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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

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


  • 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


  • 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

Supplemental instruction
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

Faculty mentoring
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

Conceptual framework
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


  • 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


  • 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)


  • 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)


  • 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)


  • 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


  • Limitations

    • Secondary data analysis

    • Propensity score matching only as good as the variables available

    • Two outcomes measure intentions rather than actual behavior

Findings predicting supplemental instruction
Findings – Predicting Supplemental Instruction

  • HS GPA (+)

  • STEM identity as a freshman (+)

  • HPW talking with teachers outside of class in HS (+)

Findings predicting above average mentorship
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 (-)


  • 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


  • 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

Future research
Future Research

  • Examine effects of mentorship, supplemental instruction, and other interventions on longer-term outcomes

    • STEM graduate program enrollment

    • Entry into STEM workforce

Contact information
Contact Information

Administrative Staff:

Dominique Harrison


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:

Project e-mail: [email protected]

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.