expected background was $55\pm 12$ accidental coincidence events, estimated using a dedicated 152 hour background calibration run with a Yttrium-PVC gamma-only source and data-driven modeling. From these calibrations, we extracted the light (charge) yield for liquid xenon at our field strength of $23\mathrm{V}/\mathrm{cm}$ between 0.3 (0.7) ${\mathrm{keV}}_{\mathrm{NR}}$ and $5.0{\mathrm{keV}}_{\mathrm{NR}}$ . This calibration is crucial for accurately measuring the solar ${}^8\mathrm{B}$ neutrino coherent elastic neutrino-nucleus scattering and searching for light dark matter particles with masses below $12\mathrm{GeV}/{\mathrm{c}}^2$ . 

We report on the search for x-ray radiation as predicted from dynamical quantum collapse with low-energy electronic recoil data in the energy range of 1–140 keV from the first science run of the XENONnT dark matter detector. Spontaneous radiation is an unavoidable effect of dynamical collapse models, which were introduced as a possible solution to the long-standing measurement problem in quantum mechanics. The analysis utilizes a model that for the first time accounts for cancellation effects in the emitted spectrum, which arise in the x-ray range due to the opposing electron-proton charges in xenon atoms. New world-leading limits on the free parameters of the Markovian continuous spontaneous localization and Diósi-Penrose models are set, improving previous best constraints by two orders of magnitude and a factor of five, respectively. For the strength and correlation length of the continuous spontaneous localization model, values in the originally proposed parameter ranges are experimentally excluded for the first time. 

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.