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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:

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.