Practical Tips for Classroom Teachers


Guest Blogger, Jennifer Ungermultiple pieces of large chart paper displaying data analysis that creates a hand-drawn data wall

When TERC’s Using Data facilitators work with schools and districts, we assist with establishing a continuous improvement culture that is grounded on collaborative inquiry. In the process, a lot of chart-paper-sized posters are created. There’s a sound and productive reason for this large-format paper trail!

Anyone who has engaged in data analysis with Using Data’s protocols and processes knows that we value public recording on chart paper because it gets everyone literally “on the same page” and talking together as they uncover learning challenges, own them, and identify strategic solutions. (more…)

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

If you want to tap one of the most powerful uses of data, disaggregate! Disaggregation means looking at how specific subgroups perform. Typically, formal student achievement data come “aggregated,” reported for the population as a whole—the whole state, school, grade level, or class. Disaggregating can bring to light critical problems and issues that might otherwise remain invisible.

For example, one district’s state test data indicated that eighth-grade math scores steadily improved over three years. When the data team disaggregated those data, they discovered that boys’ scores improved, while girls’ scores actually declined.different colored stick figures sorted into color-coordinated groups Another school noticed increased enrollment in their after-school science club. However, disaggregated data indicated that minority students, even those in more advanced classes, weren’t signing up. These are just some of the questions that disaggregated data can help answer:

• Is there an achievement gap among different demographic groups? Is it getting bigger or smaller?

• Are minority or female students enrolling in higher-level mathematics and science courses at the same rate as other students?

• Are poor or minority students over-represented in special education or under-represented in gifted and talented programs? (more…)

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

All too often state test results may be the only source consulted when targeting specific areas for improvement. However, decisions about instructional changes that reflect only this single data source, might lead to errors in your decision-making.

If you want your data to lead you toward making meaningful changes, an important principle to follow, is triangulation. wire rim glasses with three lensesTriangulation means using three independent data sources to examine apparent issues or problems. You might ask, “Why bother with the extra work of triangulating?” Consider this analogy:

A third-grade teacher asks Mary to look through the front panel of the classroom terrarium and list everything she sees. Mary diligently makes a thorough list and begins to return to her seat when the teacher asks her to take a second look through the side panel of the terrarium. She immediately sees several plants and animals obscured in the front panel view by rocks and shrubs. By using this second “window,” Mary now has a more complete picture. Then the teacher asks Mary to peer through the top of the terrarium to see if there is anything else. Mary is able to add to her list before she sits down. Her three-window analysis reveals a far more comprehensive picture than any one window alone.*

The notion of using multiple windows or perspectives also applies to understanding and applying information from student achievement data. Consider these Action Steps: (more…)

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

“Learn from yesterday, live for today, hope for tomorrow. The important thing is not to stop questioning.” Albert Einsteinmagnifying glass trained on the word why in red text

Once a school or grade-level data team has analyzed several data sources to pinpoint a student learning problem, they often feel ready to leap into action and solve it. To ensure that the solution pursued produces the hope-for results, it’s essential to engage in a collaborative process of causal analysis to identify the “root” cause of the problem.

There are many tools that support root cause analysis, one of them is referred to as Why-Why-Why—a question-asking technique used to explore cause and effect relationships. Why-Why-Why helps a group look beyond symptoms to underlying causes by taking the identified problem and asking why it exists at least three times—each time probing more deeply. (more…)

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

“Make data observations. Then generate possible explanations that inform next-steps to finding the best teaching and learning solutions.”
(from: Love, Nancy et al. The Data Coach’s Guide to Improving Learning for All Students, 2008.)

drawing of a figure with a question mark and thought bubbleData analysis is more effective, and more on-target for getting student achievement results, if a team of stakeholders first observe and list as many details as possible about what the data reveal, followed by making inferences about these observations, and then asking “why is this happening?” “what else do we need to know to be sure?”.

Infer/Question is the fourth stage in a team-based, 4-phase dialogue process* that guides deep discussion toward deriving accurate meaning from performance data. (See more information about Step 1: Predict, Step 2: Go Visual, and Step 3: Make Observations.)

These action steps will help you and your data team share inferences about the story the data reveal—inferences that will inform important next-steps toward identifying a valid student learning problem and its true causes. (more…)

Guest Blogger: Dr. William L. Heller, Using Data Program Director, Teaching Matters*

Data-savvy investigators never make important decisions based on a single source. When teams following the Using Data process believe they may have found a student learning problem, based on their analysis of standardized testing results, they know to confirm the problem through an examination of student work and other common formative assessments. When they do this, it’s important for them to have a norming process in place to ensure that group of people looking at large scoring checklist with multiple scoring options presented and a large red pencil ready to select the right checkboxthe data being generated is reliable and useful.

Norming is the process of calibrating the use of a single set of scoring criteria among multiple scorers. If norming is successful, a particular piece of work should receive the same score regardless of who is scoring it. With the advent of the Common Core State Standards Initiative, we may anticipate that curriculum-embedded performance tasks will begin to gain prominence over traditional multiple-choice tests, and it will be even more important for teachers to be aware of how to make the best use of these assessments. Whether or not they are rigorous about norming can make a very big difference. (more…)

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. (more…)

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