Presentation Overview

Transcription

1 Factors, Practices, and Policies Influencing Students Upward Transfer to Baccalaureate-Degree Programs and Institutions Barbara K. Townsend Dissertation of the Year Presentation National Institute for the Study of Transfer Students Conference Atlanta, GA February 5, 2014 Robin LaSota, PhD, University of Washington Post Doctoral Research Associate, University of Illinois Urbana-Champaign (UIUC) Office of Community College Research and Leadership (OCCRL)

2 Presentation Overview Research Questions Quantitative and Qualitative Strands Design Choices Findings Implications Analytical Limitations Manuscripts to submit

3 A First Area of Inquiry Q1.1 How do student behaviors, community college characteristics, and state policies and conditions influence students upward transfer probability? Q 1.2 How do these factors, policies, and conditions influence upward transfer probability, particularly for low-income and first-generation community college students?

4 Student, College, and State Factors Influencing 2/4 Transfer

5 A Second Area of Inquiry Q2.1 What are promising practices in colleges and states aimed at improving students upward transfer, and how may they constitute a system of support for improved 2/4 transfer? Q2.2 How do leaders engage in ongoing innovation around these practices?

6 Explanatory Sequential Mixed Methods Design Multi-level modeling analysis of BPS and supplemental data Case studies of 6 colleges in 3 states QUANT/QUAL Integration: 1) Using QUANT anal. To guide QUAL sampling 2) Cross-reference claims from QUANT and QUAL 3) Use QUAL to complement and extend QUAN. 4) Integrate both strands to guide future mixed methods inquiry

7 Sampling Design Choices from BPS N=5010 community college students; weighted N=1,528,900 Students not co-enrolled in two or more colleges BPS nationally representative longitudinal population survey of all postsecondary entrants BPS not representative of states, colleges, or CC entrants State and community college factors investigated build upon existing literature

8 Which State and Community College Factors May Influence Transfer? State articulation and transfer policies? Very little, if any influence (Roksa, Kienzl, Goldhaber & Gross) State cooperative agreements? Maybe. (Kienzl) Community college practices? Depends. Perhaps not much. (MDRC) Community college expenditures? Slight/student services expenditures (Gross and Goldhaber). None (Stange). Community college smaller size, higher faculty-to-student ratio? Yes. (Bailey et al., Gross & Goldhaber) Degree of college mission stratification (emphasis on transfer-oriented programs vs. non-transfer oriented, e.g. health/vocational/technical). Influential. (Dougherty) Proximity and selectivity of nearest public four-year institution? Maybe. (Rouse)

9 Rationale for Multi-Level Methodology Results of Unconditional Model or Intra-Class Correlation State location explains 2% of variance in 2/4 transfer probability Primary college attended explains 6% of variance Student characteristics explains most of the variance Therefore, used multi-level logistic regression Randomly varying intercepts and slopes between colleges and states for Low-income, first generation First generation, not low income Planned to transfer at time of entry Declared major in health/vocational/technical field

10 Positive Predictors Associated with Upward Transfer Probability 0 = 50/50 Probability of 2/4 Transfer Conditioned on Factors in the Model Primarily Full-Time Aged at Entry Worked 1-19 Hrs/Wk on Average GPA in First Year (tenths) Sports Participation Often or Sometimes STEM, Humanities, Education Major Gross State Product (standardized) Academic Advising Often or Sometimes CC Transfer Out Rate Planned to Transfer at Entry

11 Negative Predictors Associated with Upward Transfer Probability 0 = 50/50 Predicted Probability of 2/4 Transfer Conditioned on Factors in the Random Effects Model Primarily Part Time First Generation, Low Income First Generation, Not Low Income CC Pct Health Voc Completions Took Any Remedial Education Unemployment in CC's County Health, Vocational, or Prof/Tech Major

12 College Characteristics: Association with 2/4 Transfer Proportion of associates degree completions in health/vocational fields (neg., p<.10) College transfer-out rate (2% increased odds of transfer) in regression without analysis of random effects by slope County-level unemployment (neg., p<.10) Not sig. = i.e. per-student expenditures for instruction or student services, distance to nearest public four-year institution, distance to nearest non or less-selective four-year institution, faculty-to-student ratio, community college enrollment size, percent of full-time faculty, percent of full-time students

13 State Policies Association with Students Upward Transfer Main Effects Model, Random intercepts only, no varying slopes + 35% higher 2/4 transfer odds: State with one standard deviation higher Gross State Product Per Capita in 2003 None of the State Articulation and Transfer Policy Components explained variance in 2/4 transfer probability. Policy Components - Transfer data reporting - State transfer incentives - State transfer guide - Transferable general education curriculum - Statewide cooperative agreements - Common course numbering - Statewide articulation/transfer policy

14 Regression Results: Slopes for Sub-Populations that Vary by College and/or State Random and Fixed Effects Model showed that these factors moderate 2/4 transfer probability for these populations. Low-Income, First Generation: Higher gross state product Common course numbering College transfer-out rate First Generation, Not Low Income: Higher Gross State Product Common Course Numbering Intending): College transfer-out rate Health/Vocational Major (vs. business/undeclared): State articulation/transfer policies not sig. Transfer-out rate not sig. Planned to Transfer (vs. Not Transfer

