The K-12 Literacy Leadership Fieldbook by Taylor Rosemarye T.;Gunter Glenda A.;

Author:Taylor, Rosemarye T.;Gunter, Glenda A.; [Taylor, Rosemarye T.; Gunter, Glenda A.]
Language: eng
Format: epub
ISBN: 1994348
Publisher: Corwin Press
Published: 2005-05-18T00:00:00+00:00

STEP 6: ASKING WHAT IS WORKING AND WHAT IS NOT

Prior to asking the big question posed in Step 6, you will want to review school data from multiple perspectives and in the light of student and teacher subcategories.

First you will want to determine the categories of student data that are important. For instance, you will want to look at data by these groupings: first, grade level, class, gender, second language, special education, ethnicity, and free and reduced-fee lunch. Some of these categories may not be appropriate for your students, and you may have others to add. Until we know who we are succeeding with and who we are not succeeding with, we really cannot move forward with planning to assist in improving literacy or content learning. Generally, girls outperform boys in reading, and white boys outperform Hispanic and African American boys. All students may not require the same level of literacy learning; some may have emergency status.

Adairsville, Kentucky, has a kindergarten through eighth grade school. Former principal Mike Hurt charted student achievement data each year and documented gains. These gains were by school, by grade, and by classroom. He noted that there were teachers who consistently had greater gains than others and found it interesting that the teachers with the greatest gains attributed the gain to the students. Another point of interest was that the teachers who did not have the greatest gains also attributed their lack of gain and the gain of the other teachers to the students. With this observation, Mike strategically set expectations and provided appropriate professional development for faculty and interventions for those children who needed it most. These data-driven decisions (using both hard data and observational data) assisted Adairsville K–8 School in becoming a nationally recognized school for consistent growth in student achievement.

At Sebastian River High School, they know which students perform at what level, so they can intervene to the extent of the specific student’s lack of literacy performance. The students all belong to an “academy” (a small learning community), and the administrators know which academies made the greatest gain and which made the least. By looking at achievement and growth based on class, teacher, team, or academy, a school can learn from the teachers and from the data whose students are growing the most. The administrators or literacy coach can also “coach up” the teachers whose students lack growth in reading or content learning.

An example of typical data-driven decision-making practice would be to compare fourth grade scores for 2004 with fourth grade achievement of 2005. What is wrong with this data analysis? The comparison is for different groups of students, who are assumed to be alike, but all teachers know that each year’s group of students is decidedly different from the last, for a multitude of reasons. In contrast to comparing different years’ students in the same grade level, track cohorts of students from one grade level to the next to determine whether the group is improving in their mean reading score. If the mean is not increasing each year, then there is a red flag for improving literacy learning.

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