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The Impact of Undergraduate Interventions on STEM Student Outcomes

Kevin Eagan, Sylvia Hurtado , Bryce Hughes, & Tanya Figueroa, UCLA Association for Institutional Research Annual Forum Orlando, FL May 28, 2014. The Impact of Undergraduate Interventions on STEM Student Outcomes. Introduction.

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The Impact of Undergraduate Interventions on STEM Student Outcomes

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  1. Kevin Eagan, Sylvia Hurtado, Bryce Hughes, & Tanya Figueroa, UCLA Association for Institutional Research Annual Forum Orlando, FL May 28, 2014 The Impact of Undergraduate Interventions on STEM Student Outcomes

  2. Introduction • Colleges and universities have been challenged to produce an additional one million STEM degrees over the next decade • The NSF, NIH, and institutions have invested heavily in interventions which have been shown to improve academic performance and retention in STEM • Supplemental instruction and faculty mentoring are two cost-effective types of interventions often already provided at institutions

  3. Purpose • To examine the effect of supplemental instruction and faculty mentoring on STEM identity, intentions to enroll in a STEM graduate program, and commitment to a STEM career • To utilize a quasi-experimental statistical modeling technique to better isolate the effects of these interventions on the three outcomes of interest

  4. Supplemental Instruction • Developed by Deanna Martin at the University of Missouri-Kansas City • Targets “at-risk courses” as opposed to “at-risk students” • Peer-facilitated sessions focused on problem solving and enhancing course material • Voluntary; not remedial • Supplemental instruction has been shown to improve academic performance and term-to-term retention rates in single-institution studies

  5. Faculty Mentoring • Intentional support, as opposed to happenstance faculty-student interactions • Consists of professional and personal support activities • Faculty mentoring also improves academic performance and retention • However, students who typically seek faculty mentoring are students already positioned to succeed

  6. Conceptual Framework • Situated Learning Theory (Lave and Wenger, 1991) • STEM as a community of practice • Learning as legitimate peripheral participation • Through the process of learning new members become more central to the community through identifying with the community • Social Learning Theory (Bandura, 1971) • Learning is a cognitive and behavioral process that occurs through both observation and modeling • Learning results from a dynamic interaction between cognition, environment, and behavior • Theory of Planned Behavior (Ajzen, 1991) • Intentions are a crucial precursor to behavior

  7. Methods • Data Source and Sample • Longitudinal Dataset • 2004 CIRP Freshman Survey • 2008 CIRP College Senior Survey • NIH and NSF funding augmented participation of MSI’s and STEM-producing institutions • 4,166 longitudinal student cases who intended to major in STEM across 237 institutions

  8. Methods • Dependent Variables • STEM identity – Four-item factor • Becoming an authority in my field • Making a theoretical contribution to science • Receiving recognition from others for contributions to my field • Finding a cure for a health problem • Commitment to a STEM career (dichotomous) • Intentions to pursue STEM graduate study (dichotomous)

  9. Methods • Independent Variables • Participation in Supplemental Instruction • Receipt of Faculty Mentoring – 9-item factor • Each is dichotomized for the propensity score matching analysis • Supplemental instruction: Students who participated (frequently or occasionally) versus non-participants • Faculty mentorship: Above average (>50) mentorship versus average or below average (<50)

  10. Methods • Control variables • Background characteristics • Pre-college academic preparation • Pre-college aspirations and expectations • Initial measures of STEM identity, plans to pursue a STEM career, and expectations of pursuing graduate study in STEM (i.e. Pretest)

  11. Methods • Analytic Strategy • Missing data addressed through EM algorithm • Propensity score matching • Probit regression • Precollege characteristics and experiences predicting mentorship and supplemental instruction participation • Nearest neighbor matching • T-tests conducted with matched sample for each intervention for each of the three outcomes

  12. Methods • Limitations • Secondary data analysis • Propensity score matching only as good as the variables available • Two outcomes measure intentions rather than actual behavior

  13. Findings – Predicting Supplemental Instruction • HS GPA (+) • STEM identity as a freshman (+) • HPW talking with teachers outside of class in HS (+)

  14. Findings – Predicting Above Average Mentorship • Race: Latino vs. White (-) • Race: Asian American vs. White (-) • HS GPA (+) • Mother’s education (+) • STEM identity as an incoming freshman (+) • Reason for attending college: Prepare for graduate school (+) • HPW: Talking with teachers outside of class in HS (+) • Major: Engineering or computer science (-) • Concerns about ability to pay for college (-)

  15. Effects of SI and Mentorship on Outcomes

  16. Effects of SI and Mentorship on Outcomes for URM Students

  17. Discussion • Supplemental instruction as a way to establish a community of practice • Strengthens students’ STEM identity • Increases likelihood to plan to enroll in STEM graduate programs • Particularly beneficial for URM STEM identity development • Faculty Mentorship • Benefits of mentorship extend even after accounting for the types of students likely to receive or seek out mentorship • Mentorship even more impactful for URM students’ STEM identity development

  18. Implications • Undergraduate research can be a resource-intensive intervention • Supplemental instruction and faculty mentoring are additional important STEM persistence tools • Structure or web of opportunity in STEM • Lends support for further expansion of supplemental instruction offerings and for broader access to intentional faculty mentoring • Propensity score matching provides a method for reducing bias due to participant self-selection when assessing STEM interventions

  19. Future Research • Examine effects of mentorship, supplemental instruction, and other interventions on longer-term outcomes • STEM graduate program enrollment • Entry into STEM workforce

  20. Contact Information Administrative Staff: Dominique Harrison Faculty/Co-PIs: Sylvia Hurtado Kevin Eagan Graduate Research Assistants: Tanya Figueroa Bryce Hughes Undergraduate Research Assistants: Paloma Martinez Robert Paul Papers and reports are available for download from project website: http://heri.ucla.edu/nih Project e-mail: herinih@ucla.edu This study was made possible by the support of the National Institute of General Medical Sciences, NIH Grant Numbers 1 R01 GMO71968-01 and R01 GMO71968-05, the National Science Foundation, NSF Grant Number 0757076, and the American Recovery and Reinvestment Act of 2009 through the National Institute of General Medical Sciences, NIH Grant 1RC1GM090776-01. This independent research and the views expressed here do not indicate endorsement by the sponsors.

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