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A Characterization of Engineering and Computer Science Undergraduate Participation in High-impact Educational Practices at Two Western Land-grant InstitutionsCurrently, substantial efforts are underway to improve the engagement and retention of engineering and computer science (E/CS) students in their academic programs. Student participation in specific activities known as High Impact Educational Practices (HIP) has been shown to improve student outcomes across a variety of degree fields. Thus, we suggest that understanding how and why E/CS students, especially those from historically underrepresented groups, participate in HIP is vital for supporting efforts aimed at improving E/CS student engagement and retention. The aim of the current study is to examine the participation of E/CS undergraduates enrolled at two western land-grant institutions (both institutions are predominantly white; one is an emerging Hispanic-serving institution) across five HIEP (i.e., global learning and study aboard internships, learning communities, service and community-based learning, and undergraduate research) that are offered outside of required E/CS curricula and are widely documented in the research literature. As part of a larger study, researchers developed an online questionnaire to explore student HIP participation and then surveyed E/CS students (n = 576) across both land-grant institutions. Subsequently, researchers will use survey results to inform the development of focus groups interview protocols. Focus group interviews will be conducted with purposefully selected E/CS students whomore »
Effects of High Impact Educational Practices on Engineering and Computer Science Student Participation, Persistence, and Success at Land Grant UniversitiesDespite efforts to attract and retain more students in engineering and computer science — particularly women and students from underrepresented groups — diversity within these educational programs and the technical workforce remains stubbornly low. Research shows that undergraduate retention, persistence, and success in college is affected by several factors, including sense of belonging, task value, positive student-faculty interactions, school connectedness, and student engagement , . Kuh  found that improvement in persistence, performance, and graduation for students in college were correlated to students’ level of participation in particular activities known as high impact educational practices (HIEP). HIEP include, among others, culminating experiences, learning communities, service learning, study abroad, and undergraduate research; Kuh  concluded that these activities may be effective at promoting overall student success. Kuh  and others  further hypothesized that participation in HIEP may especially benefit students from non-majority groups. Whether and how engineering and computer science students benefit from participating in HIEP and whether students from non-majority groups have access to HIEP activities, however, remain as questions to investigate. In this project, we examine engineering and computer science student participation in HIEP at two public land grant institutions. In this study, we seek to understand howmore »
A new calibration method for charm jet identification validated with proton-proton collision events at √s = 13 TeVAbstract Many measurements at the LHC require efficient identification of heavy-flavour jets, i.e. jets originating from bottom (b) or charm (c) quarks. An overview of the algorithms used to identify c jets is described and a novel method to calibrate them is presented. This new method adjusts the entire distributions of the outputs obtained when the algorithms are applied to jets of different flavours. It is based on an iterative approach exploiting three distinct control regions that are enriched with either b jets, c jets, or light-flavour and gluon jets. Results are presented in the form of correction factors evaluated using proton-proton collision data with an integrated luminosity of 41.5 fb -1 at √s = 13 TeV, collected by the CMS experiment in 2017. The closure of the method is tested by applying the measured correction factors on simulated data sets and checking the agreement between the adjusted simulation and collision data. Furthermore, a validation is performed by testing the method on pseudodata, which emulate various mismodelling conditions. The calibrated results enable the use of the full distributions of heavy-flavour identification algorithm outputs, e.g. as inputs to machine-learning models. Thus, they are expected to increase the sensitivity of future physicsmore »