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This paper describes an approach that can be used by faculty and administrators to efficiently develop program-level student support plans to increase retention and completion in STEM disciplines. These recommendations were developed as part of a National Science Foundation-sponsored workshop intended to help two-year college faculty and administrators prepare proposals for the National Science Foundation Scholarships in Science, Technology, Engineering, and Mathematics (S-STEM) Program. S-STEM scholarship proposals are expected to be built on a foundation of deep needs analyses specific to the targeted population of students in STEM disciplines. Based on needs assessment, programs can then focus on implementing appropriate interventions that will be most effective in improving the retention and completion of their students. Guidelines for streamlining the acquisition and organization of critical elements of student needs analyses can be useful for two-year college faculty and administrators to develop NSF S-STEM proposals and any other initiatives they may pursue to improve student success at their institutions. Our approach recognizes that needs analysis benefits from three levels of data: institutional data, program-level data, and student-level data. Institutional-level data includes retention and completion data as well as results of institutional-level surveys of current students or alumni and the National Survey of Student Engagement (NSSE). Program-level data includes retention and completion data at the program level that may show significant differences from institutional results. In addition, program data should include course-level grades and failure rates, student GPA correlated with student program year, and student demographic data if available. The program data can help identify attrition points at the program level. Student data forms a third level that can clarify and focus student needs analyses. One aspect of student-level data is personal attributes associated with academic and career success in STEM fields. Examples include a growth mindset, stem identity, a sense of belonging, and academic self-efficacy. The validated surveys that exist to characterize these attributes are outlined in the paper. These surveys can be used at the program level to identify both baseline data and critical needs. In parallel with surveys, the creation of a student need archetype using techniques from the NSF I-Corps for Learning (I-Corps L) model can be used to elicit another dimension of challenges faced by students. The I-Corps for Learning model emphasizes the benefit of unstructured one-on-one informal interviews to elicit unscripted data from students to test assumptions and uncover opportunities for impact. The paper provides step-by-step guidelines for efficient implementation of I-Corps for Learning student needs discovery methods. In summary, even with external grant funding such as NSF S-STEM funds, student support initiatives must allocate available funds strategically to obtain the most impact. Collection of data at institutional, program, and student levels can facilitate the synthesis of a student-need archetype that supports faculty and administrative decision-makers. This paper aims to provide practical guidelines to two-year college faculty and administrators for creating a compelling student needs assessment and characterization of institutional context. 

The National Science Foundation (NSF) Advanced Technological Education (ATE) program is effective in assisting two-year college (2YC) institutions of higher education to improve the education of technicians in science and engineering, yet grant proposals from 2YCs to ATE (and NSF as a whole) have declined in number over the past decade. The problem of NSF proposals declining in numbers is multifaceted, though data demonstrates that both 2YCs and NSF can reverse or mitigate the decline in ATE proposals through identified measures; 2YCs can change their grants culture through specific institutional changes, and NSF can aid 2YCs to build their capacity to develop competitive proposals through mentoring and professional development sustainably. This article discusses data, insights, and solutions through the lens of two NSF ATE projects: Project Vision (a mentoring project) and Grant Insights (an applied research project). 

The National Science Foundation (NSF) Advanced Technological Education (ATE) program is effective in assisting two-year college (2YC) institutions of higher education to improve the education of technicians in science and engineering, yet grant proposals from 2YCs to ATE (and NSF as a whole) have declined in number over the past decade. The problem of NSF proposals declining in numbers is multifaceted, though data demonstrates that both 2YCs and NSF can reverse or mitigate the decline in ATE proposals through identified measures; 2YCs can change their grants culture through specific institutional changes, and NSF can aid 2YCs to build their capacity to develop competitive proposals through mentoring and professional development sustainably. This article discusses data, insights, and solutions through the lens of two NSF ATE projects: Project Vision (a mentoring project) and Grant Insights (an applied research project). 

Parallel and Distributed Computing (PDC) has become pervasive and is now exercised on a variety of platforms. Therefore, understanding how parallelism and distributed computing affect problem solving is important for every computing and engineering professional. However, most students in computer science (CS) and computer engineering (CE) programs are still introduced to computational problem solving using an old model, in which all processing is serial and synchronous, with input and output via text using a terminal interface or a local file system. Teaching a range of PDC knowledge and skills at multiple levels in Computer Science (CS) and related Computing and Engineering curricula is essential. The challenges are significant and numerous. Although some progress has been made in terms of curriculum recommendations and educational resources in computer science, trained faculty, motivation, and inertia are still some of the major impediments to introducing PDC early in computing curricula. The authors of this paper conducted a series of week-long faculty training workshops on the integration of PDC topics in CS1 and CS2 classes, and this paper provides an experience report on the impact and effectiveness of these workshops. Our survey results indicate such faculty development workshops can be effective in gradual inclusion of PDC in early computing curricula. 

