Effects of High Impact Educational Practices on Engineering and Computer Science Student Participation, Persistence, and Success at Land Grant Universities: Award# RIEF-1927218 – Year 2 Abstract Funded by the National Science Foundation (NSF), this project aims to investigate and identify associations (if any) that exist between student participation in High Impact Educational Practices (HIP) and their educational outcomes in undergraduate engineering and computer science (E/CS) programs. To understand the effects of HIP participation among E/CS students from groups historically underrepresented and underserved in E/CS, this study takes place within the rural, public university context at two western land grant institutions (one of which is an Hispanic-serving institution). Conceptualizing diversity broadly, this study considers gender, race and ethnicity, and first-generation, transfer, and nontraditional student status to be facets of identity that contribute to the diversity of academic programs and the technical workforce. This sequential, explanatory, mixed-methods study is guided by the following research questions: 1. To what extent do E/CS students participate in HIP? 2. What relationships (if any) exist between E/CS student participation in HIP and their educational outcomes (i.e., persistence in major, academic performance, and graduation)? 3. How do contextual factors (e.g., institutional, programmatic, personal, social, financial, etc.) affect E/CS student awareness of, interest in, and participation in HIP? During Project Year 1, a survey driven quantitative study was conducted. A survey informed by results of the National Survey of Student Engagement (NSSE) from each institution was developed and deployed. Survey respondents (N = 531) were students enrolled in undergraduate E/CS programs at either institution. Frequency distribution analyses were conducted to assess the respondents’ level of participation in extracurricular HIPs (i.e., global learning and study aboard, internships, learning communities, service and community-based learning, and undergraduate research) that have been shown in the literature to positively impact undergraduate student success. Further statistical analysis was conducted to understand the effects of HIP participation, coursework enjoyability, and confidence at completing a degree on the academic success of underrepresented and nontraditional E/CS students. Exploratory factor analysis was used to derive an "academic success" variable from five items that sought to measure how students persevere to attain academic goals. Results showed that a linear relationship in the target population exists and that the resultant multiple regression model is a good fit for the data. During the Project Year 2, survey results were used to develop focus group interview protocols and guide the purposive selection of focus group participants. Focus group interviews were conducted with a total of 27 undergraduates (12 males, 15 females, 16 engineering students, 11 computer science students) across both institutions via video conferencing (i.e., ZOOM) during the spring and fall 2021 semesters. Currently, verified focus group transcripts are being systematically analyzed and coded by a team of four trained coders to identify themes and answer the research questions. This paper will provide an overview of the preliminary themes so far identified. Future project activities during Project Year 3 will focus on refining themes identified during the focus group transcript analysis. Survey and focus group data will then be combined to develop deeper understandings of why and how E/CS students participate in the HIP at their university, taking into account the institutional and programmatic contexts at each institution. Ultimately, the project will develop and disseminate recommendations for improving diverse E/CS student awareness of, interest in, and participation in HIP, at similar land grant institutions nationally.
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The Maximum Separation Subspace in Sufficient Dimension Reduction with Categorical Response
Sufficient dimension reduction (SDR) is a very useful concept for exploratory analysis and data visualization in regression, especially when the number of covariates is large. Many SDR methods have been proposed for regression with a continuous response, where the central subspace (CS) is the target of estimation. Various conditions, such as the linearity condition and the constant covariance condition, are imposed so that these methods can estimate at least a portion of the CS. In this paper we study SDR for regression and discriminant analysis with categorical response. Motivated by the exploratory analysis and data visualization aspects of SDR, we propose a new geometric framework to reformulate the SDR problem in terms of manifold optimization and introduce a new concept called Maximum Separation Subspace (MASES). The MASES naturally preserves the “sufficiency” in SDR without imposing additional conditions on the predictor distribution, and directly inspires a semi-parametric estimator. Numerical studies show MASES exhibits superior performance as compared with competing SDR methods in specific settings.
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- NSF-PAR ID:
- 10192086
- Date Published:
- Journal Name:
- Journal of machine learning research
- Volume:
- 21
- Issue:
- 29
- ISSN:
- 1533-7928
- Page Range / eLocation ID:
- 1-36
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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