A Comparative Analysis of Traditional Versus Block and Accelerated Block Scheduled High Schools Over an Eight-Year Period in a Large Urban School District

This study compared traditional, A/B and accelerated block scheduling and its effects on student achievement and attendance by comparing the differences in student outcomes observed before and after the adoption of block/accelerated block schedules. The independent variable was the use of time in a block-scheduling model. The dependent variables were student outcomes measured by nine indicators based on the Academic Excellence Indicator System in Texas: student attendance, graduation rate, dropout rate, percentage of students taking advanced courses, percentage of students passing all Exit-level Texas Assessment of Academic Skills tests, percentage of students taking College Admissions Tests, mean SAT total score of those students who took the SAT, mean ACT total score of those students who took the ACT, and percentage of students who are at or above criterion on the SAT or ACT of those students taking the SAT or ACT. Data from archival files from the Texas Education Agency's Academic Excellence Indicator System for each respective year of the eight-year longitudinal study was collected. Scheduling models (traditional, alternating block and accelerated block) were investigated. The sample was drawn from the student population of a large urban school district in north central Texas, a district serving approximately 77,000 students. The district has twelve regular high schools serving students in grades nine through twelve. All twelve regular high schools were included in this study. The indicators were analyzed using SPSS multivariate and univariate analysis to compare the means, regression line slopes, and regression line intercepts for each type of schedule: traditional only, traditional prior to A/B block change, traditional prior to accelerated block change, A/B block, and accelerated block. The regression line, slopes, and intercepts were based on separate regression analysis where a school year was used to predict the AEIS indicators for each type of schedule. With the exception of graduation rate, significant difference was found for all dependent variables