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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Developing undergraduate teaching materials in collaboration with pre-university students
ABSTRACT In this project we have involved four high-achieving pre-university summer placement students in the development of undergraduate teaching materials, namely tutorial videos for first year undergraduate Electrical and Electronic Engineering lab, and computer simulations of didactic semiconductor structures for an Electrical Science first year compulsory taught module. Here we describe our approach and preliminary results.
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- Award ID(s):
- 1663296
- NSF-PAR ID:
- 10028121
- Date Published:
- Journal Name:
- MRS Advances
- Volume:
- 2
- Issue:
- 31-32
- ISSN:
- 2059-8521
- Page Range / eLocation ID:
- 1713 to 1719
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1557/adv.2017.333
