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A Mixed-method Approach to Characterizing the Experience of Transfer Students in Engineering

A Mixed-method Approach to Characterizing the Experience of Transfer Students in Engineering. Matthew W. Ohland Professor of Engineering Education, Purdue University Catherine E. Brawner President, Research Triangle Educational Consultants

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A Mixed-method Approach to Characterizing the Experience of Transfer Students in Engineering

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  1. A Mixed-method Approach to Characterizing the Experience of Transfer Students in Engineering Matthew W. Ohland Professor of Engineering Education, Purdue University Catherine E. BrawnerPresident, Research Triangle Educational Consultants National Institute for the Study of Transfer Students, Frisco, Texas, January 31, 2013

  2. Background Qualitative Study • Quantitative Study • Analyze extensive database • of student records • (e.g., compare transfer students • with FTIC students) • Review transfer policies • at MIDFIELD institutions • Conduct in-depth interviews • of engineering transfer students • at 6 or 7 MIDFIELD institutions

  3. Our research has multiple goals. • To describe how transfer students can increase the pool of STEM talent. • To estimate the potential for transfer students to improve the diversity of the pool of STEM talent • To learn more about who transfer students are, what they experience, and what happens to them • because institutions have made a commitment to serve them. • because of questions (1) and (2)

  4. The Quantitative Data Source The Multi-Institution Database for Investigating Engineering Longitudinal Development • 11 public universities, more than 1/10 of US engineering graduates • Predominantly southeastern, higher proportion (20%) of engineering students than other institutions offering engineering (10%) • Over 1 million unique students over a 20-year period including over 200,000 engineering students • Policy information available through catalogs, web archives, and consultation with partners

  5. Even after constraints are applied, the sample is large and high-quality. • Students at one time enrolled in engineering, and • U.S. citizens or permanent residents, and • for whom the dataset includes 6 years of complete data, and • who declared an engineering major by the fifth semester of enrollment. • 21,542 transfers remain • 73,190 non-transfers remain

  6. Quantitative Methods - descriptive • Descriptive statistics to compare the two student populations  • Standard t-tests and chi-square tests to study differences across populations • Cohen’s d and Cramer’s V to estimate effect sizes • Bonferroni adjustment to reduce the probability of false discovery  

  7. “Stickiness” • The likelihood of a student to “stick” with a major to six-year graduation once enrolled in that major • Assumptions • Selecting a major indicates intent to graduate in that major. • Admission to a degree program implies a commitment by the institution or program to facilitate the student’s success in that major. • Credits earned elsewhere by students contribute toward six-year window.

  8. What influences Stickiness? • Program action and inaction. • Interest of students choosing major • Departments can control these factors • Unaffected by fraction of students choosing major • Should be biased positively where students enter a major later (FYE, transfer, switchers) because those students have already persisted to that point (and attrition is front-loaded)

  9. Qualitative Approach • Interviews with Campus Personnel at all 11 Campuses • Review of Published Articulation Policies at all 11 Campuses • Interviews with Recent Transfers at 4 Institutions • Engineering predominates at 2 schools and 2 schools have well-regarded engineering colleges in an Arts and Sciences institution. All are the flagship engineering schools in their state • Findings based on preliminary analysis of interview transcripts

  10. Student Interview Details • Interviews conducted in the Fall of 2011 and Spring and Fall of 2012. Two more scheduled for Spring 2013 • University personnel sent email invitation to recent transfers majoring in chemical, civil, computer, electrical, industrial, mechanical, and freshman engineering • Interviewed 67 students • Interviewees were diverse with respect to race, gender, and major

  11. Interview Topics • Why did the student choose engineering as a field of study? • Reasons for selecting sending institution • Reasons for selecting receiving (MIDFIELD) institution • Experiences with the transfer process • Experiences with social and academic transitions • Suggestions for improving the transfer and transition processes

  12. Interview Recruitment Questionnaire • Volunteers answered a qualification survey (N=126 valid responses) • Questions included: • Prior Institutions Attended; Degrees Received • Age • Major • GPA at Last and MIDFIELD Institutions • Parental Education (N=66) as a Proxy for First Generation Status • Full-time/Part-time status (N=66)

  13. Findings • MIDFIELD demographics • Interview volunteer demographics • Interview volunteer sending institution characteristics • Interview results

  14. MIDFIELD Demographic Characteristics • Age: Older than non-transfers (on average) • 21.8 years old (almost 4 years older than non-transfers who typically enter college straight out of high school) • Gender: Somewhat more likely to be male • 80.7% male (2.2% higher than non-transfers) • Ethnicity: More likely to be URMs, particularly Black • 14.5% Black (v. 9.2% among non-transfers) • 19.4% URM (v. 12.5% among non-transfers) • Credit load: part-time status is four times as prevalent in the transfer population (30.7% versus 7.7%).

