Applying these six steps enables school teams to use data to target re-teaching, implement enrichments and interventions where needed, and plan for instructional improvement in the next unit.
STEP 2: IDENTIFY THE QUESTIONS TO ANSWER IN THIS DATA DIALOGUE.
Every data analysis session should be designed to answer one or more essential questions. According to school improvement expert Douglas Reeves, "having a clearly focused question will avoid the tedious and time-wasting exercise of trolling through spreadsheets and databases without any direction."28
Lachat and Smith's study of data use in several urban United States' high schools showed that the essential questions approach provides the fuel that drives collaborative analysis: "When school leaders used questions to focus the collaborative examination of data, school staff became more engaged in the process. When important questions drove the dialogue about school effectiveness, school staff quickly learned how to identify and use different types of data to answer those questions."29
Here are some examples of overarching questions that data analysis sessions by teacher teams could address:
- What knowledge and skills do our students have as we begin our new school year or unit?
- How well did our students perform in the recent benchmark assessment?
- How did student achievement vary among our several course sections?
- What is the variation in students' knowledge and skills within each of our classes or course sections?
- What instructional changes will we need to make now to increase student achievement on the MSA/HSA scheduled in six months?
- What are the strengths and weaknesses of our students as shown by a variety of assessments?
- What can we learn about our students to help us in our instructional planning?
- How can data be windows to help us better know our students?
- What do we know now about the strengths and weaknesses of our students that we did not know the last time we analyzed data?
As school teams become more data savvy and skillful, they might explore questions such as:
- To what extent have specific programs, interventions, and services improved student learning?
- What is the longitudinal progress of a specific cohort of students as they move through the grades and the course sequences of important subjects?
- What are the characteristics of students who achieve proficiency and of those who do not?
- How do student grades correlate with state assessment and district benchmark results?
- How can we use students' sub-skill data profiles to target teachers' professional development needs?30
- 28 Reeves, D. (2008/2009, December/January). Looking deeper into the data. Educational Leadership, 66 (4), 89-90.
- 29 Lachat, D. & Smith, S. (2004). Data use in urban high schools. Providence, RI: Education Alliance at Brown University, as cited in Ronka, D. et al. (2008/2009, December/January). Answering the questions that count. Educational Leadership, 66 (4), 18-24.
- 30 Questions suggested by those in Ronka, D., et al. (2008/2009, December/January). Answering the questions that count. Educational Leadership, 66 (4), 18-24.