15 Findings: Quantitative Inquiry Strand Affirmed prior research about ambiguous or unknown effects of state transfer and articulation policies Offered new evidence about the role of state common course numbering in increasing first-generation students transfer Influential college-level factor College mission focus; college s transfer-out rate Full-time attendance and transfer intention are particularly influential student factors

16 Some Implications: Quantitative Strand Promising areas for policy intervention, esp. in high schools Help students create specific plans for obtaining a bachelor s degree aligned in a specific field and outline a transfer pathway Promote continuous full-time attendance and advising with incentives and accountability Widely promote available state resources and policies for improved transfer

17 Rationale for Case Study Design Goal: To explore and identify possible state policy actions and college policies or practices that enhance student 2/4 transfer probability Structure analysis for meaningful contrasts relative to the goal States and Colleges with Higher Transfer vs. Average Transfer Rates (within their state) Policy Innovative States in Articulation and Transfer Colleges Engaged in Data-Use and Innovation States with significant CC sector and states & colleges with student populations of interest

18 State Case Selection: Florida, Georgia, and Washington Used OLS regression to find states performing above average in transfer, controlling for state and student population characteristics Considered prior research on policy innovative states in transfer and articulation Chose states with a considerable proportion of postsecondary students enrolled in two-year colleges and with racial/income diversity

19 College Selection: Above-Average and Average Performer Used OLS regression to find colleges performing above average in transfer, controlling for college and student population characteristics Consulted State Higher Education Executive Officers (SHEEO) from each state and Aspen Prize Top 120 data Used SHEEO advice and college s participation in Achieving the Dream indicators of data-use and innovation

20 Qualitative Methods Interviews with state policy officials in articulation and transfer (N=20) Interviews with college administrators, faculty, and student affairs staff (N=110) Individual interviews and focus groups with students (N=49) N=179 overall Analytic Strategy: Analytic Memo Writing and Data Synthesis

21 Findings: Qualitative Inquiry Strand

22 Findings Advising in Above-Average Performers Transfer not a universal outcome or push for all students College-level systems of support for transfer generally constrained Above-average colleges generally have: Academic leaders who champion students transfer and successfully engage others in this work Mandatory student advising models Student affairs staff dedicated to coaching students on transfer

23 Findings Advising in Above-Average Performers Above-average colleges also tend to have: Faculty contracts which include student advising hrs. Faculty and staff engaged in planning out-of-class supports and enrichment experiences for students that aid transfer Campus supports for TRIO and similar STEM programs for low-income, minority, and first generation students Key Support for Stronger Advising: Active communication/coordination with public and private four-year institutions within major fields by administrators and faculty

24 Findings State Policy as a Context for Colleges Innovation Creating a stronger system of support for students upward transfer State-college collaboration on policy design 2-yr to 4-yr collaboration on articulation and transfer..robust communications and Data-based problem solving focused on increasing step-by-step outcomes to BA attainment supports college-level innovation

25 Common Course Numbering: Lessons Learned from Florida Moderating positive influence of common course numbering (CCN) for first-generation college students from quantitative inquiry CCN Proxy for a more robust transfer policy context? CCN built from communication across lower and upper division faculty and programs Florida: CCN in place for 30 yrs; created when 2 yrs and 4 yrs were governed in one system

26 Some Implications: Qualitative Inquiry States: Incentives and support for college-level innovation Support for measuring innovation effectiveness Build transfer into performance accountability Colleges: Collaborative problem-solving re: transfer Broad implementation of personalized learning & transfer advising Incentives to be transfer champions States and colleges: More efficient, accessible processes to using data for decision support about students transfer

27 Analytical Limitations - Quantitative Data Limitations BPS measures of academic and social integration State policy measures binary coding No adequate measure of policy strength for the period Available college-level data mostly not predictive of transfer Not a causal inference multi-level model Does not examine reasons for stopping out or mixed attendance e.g. role of financial aid

28 Analytical Limitations - Qualitative Examined broad scope of practices affecting transfer probability (from pre-college to graduation check/final term advising) rather than one or two specific innovations Used analytical memo writing not software-based coding methodology Inductive approach to claim formulation rather than deductive hypothesis-testing Different framing literatures inform each strand, complementary analyses, not necessarily integrated

29 With Appreciation To all the participants in my study To Debra Bragg and OCCRL for post-doc support To NISTS for the honor of the award and presentation with you To my chair, Bill Zumeta To my co-advisor, Marge Plecki To my committee members: Mike Knapp Bob Abbott Jennie Romich To my fellow doctoral students And to IES and AIR for funding Sponsored by the US Department of Education, Institute of Education Sciences (#R305B090012) and the Association of Institutional Research Dissertation Grant

30 Manuscripts to be submitted What Matters In Increasing Community College Students Upward Transfer to the Baccalaureate Degree: Findings from the Beginning Postsecondary Study (Research in Higher Education/AIR) Supports and Barriers for Data-Based Decision-Making to Improve Students Upward Transfer (Review of Higher Education/ASHE) How CC Leaders Engage in Innovation to Improve Transfer (Journal of Community College Research and Practice) Mixed Methods Design Challenges and Opportunities: A Sequential, Explanatory Approach to Studying Students 2/4 Transfer (Journal of Mixed Methods Research)