This study examined potential bias with respect to perceived gender and race in pre-service teachers’ professional noticing of children’s mathematical thinking. The goal of the study was to explore emerging connections between professional noticing and equity concerns in mathematics education and discover the extent to which such noticing may be influenced by a student’s race and gender. A sample of 151 preservice teachers participated, and our findings suggest that bias tends to emerge in the interpreting phase of professional noticing; however, such emergence was not statistically significant when compared across the perceived race and gender of the students. 

In this paper, we report the description and evaluation of an annual workshop titled “Capacity Building Workshops for Competitive S-STEM Proposals from Two-Year Colleges in the Western U.S.” which was offered in June of 2019, 2020, and 2021 with the goal of facilitating submissions to the NSF S-STEM program from 2-year colleges (2YCs). The two-day workshop was composed of separate sessions during which participants discussed several aspects of proposal preparation. Participants also received pre- and post-workshop support through webinars and office hours. To evaluate the program, post-workshop surveys were administered through Qualtrics™. The workshop and related activities received overall positive feedback with specific suggestions on how to better support participants. The paper discusses specific challenges faced by 2YC teams in preparing their proposals. Over three offerings, the program welcomed 103 participants on 51 teams from 2YCs. As of 2021, 11 teams total (from the 2019 cohort) submitted proposals. Among them, four were funded, which is approximately double the typical success rate. Six of the declined teams resubmitted and one of them is currently in negotiations. 

Abstract Using the formalism of Newton hyperplane arrangements, we resolve the open questions regarding angle rank left over from work of the first two authors with Roe and Vincent. As a consequence we end up generalizing theorems of Lenstra–Zarhin and Tankeev proving several new cases of the Tate conjecture for abelian varieties over finite fields. We also obtain an effective version of a recent theorem of Zarhin bounding the heights of coefficients in multiplicative relations among Frobenius eigenvalues. 

The purpose of this study was to evaluate the reliability and validity of the raw accelerometry output from research-grade and consumer wearable devices compared to accelerations produced by a mechanical shaker table. Raw accelerometry data from a total of 40 devices (i.e., n = 10 ActiGraph wGT3X-BT, n = 10 Apple Watch Series 7, n = 10 Garmin Vivoactive 4S, and n = 10 Fitbit Sense) were compared to reference accelerations produced by an orbital shaker table at speeds ranging from 0.6 Hz (4.4 milligravity-mg) to 3.2 Hz (124.7mg). Two-way random effects absolute intraclass correlation coefficients (ICC) tested inter-device reliability. Pearson product moment, Lin’s concordance correlation coefficient (CCC), absolute error, mean bias, and equivalence testing were calculated to assess the validity between the raw estimates from the devices and the reference metric. Estimates from Apple, ActiGraph, Garmin, and Fitbit were reliable, with ICCs = 0.99, 0.97, 0.88, and 0.88, respectively. Estimates from ActiGraph, Apple, and Fitbit devices exhibited excellent concordance with the reference CCCs = 0.88, 0.83, and 0.85, respectively, while estimates from Garmin exhibited moderate concordance CCC = 0.59 based on the mean aggregation method. ActiGraph, Apple, and Fitbit produced similar absolute errors = 16.9mg, 21.6mg, and 22.0mg, respectively, while Garmin produced higher absolute error = 32.5mg compared to the reference. ActiGraph produced the lowest mean bias 0.0mg (95%CI = -40.0, 41.0). Equivalence testing revealed raw accelerometry data from all devices were not statistically significantly within the equivalence bounds of the shaker speed. Findings from this study provide evidence that raw accelerometry data from Apple, Garmin, and Fitbit devices can be used to reliably estimate movement; however, no estimates were statistically significantly equivalent to the reference. Future studies could explore device-agnostic and harmonization methods for estimating physical activity using the raw accelerometry signals from the consumer wearables studied herein. 

Abstract We define a notion of height for rational points with respect to a vector bundle on a proper algebraic stack with finite diagonal over a global field, which generalizes the usual notion for rational points on projective varieties. We explain how to compute this height for various stacks of interest (for instance: classifying stacks of finite groups, symmetric products of varieties, moduli stacks of abelian varieties, weighted projective spaces). In many cases, our uniform definition reproduces ways already in use for measuring the complexity of rational points, while in others it is something new. Finally, we formulate a conjecture about the number of rational points of bounded height (in our sense) on a stack $$\mathcal {X}$$ , which specializes to the Batyrev–Manin conjecture when $$\mathcal {X}$$ is a scheme and to Malle’s conjecture when $$\mathcal {X}$$ is the classifying stack of a finite group.