The Impact of Socioeconomic Status on Access and Persistence in Engineering

1. The Impact of Socioeconomic Status on Access and Persistence in Engineering Matthew OhlandXingyu Chen Noah SalzmanNichole Ramirez Russell Long Purdue University School of Engineering Education Valerie Lundy-Wagner Teacher’s College – Columbia University Community College Research Center Marisa Orr Louisiana Tech University Mechanical Engineering

2. Outline of this talk • Why we’re considering social class in our research on engineering student pathways • How social class is usually measured and how we are measuring it from the data we have • How socioeconomic status (SES) affects enrollment, grades, persistence, and graduation in college engineering programs • How social class effects are understood by university employees working with engineering students • Special topics related to merit-based scholarships and their effect on serving less privileged students.

3. Introduction • Why explore social class? • Expands traditional diversity efforts • Move beyond binary understandings of class • Need to understand how to remove barriers to success • Especially important in engineering due to its role promoting social mobility among disadvantaged groups

4. The Theory Behind This Work • Traditional Operationalization of Socioeconomic Status (SES) • Family Income • Parents’ education • Parents’ occupation • SES is strongly associated with Cultural Capital - Bourdieu’s Theory of Stratification • Knowledge, cultural awareness, credentials, preferences, skills, abilities and mannerisms • Cultural capital is used as social currency that can be used to an individual’s advantage • SES is one measure of social class

5. Other work shows that low SES students are LESS likely to: • Complete a rigorous high school curriculum, thus may be unprepared for STEM majors • Attend a 4-year college/university after high school • Attend selective institutions • At the 146 most selective institutions • 74% students from top socioeconomic quarter • 3% from the lowest • Choose selective and often lucrative majors, like engineering

6. Free lunch enrollment is a proxy of school SES • Common SES indicators • Family income • Parental education • Occupational prestige • Poverty status (free/reduced lunch enrollment) • Peer poverty status slightly less influential on individual achievement than individual poverty status – but peer poverty status is less invasive (and what we have access to) • SES outweighs race/ethnicity in predicting engineering access, persistence, completion • In our dataset, engineering students come from similar socioeconomic backgrounds to other students.

7. A new variable: Peer socioeconomic status (pSES) High school data • National Center for Education Statistics (NCES) • 29,171 public schools • Free lunch and enrollment data (1987-2004) Institutional data • The Multi-Institution Database for Investigating Engineering Longitudinal Development (MIDFIELD) • 11 public research universities • 226,221 engineering students Analysis (SAS) • N=55,132 (engr. students w/ pSES) • Average pSES = 0.87 • Logistic (enrollment, persistence) and linear (GPA) regressions

8. Our results are based mostly on students from public high schools

9. Gender gap in engineering enrollment increases with pSES * Bands indicate 95% confidence limit Male Female

10. First year GPA increases with pSES * Band indicates 95% confidence limit Peer SES is not a predictor of persistence in engineering to the 3rd semester

11. Probability of 6-year graduation in engineering increases with pSES * Band indicates 95% confidence limit

12. The pSES of a high school is: • Positively related to enrollment in engineering • The relationship of pSES is stronger for men than women • Positively related to 3rd semester GPA • Not related to persistence in engineering to the 3rd semester • Positively related to graduation in engineering within 6 years Implication: Students from low SES schools persist to their sophomore year, but at a disadvantage and less likely to continue beyond that point.

13. More Research Questions • Does using a time-variant measure of economic status better predict engineering persistence than a time-averaged measure? • How do school and district-level measures of economic status compare in predicting engineering persistence? • How does the effect of SES vary among universities?

14. Ways we considered measuring SES

15. Predicted probability of six-year graduation in engineering at different institutions

16. Key Findings and Conclusions • Peer Economic Status is most predictive when averaged over a long time period • The economic and cultural resources that affect future success in engineering accumulate over time • District-level variables were significant, but could not explain as much variance as the school-level variables. • Many more variables are available at the district level: expenditures per student and census data such as household income, education, and employment levels • The effects of SES vary by institution • While some institutions favor students from high-SES backgrounds, others have fostered a climate in which all students have a fair chance at success

17. This leads to more research questions… • If the effects of SES are vary by institution, then we need to talk to people at the institutions to know how they identify and address SES. • We talked to the people in a university who seemed most likely to identify and manage SES issues—the academic advisors

18. Studying Advisors’ Perspectives on SES • Participants and Data Collection • 18 staff academic advisors at 8 of the 11 public institutions in the Multiple-Institution Database for Investigating Engineering Longitudinal Development (MIDFIELD) • Semi-structured interviews • Audio recorded and transcribed verbatim • Data Analysis • Analyzed using a priori codes: • Definition of SES • Differences in SES • Cultural Capital • Habitus

19. Assertion 1: Academic advisors were largely unable to articulate a coherent definition of SES • A tendency to confound terms: • Single parent household … always. Living with someone who’s not a parent…always. Um… letting me know they’re first generation … pretty much always. • Problems with assumptions: • …they were indignant, you know. They were all over the place. I mean some of them came in to me and looked like… and they were first generation students… they were very well off, you know... And they’d be like ‘No. I can afford my own tutoring. I drive a Lexus.’ • Students avoid self-identification: an invisible minority

20. Assertion 2: Advisors associated inadequate college preparation with socioeconomically disadvantaged students • Acknowledged significant variation in preparation: • Sometimes they are sort of under-prepared students and sometimes they’re just top of the heap I mean as far as the preparation and kind of credits they have coming in …but I would say more of them are probably not as prepared. • Harder time talking to faculty: • I have the impression that they’re intimidated by faculty and one of the things I ask my students to do is to make sure that they talk to the faculty, each of their professors three times in the semester…at least once in their office hours and two more times either with an email question or after class or something so that they stand out in a crowd. Because one of their success strategies is to have the connection with the professor, letting the professor know that they’re serious students.

