the role of cbsl courses in the retention of non-traditional students

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

Students

Susan Reed DePaul University

Helen Rosenberg University of Wisconsin-Parkside

Anne Statham University of Southern Indiana

Howard Rosing DePaul University

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: 38-49.

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-traditionalityAge

Fulltime/Part Time

First Generation College Student

Race

Service Learning

DemographicsGender

Freshman/Transfer Students

GPA

Interaction terms

MethodologyLogistic Regression Analysis

predicts persistence or graduation or non-enrollment as dichotomous dependent variable

Used backwards, stepwise technique for exploratory analysisAllows 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 USI UW-Parkside DePaul

Service Learning Course .08 .13 .10

Race (1=white) .89 .71 .60

Age (1=<24) .89 .88 .92

First Generation (FG=1) .33 .62

Entry Status (Freshman=1) .75 .72 .64

Gender (Male=1) .43 .57 .44

Full Time/Part Time (1=FT) .80 .44 .90

GPA 2.661 2.593 3.09

USI ResultsLogistic Regression on Fall 2010 Enrollment/Graduation

95% CI for Odds Ratio



B (SE) Odds Ratio B (SE) Odds Ratio B (SE) Odds Ratio

Included

Constant .667 -1.426 -4.149

Service Learning

.608(.166) 1.836 .509(.197) 1.661 ------ ------

Race .269 (.122) 1.308 .290(.144) 1.337 ------- ------

Age -.428 (.129 .652 .770(.191) 2.160 ------- ------

First Generation -.246(.087) .782 ------- ------ ------- ------

Entry Status ------ ------- -.489(.134) .613 -.498(.135) .609

Gender -.145(.083) .865 -1.89 (.095) .828 .223(.107) 1.249

Full Time 2.918(.140) 18.508 2.497(.148) 12.147

GPA 1.233(.069) 3.433 Note: R2 = X (Hosmer &

Lemeshow), .016 (Cox & Snell), .023 (Nagelkerke). Model X

2(1) = 45.25, p<.01. *p<.01.

Note: R2 = X (Hosmer & Lemeshow), .211 (Cox & Snell), .295 (Nagelkerke). Model X

2(1) = 665.31, p<.01. *p<.01.


2(1) = 1126.91, p<.01. *p<.01.





Included

Constant 1.022 -.434 -4.505

Service Learning

.448(.108) 1.565 .217(.116) 1.242 ------ -------

Race .582 (.167) 1.790 .545(.193) 1.724 ------- -------

Age -.637(.216) .592 ------- ------ -.643(.774)- .529

First Generation

------ -------- ------- ------ ------ -------

Entry Status -.382(.165) .683 -1.005(.134) .366 -.774(.206) .461

Gender -------- -------- -.235 (.132) .791 --------- -------

Full Time 2.771(.159) 15.974 2.233(.182) 9.326

GPA 1.734(.119) 5.666


2(1) = 44.05, p<.01. *p<.01.


2(1) = 406.13, p<.01. *p<.01.


2(4) = 661.61, p<.01. *p<.01.






Included

Constant .995 -.249 -4.550

Service Learning

.430(.090) 1.537 .2687(.094) 1.307 ------ ----

Race .679 (.218) 1.972 .574(.254) 1.775 ------- ----

Age -.680(.232) .506 ------- ------ -.548.(.297)- .578

First Generation

------ ----- ------- ------ ------ ------

Entry Status -.590(.213) .683 -.590(.213) .554 --------- -------

Gender -------- -------- -------- ------- --------- ----

Full Time 2.709(.191) 15.015 2.261(.196) 9.591

GPA 1.73(.160) 5.657


2(1) = 50.10, p<.01. *p<.01.


2(4) = 274.694, p<.01. *p<.01.


2(3) = 398.94, p<.01. *p<.01.






Included Constant .672

(.155) .192 (.169) -1.796 (.277) Service Learning

.234 (.337) 1.264 .536 (.228)* 1.709 .593 (.241)* 1.810

Race -.099 (.146) .905 -.306 (.160)* .680 -.432 (.170)* .650

Age -.034 (.223) .966 .292 (.239) 1.339 -.109 (.256) .896

First Generation

-.064 (.134) .966 -.190 (.147) .827 -.387 (.158)* .679

Entry Status .129 (.158) 1.138 .134 (.172) 1.143 -.084 (103) .919

Gender .058 (.130) 1.060 -.080 (.142) .923 -.190 (.151) .827

Full Time 1.877 (.158)* 6.534 -.190 (.051) .827GPA 1.031 (.108)* 2.804

UW-Parkside ResultsLogistic Regression on Fall 2010 Enrollment/Graduation





Included Constant -.402 (.154) -.886 (.185) .412 -3.251 (.426) Service Learning 1.031* (.154) 2.805 .613 (.184)* 1.846 .194 (.211) 1.214Race

.266 (.141) 1.305 .050 (.166) 1.051 .129 (.198) 1.138Age

-.023 (.217) .978 .296 (.247) 1.344 -.287 (.315) .750First Generation -.008 (.131) .992 -.213 (.157) .808 -.351 (.196) .704Entry Status

.221 (.153) 1.247 .238 (.182) 1.268 .010 (.228) 1.010Gender -.048 (.127) .954 -.216 (.151) .806 -.478 (.191)* .620Full Time 2.828 (.184)* 16.904 1.269 (.221)* 3.558GPA 1.439 (.174)* 4.218






Included Constant -1.145 (.171) -1.991 (.218) .137 -1.975 (.527) Service Learning 1.097 (.102)* 2.995 .811 (.115)* 2.249 .276 (.130)* 1.317Race

-.006 (.157) .994 -.252 (.188) .778 -.583 (.255)* .558Age

-.118 (.233) .889 .115 (.268) 1.122 -.034 (.374) .967First Generation .062 (.140) 1.064 .021 (.177) 1.021 -.071 (.226) .931Entry Status

.317 (.164)* 1.373 .773 (.201)* 2.167 .086 (.282) 1.090Gender .294 (.137)* 1.341 .374 (.174)* 1.454 .258 (.224) 1.295Full Time 3.506 (.223)* 33.316 2.034 (.256)* 7.645GPA .276 (.130)* 1.317


DePaul ResultsLogistic Regression on Fall 2010 Enrollment/Graduation

B (SE) Odds Ration

Constant -.524

Service Learning -1.311 (.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

GPA 1.066(.281) 2.904

R square =.115 (Cox & Snell) Chi Square=34.841 p=.776

.205 (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 1st 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 StudentsTransfer 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 [email protected]

Helen [email protected]

Anne Stratham [email protected]

Howard [email protected]

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the role of cbsl courses in the retention of non-traditional students

Documents

older students

year students

students perceptions

sample incoming students

traditional students

role of service learning

traditional age students

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