Institutions of higher learning have been tracking student course-drop rates as a measure of student success along with faculty performance data. However, there is a lack of understanding as to how faculty performance data influences drop rates. The purpose of this study was to determine whether faculty knowledge of performance data creates a difference in drop rates. This study combined theories of performance measurement, decision support, self-determination theory (SDT), and personal decision making (PDM) as a conceptual foundation that linked faculty knowledge to student success. The specific research question addressed if data can be used to assist faculty efforts in reducing student attrition. This experimental longitudinal study tested the effect of faculty knowledge of personal performance measures on student course-drop rates. A sample of 32 subjects from a major university were randomly selected and assigned to equivalent-groups that included an experimental group, which received performance feedback and instruction, and an uninformed control group. Paired sample t-tests indicated a significant 32.8% reduction in student attrition for faculty in the experimental group, compared to a 10.3% increase in attrition observed for the control group faculty. Results suggest that providing faculty access to performance data via a decision support system will result in a reduction of student course drop rates. The key social value for this study is to provide a blueprint in collecting, structuring, and disseminating data that assist faculty and institutions in addressing student persistence. Students who persist in their courses have a greater potential of completing their studies and thus gaining access to better paying careers, higher levels of self-esteem, and an overall improved quality of life.
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