A STUDY TO INFORM PRINCIPALS ABOUT AN INSTRUCTIONAL DESIGN, LOOPING, AND ITS INFLUENCE ON STUDENT ACHIEVEMENT

With No Child Left Behind (2001), annual yearly progress, and high stakes testing, administrators must implement structures and processes that maximize the learning time. In addition, with a growing number of at-risk students, English Language Learners, and economically disadvantaged students, teachers must implement best practices with the latest research in the creation of a classroom climate that allows students to take risks. This has developed the need for looping, when students remain with their teacher(s) for more than one year to ensure academic success and stronger relationships. This study addressed the effects of looping in grades five and six on maximizing learning time and strengthening the relationship between the teacher and students by addressing the following questions: (1) Do statistical differences exist in reading comprehension achievement between students in the intermediate level grades who experienced looping than those who did not? (2) Do statistical differences exist in mathematics achievement between students in the intermediate level grades who experienced looping than those who did not? Archival data of the AIMSweb Universal Curriculum Based Measure, or General Outcome Measure, were collected from the beginning of the year in fifth grade and compared to the middle of the year in sixth grade. The gain scores were calculated using a T-Test was to compare the growth of the looping group versus the non-looping group. The AIMSweb Universal Curriculum Based Measure (Reading MAZE) data indicated for research question one was to accept the hypothesis for there was no statistical significance between the looping and non-looping group. The AIMSweb

Universal Curriculum Based Measure (Mathematics CBM) data indicated for research question two was to accept the hypothesis for there was a statistical significance between the looping and non-looping group. Recommendations for future research include: optimal length for looping, optimal age to loop, adding qualitative data, and implications of extra learning time.