The Role of CBSL Courses in the Retention of Non-traditional Students

Non-traditional students less likely to graduate Identifying non traditional students Older, part-time, working, caregiving, married, commuting…. Older students, 43% of undergraduates Predicted to grow 20% as opposed to 11% for traditional age students (NCES, 2011). High correlation among measures makes non-traditionality difficult to analyze Students with two or more non-traditional characteristics less likely to complete degree (CSSE, 2005; NCES, 2008). The term "nontraditional student" is not a precise one (NCES, 2002)

Factors contributing to retention Tinto (1975, 1997, 2005) identified four factors that affect retention: academic integration social integration financial pressures psychological differences Engagement as measure of academic integration: NSSE finds that students involved in “high impact practices” more likely to re-enroll (Kuh, 2012)

Service learning and retention Bringle, Hatcher and Muthiah The role of service learning on retention of first-year students to second year. Michigan Journal on Community Service Learning Spring 2010: Method: Eleven colleges in Indiana, freshman Student interviews about plans to reenroll; quality of CBSL course AND data about actual reenrollment Results: Freshman who take service learning course are more likely to reenroll (not significant when controlling for students’ stated plans to reenroll)

Rosenberg, Reed, Statham and Rosing (2011) compared students’ perceptions of their CBSL experiences at three universities and found… …adult and working students less likely to strongly agree that service learning enhanced classroom experience or skills …those with fewer previous opportunities to develop skills through work experiences appreciated CBSL …significant differences between our universities…public/private, more urban/less urban Service-learning with non-traditional students

Sample Incoming students in Fall, 2009 for three Midwestern Universities Incoming students include freshmen and transfer students University of Wisconsin-Parkside DePaul University, Chicago University of Southern Indiana Data obtained from Institutional Research Offices (IRO) Help with data collection and analysis from IRO varied by campus

Independent Variables Measures of Non-traditionality Age Fulltime/Part Time First Generation College Student Race Service Learning Demographics Gender Freshman/Transfer Students GPA Interaction terms

Methodology Logistic Regression Analysis predicts persistence or graduation or non-enrollment as dichotomous dependent variable Used backwards, stepwise technique for exploratory analysis Allows entry of sets of variables in stepwise manner to assess the relative variance explained by each model First step entered measures of non-traditionality, demographics and effects of taking a CBSL course Second step entered fulltime/part time student status Third step entered GPA Followed sample cross 1, 2, and 3 years © 1998 G. Meixner

Comparison of Means of Variables in Analysis (by Campus) Independent Variables USIUW-ParksideDePaul Service Learning Course Race (1=white) Age (1=<24) First Generation (FG=1) Entry Status (Freshman=1) Gender (Male=1) Full Time/Part Time (1=FT) GPA

USI Results Logistic Regression on Fall 2010 Enrollment/Graduation 95% CI for Odds Ratio 95% CI for Odds Ratio 95% CI for Odds Ratio B (SE)Odds RatioB (SE)Odds RatioB (SE)Odds Ratio Included Constant Service Learning.608(.166) (.197) Race.269 (.122) (.144) Age ( (.191) First Generation -.246(.087) Entry Status (.134) (.135).609 Gender -.145(.083) (.095) (.107)1.249 Full Time 2.918(.140) (.148) GPA 1.233(.069)3.433 Note: R 2 = X (Hosmer & Lemeshow),.016 (Cox & Snell),.023 (Nagelkerke). Model X 2 (1) = 45.25, p<.01. *p<.01. Note: R 2 = X (Hosmer & Lemeshow),.211 (Cox & Snell),.295 (Nagelkerke). Model X 2 (1) = , p<.01. *p<.01. Note: R 2 = X (Hosmer & Lemeshow),.334 (Cox & Snell),.468 (Nagelkerke). Model X 2 (1) = , p<.01. *p<.01.

95% CI for Odds Ratio 95% CI for Odds Ratio 95% CI for Odds Ratio B (SE)Odds RatioB (SE)Odds RatioB (SE)Odds Ratio Included Constant Service Learning.448(.108) (.116) Race.582 (.167) (.193) Age -.637(.216) (.774)-.529 First Generation Entry Status -.382(.165) (.134) (.206).461 Gender (.132) Full Time 2.771(.159) (.182)9.326 GPA 1.734(.119)5.666 Note: R 2 = X (Hosmer & Lemeshow),.027 (Cox & Snell),.042 (Nagelkerke). Model X 2 (1) = 44.05, p<.01. *p<.01. Note: R 2 = X (Hosmer & Lemeshow),.194 (Cox & Snell),.301 (Nagelkerke). Model X 2 (1) = , p<.01. *p<.01. Note: R 2 = X (Hosmer & Lemeshow),.297 (Cox & Snell),.459 (Nagelkerke). Model X 2 (4) = , p<.01. *p<.01. USI Results Logistic Regression on Fall 2011 Enrollment/Graduation