21. Harder time navigating the university system • There are questions which show that they are completely unused to dealing with a large campus. Like this is a bureaucracy and if you’re not used to dealing with a bureaucracy … what’s a withdrawal date, what’s a drop date, what’s a syllabus…all that stuff. • We have students, you know, they’ll come in and I’ll sometimes mention that ‘I see that you did really poorly that semester. Did you try to get a withdrawal?’ ‘Oh, I didn’t know I could get a withdrawal.’…some students just never assume that there’s any sort of exceptions to anything…I would say that less privileged students probably don’t ask for exceptions or don’t know that they can ask

22. Assertion 3: Families play different roles for low-SES students. • Limited ‘college knowledge’ • “Well, I’ll go and talk to my dad but he doesn’t understand. He’s never been to college so he doesn’t realize what that means when I say ‘Oh, this is so hard’ or you know ‘I’m struggling with my mid-terms.’ … My dad doesn’t really know what type of advice to give me.” • Desire to succeed to help family • It seems like their motivation is very high to pursue engineering because many of them know that it will make a big difference in their family’s lives • Many of my students come in and say they are the first person in their whole family …to go to college and they are the role model and they want to do well and they want to help their family and their… one of their goals… every single one of my students’ top goal is to graduate college…and many of them say ‘to take care of my family, to help my brothers and sisters, to buy my mom a house.’

23. Family problems can be an additional burden • There are some students with really difficult stories as far as… they’re the sole support for their family…pretty horrific situations that I just couldn’t have imagined going through when I was their age. • Her family was very poor and she has several brothers and sisters and her one brother’s in jail for stealing and her family has had to move from place to place because the parents couldn’t hold a job and they had to keep moving to find jobs and they had basic level jobs and when she was a senior in high school she decided that she was not going to live this way and she took it upon herself that she needs to help her family.

24. Conclusions and Implications • Undergraduate engineering advisors in this study recognized a lack of cultural capital in students they perceived as being low-SES • Students from different social classes acknowledge and understand information about navigating college differently • Expectations and assumptions were not always accurate • Highlight the need for institutions to reconsider their definitions of diversity • Reconsider how institutions can support low-SES students • Even though we talk about binary distinctions between low and high-SES, in reality it is more complicated

25. Special topic: socioeconomic issues related to merit-based scholarships

26. Take Away Messages • Don’t confuse SES with race/ethnicity • SES issues are not solely economic • Public institutions need to serve low-SES students, but should account for how that affects cohort data • If you only pay attention to diversity you can see, you aren’t serving all of Indiana’s students

27. Acknowledgements This material is based upon work supported by the National Science Foundation under Grant No. 0935058. Our coauthors contributed significantly to our understand.

28. In-state fraction of first-time students increased in MBS states, but decreased in non-MBS states. Percent change of the fraction of first-time students classified as in-state

29. MBS states were more successful at attracting or maintaining the fraction of first-time students enrolled in engineering. Percent change of the fraction of first-time students enrolled in engineering

30. Enlarging gap between grant aid and tuition fees, especially in states where MBS is not available. a, averaging across five MIDFIELD institutions with merit-based scholarships b, averaging across six MIDFIELD institutions without merit-based scholarships Out-of-state tuition and fees, in-state tuition and fees, and grant aid per student

31. Percentage of students with high relative peer SES increased after the adoption of MBS.The shift was slightly more prominent in engineering. a, MIDFIELD institutions in MBS-state 1first-year in-state engineering b, MIDFIELD institutions in MBS-state 1 first-year in-state total c, MIDFIELD institutions in non-MBS states first-year in-stateengineering d, MIDFIELD institutions in non-MBS states first-year in-state total Percentage distribution of relative peer SES for first-year in-state students

32. First-year credit hours and full load attempted • TFS: direction and degree of course load adjustment were closely related to credit hour requirements in scholarship retention rules • Engineering:moresensitiveto scholarship credit-hour requirements, moreprone to reduce first-year credit hours as compared to total residents Interpretations • Tying scholarship rewards to course load may effectively reduce time-to-graduation • Engineering students chose a lighter load to stay eligible for merit-based scholarships in response to the perception of higher per-course effort found by Mobley et al.

33. Methods, implications, limitations • Methods • Descriptive statistics: Correlation of SAT and Relative Peer SES, pre/Post scholarship comparison of SAT, Relative Peer SES • Difference-in-difference regression: relationships of merit-based scholarships to credit hours, GPA, full credit load, withdrawal, and summer course-taking • Implications • Legislators and state governors: MBS can have positive and negative effects and interact with other policies • Engineering educators: University policies can also interact with legislative policies, Administrators and advisors can anticipate effects • Only explores institutional differences within one state • Unit-record identification of scholarship recipients unavailable • Effects beyond the first year are not yet studied

34. First-year course withdrawal and summer course enrollment • Course withdrawal • TFS: more likely to withdraw at least one course in the first academic year • Engineering: unaffected • Summer course enrollment • TFS including engineering residents: more likely to take at least one summer course Interpretations • Students became more likely to withdraw from courses because they perceived higher grades would be easier to earn during summer

35. First-year GPA • TFS including engineering residents: Merit-based scholarships were negatively correlated with first-year GPA Interpretations • Increase in SAT should result in a greater increase in first-year GPA • Scholarship programs did not help improve student academic performance