  15. Volunteer demographics generally similar to quantitative data source • Age: Average age of 22.3; 19% were 25 or older • Gender: Overwhelmingly male (77%) • Ethnicity: Overwhelmingly white (73%) • Credit load: Only 4/66 were part-time students

  16. Additional demographics available for survey participants • 34% had attended more than one prior institution • 64% (of N=66) had 1+ parent with bachelor’s degree or higher • 15% (of N=66) appear to be first generation college students • Male students more likely to come from 2-year schools (56%) than female students (48%) • Hispanic students are far more likely to come from 2-year schools than students in other ethnic groups • Hispanic – 71% • White – 55% • Black – 33% • Asian – 18%

  17. Characteristics of prior institution

  18. Observations about Prior Institution • Nearly half of students come from other 4-year institutions, many the result of formal transfer arrangements (e.g., 3+2 programs) • A large majority of students in our sample transfer from institutions with which the MIDFIELD school has a formal transfer agreement of some sort • Most literature focuses on vertical transfers from 2-year Institutions

  19. Interview results – selection of a sending institution • Student was enrolled in dual-degree program with MIDFIELD institution • Statewide transfer arrangement • To save money • Scholarship led them to choose the first institution • “Backdoor” route to MIDFIELD institution • Not admitted as freshmen • Institution has reputation for facilitating transfer to MIDFIELD institution • Admitted as freshmen but chose to start academic career elsewhere • Proximity to home/family

  20. Making the Transition • Application and Admissions Process • Orientation • Advising • At sending institution • At MIDFIELD institution

  21. Applications and Admissions Process • Most described the application/admissions process as very smooth, especially for students transferring from institutions with a formal agreement of some sort in place • But still had to be VERY motivated to find information • University websites were quite helpful to many students. • Opportunity to visit MIDFIELD campus prior to entrance was especially important. Many students received valuable assistance even before applying • But the value of the assistance varied by department within the College of Engineering

  22. Orientation (University, College, and Department) • University orientations tended to be very general (e.g., parking) • College and department level orientations more useful, but highly variable in quality. • Positive: providing transfer mentors; providing assistance with registration • Negative: treating students like freshmen; flooding email boxes; treating older students like parents • Better ones often targeted to minorities and women

  23. Advising at Sending Institution • Most students did not seek it, preferring to do their own Internet research to find out what they need. • The best advising came from institutions with formal transfer arrangements, particularly those that are nearby. However neither proximity nor a formal transfer agreement guaranteed good (or any) advising. • The best advising also comes when the MIDFIELD institution reaches out to the sending institution (e.g., by inviting advisors to campus) and promotes regular communication.

  24. Advising at the MIDFIELD Institution • Departments were generally responsive to inquiries and requests for advice from prospective students. • Many students visited the MIDFIELD institution prior to applying or enrolling. • Some did so strategically in order to stand out among the applicants • Once enrolled, the quality of first semester advising, particularly regarding courses to take, varied by department and even by year within department.

  25. Summary slide of outcomes • Persistence • Performance • The effect of race • The effect of curricular structure • The effect of engineering discipline • Stickiness • GPA shock

  26. Persistence Transfers are less likely to persist in engineering and other fields than non-transfers, on average

  27. Performance Transfers have similar GPAs to non-transfers in both engineering and overall

  28. Outcomes for URM Transfers • URM transfers significantly out-persist URM non-transfers • No significant differences in GPA between URM transfers and non-transfers • Hispanics show no differences in persistence or GPA by transfer status • Black transfers out-persist Black non-transfers. No significant differences in GPA

  29. Accessibility from Other Paths – Transfer Students The FYE institutions are at or below the average FYE: first-year engineering DM: direct matriculation PGE: post-general education

  30. FTIC vs. Transfer students • The positive effect of persisting to transfer outweighs the negative effect of other barriers transfer students face • Disciplines ranked the same for FTIC and transfer • Transfer students have less disciplinary variation (also expected)

  31. Stickiness conclusions • Stickiness allows students in diverse pathways to be compared, contrasted, or pooled. • There are large disciplinary differences in stickiness, but there is no disciplinary effect on FTIC vs. transfer outcomes • Six-year graduation is 150% of “normal time”. Estimating “normal time” for transfer students is complicated. • Currently, we count transfer credits toward the six years – as if they were all useful toward graduation. • We are developing a “percent of degree completed” that is major-dependent and recalculated each semester

  32. Outcomes – GPA ShockStudents from 4-Year Schools

  33. Outcomes – GPA ShockStudents from 2-Year Schools

  34. GPA Shock • Students from 2-year schools with 3.5-4.0 GPAs there are more likely to suffer GPA shock than students from 4-year schools. • Students with lower GPAs (2.5-3.0) are more likely to have GPAs in the same range or better at the new school, regardless of the type of prior school.

  35. Areas for Future Research • Quantitative modeling of outcomes and relationships presented here  • Study differences by full- versus part-time status and sending institution type quantitatively and qualitatively • Further investigate the influence of transfer arrangements and policies, institutional factors, parent education, social capital, access to information • Extend analysis to uncover themes related to financing of college education, self-efficacy and motivational factors, institutional fit

  36. Acknowledgment This material is based upon work supported by the National Science Foundation primarily under Grants 0969474 and 1025171, with secondary support from Grants 0935058, 1129383, and 1232740. The opinions expressed in this article are those of the authors and do not necessarily reflect the views of the NSF. Presenters • Matthew Ohland,Purdue University • Catherine Brawner, Research Triangle Educational Consultants and team members • Marisa Orr, Louisiana Tech • Catherine Mobley, Clemson • Richard Layton, Rose-Hulman • Russell Long, Purdue • ClemenciaCosentino de Cohen, Margaret Sullivan, and Michael Barna, Mathematica Policy Research • Susan Lord, University of San Diego • Erin Shealy, Clemson

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