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A Regression Analysis of Student Motivation and the Effect of SI on Student Success Kathryn Beck Graduate Student, Applied Economics
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Supplemental Instruction, SI Academic Support Program – Historically difficult courses – High DFW rates SI Sessions for review and study – Example: Business Statistics 1
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Main Questions Does attending SI affect student achievement in a course? – How does SI affect the DFW rate? – Should the program be eliminated or extended?
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Theories Attending SI will improve a student’s final grade and increase understanding in the given course – Aid in future courses and increase graduation rates Attending SI may be worse for students who are better off studying differently
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Empirical Issues; Endogeneity Motivation – Correlation with attending SI (upward bias) At-risk students – Correlation with attending SI (downward bias) Unclear as to which direction the bias is causing
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Literature Review Correlations Only The International Center for Supplemental Instruction Empirical Models Blanc, Debuhr, and Martin (Journal of Higher Education, 1983) Bowles & Jones (Digital Commons @USU, 2003)
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University of Wisconsin, Rock County Located in Janesville, WI (pop: 60,000) One of 13 campuses of the UW Colleges Enrollment (Fall 2013): 1,120 Average class size: 24 Student profile: 52% part-time 33% non-traditional
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SI at UW Rock County Since 2011 18 SI sections offered in conjunction with ten different courses 10 SI leaders 30% participation rate in twice-weekly sessions Mean Course GPA SI participants:2.46 Non-SI participants:1.93
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Data University of Wisconsin Rock County – Student data from Spring 2011 until Fall 2013 – 824 total observations
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Model 1: Baseline Model Value-Added Education Production Function X ijt denotes a vector of student level characteristics for student i in class j in t semester Z jt denotes a vector of course level characteristics for j class in t semester ( βα…) are estimated coefficients
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Model 2: IV Estimation 1 st Stage: – Where W itj includes the instrumental variables 2 nd Stage:
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Results Baseline Models: – With all imputations, attendance is significant – Without imputations, attendance not significant IV Estimations: – With all imputations, attendance significant – Without imputations, not significant – Without variables that have missing, attendance is significant
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Table 1: Baseline Models ImputationsNo Imputations SI Attendance 0.0638*** (0.010) 0.024 (0.015) ACT 0.0527*** (0.014) 0.015 (0.02) HS GPA 0.272*** (0.082) 0.212* (0.12) Class Size 0.002 (0.006) -0.004 (0.009) Female -0.164* (0.087) -0.134 (0.119) Minority -0.476*** (0.138) -0.356* (0.192) Credits Enrolled 0.038*** (0.015) 0.038* (0.021) N706309 Unit of Observation is the numberof SI attended. The number in parenthesis is the standard error. *,**,***: Significant at the 10,5, and 1% level, respectively
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Table 2: IV, First Stage ImputationsNo Imputations Age 0.100*** (0.0289) 0.141 (0.158) Miles (in %) -0.057 (0.117) 0.179 (0.15) Likeliness 0.476*** (0.121) 0.564*** (0.162) ACT -0.033 (0.049) 0.05 (0.059) HS GPA 0.071 (0.303) -0.05 (0.388) Class Size -0.033 (0.023) -0.078** (0.038) Female 0.973*** (0.323) 0.87* (0.449) Minority 0.228 (0.574) 0.758 (0.686) Credits Enrolled 0.036 (0.054) 0.071 (0.076) N705309 F-Test10.424.95 Unit of Observation is the numberof SI attended. The number in parenthesis is the standard error. *,**,***: Significant at the 10,5, and 1% level, respectively
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Table 3: IV, Second Stage ImputationsNo Imputations SI Attendance 0.198*** (0.057) 0.107 (0.089) ACT 0.057*** (0.015) 0.012 (0.02) HS GPA 0.297*** (0.083) 0.233 (0.121) Class Size 0.008 (0.007) 0.002 (0.011) Female -0.339*** (0.121) -0.235 (0.161) Minority -0.488*** (0.147) -0.414** (0.202) Credits Enrolled 0.041*** (0.016) 0.035 (0.021) N705309 Over-ID Test0.08650.9145 Unit of Observation is the number of SI attended. The number in parenthesis is the standard error. *,**,***: Significant at the 10,5, and 1% level, respectively
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Table 4: IV, First Stage Imputations Age 0.116*** (0.028) Average Size of SI 0.463*** (0.170) Class Size -0.024 (0.023) Female 1.21*** (0.302) Minority 0.356 (0.56) Credits Enrolled 0.052 (0.054) N704 F-Test17.07 Unit of Observation is the number of SI attended. The number in parenthesis is the standard error. *,**,***: Significant at the 10,5, and 1% level, respectively
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Table 5: IV, Second Stage Imputations SI Attendance 0.232*** (0.072) Average Size of SI -0.086 (0.071) Class Size 0.012 (0.008) Female -0.33** (0.132) Minority -0.66*** (0.162) Credits Enrolled 0.07*** (0.017) N704 Over-ID Test0.0865 Unit of Observation is the number of SI attended. The number in parenthesis is the standard error. *,**,***: Significant at the 10,5, and 1% level, respectively
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Summary Running Baseline Models: – Attending SI is significant for larger sample with imputations included – Without imputations and reduced sample, attendance is no longer significant IV Models: – Age, Logmiles, and Likeliness as instruments – Correlated with Attending SI – Uncorrelated with final grade – Smaller Samples, nothing significant – Without missing variables, attendance is significant
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All Observations5 or More Sessions0 Sessions Final Grade 2.07 (1.22) 2.79 (0.98) 1.89 (1.25) Female 0.45 (0.498) 0.64 (0.48) 0.429 (0.495) Minority 0.087 (0.281) 0.117 (0.323) 0.083 (0.276) Sophomore Status> 0.423 (0.494) 0.58 (0.496) 0.381 (0.486) Credits Enrolled 12.61 (3.2) 12.42 (3.33) 12.53 (3.26) ACT 21.04 (3.24) 21.08 (2.86) 21.07 (3.31) HS GPA 2.93 (0.58) 2.95 (0.665) 2.93 (0.573) Previous College GPA 2.71 (0.47) 2.79 (0.433) 2.667 (0.48) Already SI Participant 0.081 (0.273) 0.208 (0.408) 0.046 (0.209) Required Course 0.406 (0.491) 0.506 (0.503) 0.379 (0.486) Expected Grade (4pt ) 3.36 (0.56) 3.45 (0.527) 3.34 (0.565) Female Professor 0.448 (0.498) 0.169 (0.377) 0.547 (0.498) Class Size 22.89 (8.68) 19.62 (8.51) 23.853 (8.59) Average Size of SI 2.59 (1.22) 3.348 (0.897) 2.32 (1.22) Same Day as Class 0.54 (0.498) 0.636 (0.484) 0.499 (0.5) Female SI Leader 0.577 (0.494) 0.753 (0.434) 0.513 (0.50) Section Average GPA 2.09 (0.338) 2.15 (0.359) 2.05 (0.33) N70577483 Appendix
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Variables
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