Analyzing your data is a process in which you will want to involve your entire staff. Good data-driven dialogue leads to data-driven decisions. If you engage staff in an ongoing data dialogue, it is much more likely that they will feel ownership for the data-based decisions you collectively make.
Principals need to develop a game plan to engage all staff in the process of analyzing state assessment data. The data analysis discussion could be initiated at a staff meeting where the principal introduced some key accountability data using the web site to show selected graphs and explanations or using transparencies printed out from the web site. This overview should include a focus on understanding the school progress targets and how to interpret the data.
Or the data analysis discussion could be initiated at a leadership team meeting where team / department leaders could examine their data and then be expected to facilitate the data analysis discussion with their team. Teams would be expected to analyze the data at a regularly scheduled team time and report their findings at an upcoming staff meeting so that all staff would have a complete picture of the student performance at their school.
Essentially, the process of data analysis is a drilling down process. As you start to see an area of concern, you want to drill down to get additional information that might give you a clearer idea of where the problem is.
To effectively lead a discussion about your assessment data, you need to ask thoughtful questions about your data results and give your staff time and support in answering the questions. You are essentially modeling constructivist teaching and/or collaborative problem solving as you lead the data analysis discussion. If your end goal is to help staff understand the data and how they inform the school's instructional program, then you will want to model a constructivist approach through the questions you ask. You will be guiding staff through the process of constructing knowledge about school progress, MSA and the student performance results for your school. If your end goal is to clarify your problem, your questions will, for the most part, model collaborative problem solving. In either case, you need to ask questions that the data answer as well as to identify questions that the data raise. It is important for staff to record the questions that the data raise and that require additional data to answer. These questions help direct the discussion in the problem clarification process of why the data look like they do.
The critical piece is that you model the importance of data analysis and that you engage all staff in the process. The odds of teachers owning the data and making the instructional changes needed for improved student achievement are much greater when they are involved in analyzing the data for what it tells them about current student achievement and their instructional program.
Leading Data Dialogues
Data dialogues should result in teachers using the information gained from that examination to improve student performance. The challenge for all school leaders is to ensure that an ongoing examination of student achievement data is used to make good instructional decisions that result in improved student learning. Though we have focused here on the analysis of the recently released school progress and MSA data, we need to emphasize that analyzing data and making data-driven decisions about instruction need to be an ongoing, collaborative process. The focus will shift from state assessment data to classroom data and the expectation will be that instructional teams use student data to make the kinds of instructional decisions that result in improved student achievement.
Leading a data dialogue effectively requires a focus, selected data, guiding questions, and an understanding of the collaborative inquiry process. Data-driven dialogue assists teams in making shared meaning of data, in surfacing multiple perspectives, in separating data from inference, and in making data-driven decisions. Though the data is key to the dialogue, the process of collaborative inquiry drives the results.
The following guidelines will assist school leaders in having a productive dialogue.
Guidelines for Leading a Data Analysis Discussion
- Determine your outcome for the discussion.
- What does the data tell us about our student's performance on MSA?
- What does our classroom data tell us about student performance on summarizing main idea? Or reading comprehension?
- What does our data tell us about staff understanding of the reading comprehension content standards?
- Choose a presentation format for the data that is easy to read
- A bar, pie, line or box and whiskers graph
- A table or chart
- Keep the focus on improvement, not on blame. If you ever want staff to feel safe in sharing and using their classroom data, you need to be very careful about using the information for improvement and not for blame.
- Model constructivist learning and/or collaborative problem solving.
- Provide adequate time for dialogue. Otherwise, you will have difficulty reaching common understanding.
- Keep the focus on what the data show, not what staff think should be done to improve the results. Educators have an inclination to solve problems, but moving to solutions should not occur until staff have both analyzed their assessment data and clarified their problem. Otherwise, solutions — even if they are effective strategies — may not address the high impact problem for the low performance.
- Guard against early conclusions of why the data look like they do. Instead, focus your discussion on identifying the questions the data raise and the additional information you need to address the questions.
Laura Lipton and Bruce Welman, who developed the Seven Norms of Collaborative Work, advise "allowing adequate time to explore assumptions, predictions, questions, and observations before offering explanations or solutions. In doing so, groups not only reach sounder conclusions but also build their capacity to inquire and learn together."
