Data Analysis and the Four C’s of Change

At the institute where I was giving my recent presentation on data, one of the key concepts being discussed (and worked with in small “home” teams) was the concept of the Arenas of Change, otherwise known as the 4C’s. This is discussed in detail in the book Change Leadership by Tony Wagner and Robert Kegan.

These Arenas of Change are a framework through which to analyze the work of school change and serve as a guide for educators who are engaged in the work of change agents. The Arenas (or 4C’s) are:

Context — the new skills needed by students for work, learning, and citizenship

Culture — the shared values, beliefs, assumptions, and behaviors about students, teachers, learning, and leadership

Conditions — the external “architecture” that must be in place to support learning )like time for learning and collaboration, clear expectations, physical space, and staffing)

Competencies — the repertoire of skills and knowledge that positively impacts student learning and is supported by high-auality staff development

In the book — and during the work of the “home” teams at the institute — educators are/were led through activities which help them envision what their school can look like through this framework. This framework can be used in a few different ways — to examine what a school currently looks like and to determine what is needed in order to move the school to what it can become.

During my session I wanted to draw a connection between one of the data analysis tools that I was presenting and these 4C’s so that attendees would be able to bring the data analysis tools back to their teams as they continued to work through the protocols that they were using to explore the 4C’s.

The tool that I focused on for this connection was Victoria Bernhardt’s model for the intersection of Multiple Measures of Data which can be found in the book Data Analysis for Continuous School Improvement.

I proposed to the participants in the sessions that the Multiple Measures framework could be overlapped with the 4C’s framework in order to help school teams determine exactly what kind of data or evidence they should look to in order to examine each of the 4C’s on their campus.

Working together in small groups, the participants identified the measures that would be necessary for understanding each of the 4C’s. Here are the combined results from both presentation sessions:

Context — All four measures
Culture — Demographics, Perceptions
Conditions — School Processes, Demographics
Competencies — Student Learning, School Processes

Finally, I added another layer of inquiry to this data analysis process for the session attendees by explaining the Data-Logic Chain that I wrote about in a post earlier this year.

By combing all three models, school teams have tools which can direct them to:

1. The kinds of questions to ask in order to improve student learning
2. The types of data to look at in order to answer those questions
3. A process for building new questions based on those answers which will lead to overall better decision-making with regard to programs and classroom instruction.

Too Much Data?

As I have stated in earlier posts about the analysis of data, I don’t believe that most school teams are looking at enough data or asking the right kinds of questions when looking at the data that they currently examine. I know this sounds a bit absurd — most campuses are awash in TONS of data about their students. Texas educators certainly have access to an enormous amount of data, but all too often, the only data that gets discussed on our campuses is the data from Standardized Test Scores. I do believe that this type of data is important for understanding certain facts about a campus and the students, but I don’t believe that this type of data gives a school the information needed to improve student learning.

The theories and models presented above (and developed by much more experienced educators than myself) are very clear on one point about data: In order to engage in effective and continuous school improvement that leads to increased student learning, we must be analyzing more data and asking many more questions about that data.

As Victoria Barnhardt states:

…If we want to get different results, we have to change the processes that create the results. Just looking at student achievement measures focuses teachers only on the results, it does not give them information about what they need to do to get different results.

And making assumptions (or going on “hunches”) about the causes of those “results” types of data (usually standardized test scores) leads only to a guessing game where we set ourselves up for making decisions based on assumptions rather than on facts. I may know that a student didn’t master certain objectives of the curriculum, but those scores tell me nothing about WHY the student didn’t master those objectives — I don’t know if it was due to instructional delivery, classroom management, student (or TEACHER) attendance, school-wide climate (low teacher and/or student morale or increased gang activity), misalignment of the curriculum, or outside distractors like family problems. Without knowing the cause, then how can we take corrective action?

As we embark on a new school year, we must become more persistent in asking more questions about the data that is presented to us. We must start asking for more data — but asking for a greater variety of data… student and teacher surveys, information/data about school processes and programs, additional climate data like attendance and discipline, demographic data,… and so on.

We must be passionate, persistent, and diligent in our efforts at data analysis and in our efforts to improve our schools for ALL students.