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Successful Learning Rate as an Indicator of Student Success Rates : A Correlational Study

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  • At the intersection of higher education and politics, there is demand for an easily understood measure of student success. Graduation rate is the current measure of choice, but the definition of graduation rate excludes many categories of students and types of success particularly applicable to community colleges. The purpose of this study was to explore the Successful Learning Rate (SLR) as a possible measure and predictor of student success. The SLR is the ratio of courses passed to courses attempted, e.g., three courses passed to four courses attempted yields an SLR of 3:4 or .75. The SLR includes more categories of students than graduation rate does, and it defines success along the way, not just at the end point of graduation. The research questions were: 1. To what extent does the SLR, measuring progress course-by-course, correlate with eventual graduation within 150% of the normal time to completion? 2. To what extent does the SLR vary in relation to student characteristics? 3. In that way does a correlation between the SLR and Graduation Rate suggest changing the definition of “normal time to completion” and the existing 150% threshold? The setting for this study was a mid-sized suburban community college enrolling approximately 30,000 students annually. Historical transcript data for 51,115 students were tracked between 2003 and 2013. The study used a quasi-experimental correlational design. Regression analysis was applied to selected variables to determine the relationship of student characteristics with SLR and with eventual graduation. Data collection involved gathering historic transcript data as provided by the college’s institutional research staff, with some data excluded as beyond the scope of this study. The SLR was calculated for every term during which the student was registered. The data were examined in various combinations using biserial correlation, Mann-Whitney U tests, and logistic regression. Due to the large number of students studied (N = 51,115), significance was set at p < .001. Biserial correlation revealed an almost negligible relationship between SLR and eventual graduation (r = 0.082). Mann-Whitney U tests revealed that the SLR of male students and female students did not differ significantly, p = .180. The mean SLR of part-time students (M = .91, SD = .25) was significantly higher than the mean SLR of full-time students (M = .83, SD = .28), p < .001. The mean SLR of non-traditional age students (M = .91, SD = .24) was significantly higher than the mean SLR of traditional age students (M = .83, SD = .29), p < .001. Among students who did graduate, logistic regression was used to explore relationships among variables. Gender was not a significant predictor, p = .307. The SLR was a significant predictor, OR = 1.065, p < .001, 95% CI [1.045 1.086], as was the student’s attendance type OR = 0.370, p < .001, 95% CI [.317 .432], and the student’s age, OR = 0.809, p = .015, 95% CI [.682 .960]. This study demonstrates that there is an almost negligible relationship between SLR and eventual graduation. In no analysis was gender a significant factor or predictor. Among those students who do graduate and in order of strength, (a) part-time attendance, (b) a higher SLR, and (c) non-traditional age were the most significant predictors of graduation. On paper, the SLR meets the criteria of a “good indicator.” In practice, it may be a good indicator of student success when combined with other indicators. Future research should look at the SLR over a larger student population and as applied to specific academic disciplines. Rather than examining the measure with only one institution, it would be useful to compare different institutions with different institutional and student characteristics. Further, it would be useful to examine the SLR of full-time non-traditional age students, as well as students with other characteristics. These factors may reveal correlations not evident in this study, and the SLR may yet prove to be a useful predictor of eventual graduation.
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  • 2017-08-16 to 2019-01-12

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