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  1. Currently, 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 »participated in the survey. Combined survey and focus group data will then be analyzed to more deeply understand why and how E/CS students participate in the HIP at their university. This research paper reports on the frequency distribution analysis of the survey data generated with E/CS undergraduates enrolled at one of the two land grant institutions. The combined sample included E/CS undergraduates from the following demographic groups: female (34 %), Asian (10 %), Black or African American (2%), Hispanic or Latinx (6%), Native American or Alaskan Native (1%), Native Hawaiian or Other Pacific Islander (1%), White (81 %), and multiracial (4 %). Results show that most (38%) E/CS students reported participating in internships, while study abroad programs garnered the smallest level of E/CS student participation (5%) across all five HIP. Internships were found most likely to engage diverse students: Female (42%), Hispanic or Latinx (24%), Multiracial (44%), Asian (31%), First-generation (29%), and nontraditional students—other than those categorized as highly nontraditional—all reported participating in internships more than any other HIP. Notable differences in participation across E/CS and demographic groups were found for other HIPs. Results further revealed that 43% of respondents did not participate in any extracurricular HIP and only 19% participated in two or more HIP. Insights derived from the survey and used to inform ongoing quantitative and qualitative analyses are discussed. Keywords: community-based learning, high impact educational practices, HIP, internships learning communities, service learning, study aboard, undergraduate research« less
  2. Despite 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 [1], [2]. Kuh [1] 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 [1] concluded that these activities may be effective at promoting overall student success. Kuh [1] and others [3] 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 »and why students participate in HIEP and how participation affects their persistence and success in engineering and computer science majors. Set within the rural, public land grant university context, this study conceptualizes diversity in a broad sense and includes women, members of underrepresented racial and ethnic groups, first generation college students, adult learners, and nontraditional student as groups contributing to the diversity of academic programs and the technical workforce.« less
  3. Abstract 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 »analyses.« less