Abstract
This S_STEM project is designed to support the retention and graduation of high-achieving, low income students with demonstrated financial need at Baylor University. Over its five-year duration, this project will fund four-year scholarships to 22 students who are pursuing Bachelor degrees in Engineering, Electrical and Computer Engineering, Mechanical Engineering and Computer Science. Engineering and Computer Science (ECS) Scholars will participate in activities which include an orientation, a monthly seminar series and required faculty mentoring.
Support services and activities for ECS Scholars build upon existing activities at Baylor and feature peer mentoring, study abroad opportunities, alumni mentoring, support and training for undergraduate research, professional development workshops, and tutoring support from the ECS Learning Resource Center. A distinguishing feature of the project is the use of EAB’s Navigate, a web-based software platform for tracking student progress, coordinating student care and employing predictive analytics. The expertise generated using a student dashboard capable of predictive analytics will have the broad impact of informing the STEM community of best practices for timely interventions, and improving retention and graduation rates.
The Navigate platform is used for predictive analytics and to track and document ECS Scholar progress toward achieving benchmark goals in the areas of retention, graduation rates, internships, undergraduate research experiences, and job placement. The use of predictive analytics has significant potential for helping students arrive at successful outcomes. However, it is an assumption of this project that the successful use of predictive analytics should take into consideration not simply the accuracy in identifying students who are struggling but in the social attributions of success and perceptions of a “big data” tool that might be received alternatively with enthusiasm or suspicion.
The focus of this paper will be to give an overview of our first-year results from the project. We were successful in recruiting the full first cohort that began in the Fall of 2020. For the first year, many of the engagement sessions with the students pivoted to a virtual experience, however, we were able to manage several events that fostered a sense of community among the ECS scholars. Our research partners from the Baylor School of Education were successful in conducting baseline qualitative research using detailed interviews with an initial focus on community fit, academic fit and faculty relationships. The paper will also summarize our use of the Navigate platform and the lessons learned in the areas of data capture and interventions.
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Undergraduate Summer Research in High-performance Computing with Engineering Applications: An Experience Report
High Performance Computing (HPC) stands at the forefront of engineering innovation. With affordable and advanced HPC resources more readily accessible than ever before, computational simulation of complex physical phenomena becomes an increasingly attractive strategy to predict the physical behavior of diverse engineered systems. Furthermore, novel applications of HPC in engineering are highly interdisciplinary, requiring advanced skills in mathematical modeling, algorithm development as well as programming skills for parallel, distributed and concurrent architectures and environments. This and other possible reasons have created a shortage of qualified workforce to conduct the much-needed research and development in these areas. This paper describes our experience with mentoring a cohort of ten high achieving undergraduate students in Summer 2019 to conduct engineering HPC research for ten weeks in X University. Our mentoring activity was informed and motivated by an initial informal study with the goal to learn the roles and status of HPC in engineering research and what can be improved to make more effective use of it. Through a combination of email surveys, in-person interviews, and a manual analysis of faculty research profiles in X University, we learn several lessons. First, a large proportion of the engineering faculty conducts research that is highly mathematical and computational and driven by disciplinary sciences, where simulation and HPC are widely needed as solutions. Second, due to the lack of resources to provide the necessary training in software development to their students, the interviewed engineering groups are limited in their ability to fully leveraging HPC capability in their research. Therefore, novel pathways for training and educating engineering researchers in HPC software development must be explored in order to further advance the engineering research capability in HPC. With a multi-year support from NSF, our summer research mentoring activities were able to accommodate ten high-achieving undergraduate students recruited from across the USA and their faculty mentors on the theme of HPC applications in engineering research. We describe the processes of students recruitment and selection, training and engagement, research mentoring, and professional development for the students. Best practices and lessons learned are identified and summarized based on our own observations and the evaluation conducted by an independent evaluator. In particular, improvements are being planned so as to deliver a more wholistic and rigorous research experience for future cohorts.
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- Award ID(s):
- 1852102
- NSF-PAR ID:
- 10200372
- Date Published:
- Journal Name:
- 2020 American Society for Engineering Education (ASEE) Virtual Annual Conference
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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