95% CI for Odds Ratio 95% CI for Odds Ratio 95% CI for Odds Ratio B (SE)Odds RatioB (SE)Odds RatioB (SE)Odds Ratio Included Constant Service Learning.430(.090) (.094) Race.679 (.218) (.254) Age -.680(.232) (.297)-.578 First Generation Entry Status -.590(.213) (.213) Gender Full Time 2.709(.191) (.196)9.591 GPA 1.73(.160)5.657 Note: R 2 = X (Hosmer & Lemeshow),.033 (Cox & Snell),.006 (Nagelkerke). Model X 2 (1) = 50.10, p<.01. *p<.01. Note: R 2 = X (Hosmer & Lemeshow),.169 (Cox & Snell),.304 (Nagelkerke). Model X 2 (4) = , p<.01. *p<.01. Note: R 2 = X (Hosmer & Lemeshow),.236 (Cox & Snell),.423 (Nagelkerke). Model X 2 (3) = , p<.01. *p<.01. USI Results Logistic Regression on Fall 2012 Enrollment/Graduation

95% CI for Odds Ratio 95% CI for Odds Ratio 95% CI for Odds Ratio B (SE)Odds RatioB (SE)Odds RatioB (SE)Odds Ratio Included Constant.672 (.155).192 (.169) (.277) Service Learning.234 (.337) (.228)* (.241)*1.810 Race (.146) (.160)* (.170)*.650 Age (.223) (.239) (.256).896 First Generation (.134) (.147) (.158)*.679 Entry Status.129 (.158) (.172) (103).919 Gender.058 (.130) (.142) (.151).827 Full Time (.158)* (.051).827 GPA (.108)*2.804 UW-Parkside Results Logistic Regression on Fall 2010 Enrollment/Graduation

95% CI for Odds Ratio 95% CI for Odds Ratio 95% CI for Odds Ratio B (SE)Odds RatioB (SE)Odds RatioB (SE)Odds Ratio Included Constant (.154) (.185) (.426) Service Learning 1.031* (.154) (.184)* (.211)1.214 Race.266 (.141) (.166) (.198)1.138 Age (.217) (.247) (.315).750 First Generation (.131) (.157) (.196).704 Entry Status.221 (.153) (.182) (.228)1.010 Gender (.127) (.151) (.191)*.620 Full Time (.184)* (.221)*3.558 GPA (.174)*4.218 UW-Parkside Results Logistic Regression on Fall 2011 Enrollment/Graduation

95% CI for Odds Ratio 95% CI for Odds Ratio 95% CI for Odds Ratio B (SE)Odds RatioB (SE)Odds RatioB (SE)Odds Ratio Included Constant (.171) (.218) (.527) Service Learning (.102)* (.115)* (.130)*1.317 Race (.157) (.188) (.255)*.558 Age (.233) (.268) (.374).967 First Generation.062 (.140) (.177) (.226).931 Entry Status.317 (.164)* (.201)* (.282)1.090 Gender.294 (.137)* (.174)* (.224)1.295 Full Time (.223)* (.256)*7.645 GPA.276 (.130)*1.317 UW-Parkside Results Logistic Regression on Fall 2012 Enrollment/Graduation

DePaul Results Logistic Regression on Fall 2010 Enrollment/Graduation B (SE)Odds Ration Constant-.524 Service Learning (.961).270 Race.572(.415)1.771 Age.178(.916)1.195 First Generation Entry Status-.465(.133).628 Gender Full Time-2.207(.686)..110 GPA1.066(.281) R square =.115 (Cox & Snell) Chi Square= p= (Nagelkerke)

Significance of Service-Learning by Institution Students who take service-learning courses are more likely to persist. USI effect disappears after adding GPA UW-Parkside effect remains after adding GPA DePaul no significant effect

Age Age is a weak predictor of persistence at one university; younger students are more likely to reenroll USI Age effect disappears after adding full-time/part-time and GPA UW-Parkside No effect DePaul No Effect

Race Race is an inconsistent predictor of persistence with white students more likely to reenroll. USI race effect disappears after adding GPA UW-Parkside race effect remains but is weakened by adding part- time/full-time status and GPA DePaul no effect

First Generation Students who have college-educated parents are more likely to persist but this effect disappears after the 1 st year. USI effect disappears after adding full-time/part-time & GPA UW-Parkside effect disappears after adding part-time/full-time status and GPA DePaul no data

Transfer Students Transfer students are more likely to persist but the effect is mitigated by full-time/part-time & GPA. USI effect disappears after adding full-time/part-time status & GPA UW-Parkside effect disappears after adding full-time/part-time status and GPA DePaul effect disappears after adding full-time/part-time & GPA

Comparing institutions: Challenges Working with Offices of Institutional Research (e.g., degree of responsiveness) Data collected not even across institutions

Service-Learning has a positive effect on all students (traditional and non-traditional) Part-time is the most significant characteristic of non-traditional students in relation to persistent enrollment Implications

Implications for further research Extend timeframe for analysis because non-traditional students take longer to complete their degree. Difficulty of analyzing measures of non-traditionality because they are highly correlated. Consider the different reasons that students enroll part- time (defining “part-time” may be different for traditional and non-traditional students)

Discussion Thank you Susan Reed Helen Rosenberg Anne Stratham Howard Rosing