Bryce E. Hughes, Juan C. Garibay, Sylvia Hurtado, & Kevin Eagan UCLA American Educational Research Association San Francisco, CA May 1, 2013 1

 National Academy of Engineering (2011) report

Lifelong Learning Imperative in Engineering

  Wave of retirements “U.S. has one of the lowest rates of graduation of bachelor level engineers in the world: only 4.5% of our university graduates are engineers” (p. ix).

 Tremendous infrastructural and environmental challenges  Despite its national import, much is still unknown about the factors that influence engineering completion 2

 Student characteristics and precollege experiences  Self-efficacy    Academic preparation Knowledge of and exposure to engineering (from parents and others) Aspirations and commitment to an engineering career  Classroom experiences   Teacher-centered practices:  Lectures, grading on a curve, individual-based work Student-centered pedagogy:  Active learning strategies, collaborative work, design- and problem-based learning 3

 Practices and Programs in Engineering (ASEE, 2012) :  Internships and cooperative experiences    Research opportunities Retention programs for URMs Financial assistance  Institutional Contexts (For STEM students)   Size, selectivity, private, and Minority-Serving Institutions Peer normative context  Previous research models on engineering student success have yet to account for these contexts 4

 To identify institutional contexts that contribute to engineering degree completion within five years of college entry.

 Identify contexts that “derail” engineering aspirants from the engineering track and improve the use of “evidence-based” approaches 5

• • • • Longitudinal Data on Engineering Aspirants Data Sources: • • • 2004 Freshman Survey Completion data from National Student Clearinghouse 2007 & 2010 HERI Faculty Survey • • STEM Best Practices Survey – administered to STEM deans and department chairs at our participating campuses IPEDS Sample: 15,913 first-time, full-time engineering aspirants across 270 institutions Analysis: Multinomial HGLM (HLM software) 6

 Dependent Variable (measured five years after college entry):  Engineering completion compared to:   Bachelor’s completion in non-engineering field No bachelor’s degree completion-includes students still enrolled (major not known) 7

 Independent variables  Background characteristics        Pre-college preparation and experiences Aspirations and expectations Intended major Aggregate peer effects Institutional characteristics Faculty contextual measures Best practices in STEM 8

Dependent Variable

Completed engineering degree in five years Completed degree in other field Did not complete

Demographics

Sex: Female American Indian Asian/Pacific Islander Black Latino/a Other race White 35.2% 24.6% 40.1% 16.88% 1.63% 13.93% 8.91% 7.13% 1.52% 66.88% 9

Institutional context

Control: Private Undergraduate FTE % STEM faculty engaging undergrads in research Avg. STEM faculty score on student-centered pedagogy

Student-level variables

Parent employed as engineer Sex: Female Aeronautical/astronautical engineering (ref: mechanical) Chemical engineering Computer engineering Electrical/electronics engineering Industrial engineering Other engineering

+ + + +

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Institutional Context

Control: Private Undergraduate FTE % STEM faculty engaging undergrads in research Selectivity Avg. STEM faculty score on student-centered pedagogy

Student-level variables

Sex: Female Parent employed as engineer American Indian, Latino/a (ref: White) Doctoral degree aspiration Civil engineering Aeronautical/astronautical engineering (ref: mechanical) Computer engineering + + + + + + + 11

Hispanic-Serving Institution Black students (ref: White) Black students at HBCUs Civil engineering (ref: mechanical) Other engineering

Non-engineering

+

No completion

+ + n.s.

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 Institutional context matters  Driven by mission, affects college’s level of resources  Minority-serving institutions continue to meet a crucial need  Faculty efforts can aggregate into cultural influences on student outcomes  Disaggregating by engineering field informs how differences in culture and coursework affect student outcomes  Students may also “go pro” early in some fields 13

 Individual colleges are uniquely positioned to graduate engineers   Understanding this position better informs practice and policy Future research should address the influence of context for community college and transfer students  Parsing out micro-, meso-, and macrolevel institutional influences provides a more complete picture of an institution’s degree productivity  Degree completion is influenced by institutional mission as well as department-level differences 14

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Faculty/Co-PIs: Sylvia Hurtado Mitchell Chang Kevin Eagan Postdoctoral Scholars: Josephine Gasiewski Graduate Research Assistants: Tanya Figueroa Gina Garcia Juan Garibay Bryce Hughes Administrative Staff: Dominique Harrison Papers and reports are available for download from project website: http://heri.ucla.edu/nih 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. 16