GUEST BLOGGER: Mary Anne Mather, Using Data Facilitator & Social Media Liaison on Twitter & FaceBook

Set aside assumptions, and focus on just the “data facts” before leaping to explanation and interpretation.
The Data Coach’s Guide to Improving Learning for All Students

Teachers are natural problem solvers. When we see evidence of individual students struggling, or indicators in our data that groups of students are underachieving, we are anxious to find solutions. The Using Data process advocates a “hold your horses” mindset that can help teachers to better pinpoint a student learning problem before jumping to explanations, interpretations, and quick-fix solutions. Data analysis is more effective if a team of face showing only one open eyestakeholders takes the time to observe and record as many details as possible about what the data reveal.

Observe is the third stage in a 4-phase dialogue process* that guides deep discussion toward deriving accurate meaning from the data. (See more information about Step 1: Predict and Step 2: Go Visual.) Engaging in this process as a data team, rather than individually, can garner the greatest impact toward improved student achievement.

The observation phase of the 4-phase dialog process is often the most difficult to maintain. It is helpful to assign a group dialogue monitor who keeps the discussion at this phase from moving too quickly to “becauses” and “we shoulds.” Observations start with phrases such as “I notice that…, I see that…, I’m struck by…., I’m surprised that…”

Sample Observations
What makes a good observation statement? Depending on the type of data you are looking at, the observations might resemble the samples below. And here are some questions to further guide the creation of refined and specific data observation statements:

–Does each statement communicate a single idea about student performance?

–Are statements short and clear?

–Do the statements incorporate numbers (the data)?

–Do the statements focus just on those direct and observable facts contained in the data, without explanation or inference?

–Do the statements use relevant data concepts such as mean, median, mode, range, or distribution?

Sample aggregate data observation:
I notice that
at the school level, student performance in math increased from Year 1 to Year 2 (44 percent to 47 percent) and then declined in Year 3 (to 33 percent).

Sample disaggregate data observation:
I see that
in the most recent year of data at the school level, 44 percent of sixth-grade African American students performed at the lowest performance level in English language arts, compared with 36 percent of Hispanics and 35 percent of white students.

Sample student work data observation:
I’m surprised
that
our regular education and special education students had the same difficulty with the vocabulary used in this open response science question.

Follow these action steps to move from a visual representation of your data to discovering all the facts your data can reveal as your data team makes observations.

 Action Steps

• First, gather your data team members. They might be a grade-level or vertical team, a subject-area department, or your school leadership team.

• Together, study a visual representation of the data you want to analyze. Allow some quiet “think time” to allow members a chance to digest and make sense of what they see. Provide some think time prompts, such as:

–What important points seem to pop out?

Police Sergeant badge

"Just the facts, please."

–What are some patterns and trends that are emerging?

–What seems surprising or unexpected?

• Share observations about the data. Stick to articulating “just the facts.” A round robin brainstorming strategy works well when making data observations. It encourages all data team members to look closely at the data and have a voice at the table.

• Capture each observation on chart paper. Continue the process until all possible observations are surfaced and captured.

As referenced in the samples, often the most insightful and informative observations can be made when multiple years of data are studied together. Data team members can learn: How did we do this year on the district benchmark assessments, compared to last year? Is this cohort of students continuing to progress in math? If you started your observations with only one year of data, you may want to gather additional data and add to the list. After capturing a complete set of observations, only now is the team ready to generate possible explanations for what they observed. Our next data tip will discuss Step 4 in the 4-phase dialogue process: Making Inferences.

*Adapted from Wellman, B., & Lipton, L., 2004. Data-Driven Dialogue: A Facilitator’s Guide to Collaborative Inquiry. Sherman, CT: MiraVia LLC. Used with permission.