Principals Can Improve Student Achievement with Data Driven Decision Making



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This study focused on the strategic data that a principal uses to determine the timing of appropriate interventions for students that are at risk for completing a high school education. The study examined the sources of data that are available to a principal about their students. Grades, credits earned, achievement tests, days out of placement due to discipline, attendance, gender, and socio-economic status were all examined for their significance on predicting a potential non-completer. A logistical and discriminant regression analysis was conducted on the data available. Through the analysis, the data that had the greatest impact on the predication model were related to attendance, math and English credits earned, and ethnicity. Once the identification of the relevant data was determined, a model was developed to predict a potential non-completer.



Education, Data, Data decision-making, Educational Achievement, Student identification model