Data Tip #3: "Go Visual"
(Love, Nancy et al., The Data Coach’s Guide to Improving Learning for All Students, 2008, p.7)
Teachers have access to rich and varied student data, often provided in a variety of computer-generated documents with lots of numbers. Where does a data team begin their dialogue about what the numbers show? How can the team integrate multiple sources of data to tell a coherent story? How can a data team bring to life pages of numbers, so that the data can paint a picture about student learning? One way to illuminate the stories within the data is for data teams to create their own visual display of the data. We call it "Go Visual!" Read more...
Data Tip #4: "Make Data Observations: Before Identifying Solutions, Get All the Facts on the Table."
(Love, Nancy et al., The Data Coach's Guide to Improving Learning for All Students, 2008)
Teachers are natural problem solvers. When we see evidence in our data that groups of students are underachieving, we are anxious to find solutions. But data analysis is most effective if a team takes the time to observe and record as many details as possible about what the data reveal. 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. Read More...
Data Tip #5 "Make Inferences and Question Your Data's Story"
“Make data observations. Then generate possible explanations that inform next steps to finding the best teaching and learning solutions.”
(Love, Nancy et al., The Data Coach’s Guide to Improving Learning for All Students, 2008.)
Before generating solutions, be certain that you fully understand the problem. As a data team, take the time to verify what learning problems are revealed in your data—and why—before suggesting solutions. After making observations about the data and listing details about what you see in it, draw inferences about why these observations are revealed. Ask yourselves, “Why are we seeing this result?” and/or “What else do we need to know to be sure of this observation?” Making inferences and asking questions before finding solutions is a classic example of the 'go slow to go fast' strategy. It gets you on track for making sure the problem you are solving is one you actually have! Read More...
Data Tip #6 When Analyzing Causes, Ask "Why? Why? Why?"
“Learn from yesterday, live for today, hope for tomorrow. The important thing is not to stop questioning.” Albert Einstein
Once a data team has analyzed several data sources to pinpoint a student learning problem, they often feel ready to leap into action and solve it. But the data team should first engage in a collaborative process of causal analysis to identify the 'root' cause of the problem, to ensure that the solution they propose addresses the true problem and produces the desired results.
One tool data teams can use to support a root-cause analysis is called “Why? Why? Why?”—a questioning technique used to explore cause-and-effect relationships. “Why? Why? Why?” helps a group look deeply, beyond the symptoms of a problem, to find underlying causes by asking “Why?” at least three times. Each time the question is asked, the team is probing more deeply into the root cause. Read More...
Data Tip #7 Finding Time For Data Inquiry
“Time for teacher collaboration is not a luxury... It is a necessity for schools that want to improve.” (Love, N., Ed.,Using Data to Improve Learning for All, 2009)
Recently, teams of teachers in a Florida school district learned TERC’s Using Data process of collaborative inquiry. After their professional development sessions, these data teams returned to their schools to apply the process they had learned and dig deeper into their own data with colleagues. As this work progressed, one teacher expressed an epiphany: “I thought we were learning a quick way to 'fix' things. I now realize that this a process that takes time!”
Meaningful data analysis requires that data teams study multiple data sources to pinpoint student learning problems, find root causes for emerging problems, and launch a plan to tackle these problems. Data teams understand that there is not a 'quick fix' approach to understanding and closing learning gaps—this work takes time. Read More...
Data Tip #8 "Triangulate, Triangulate, Triangulate"
“When we looked at our state criterion-referenced tests (CRT) for sixth grade, life science was our weakest strand. We couldn’t believe that. We thought we had a pretty strong life science program. It wasn’t until we looked at our own local assessments and saw the same weakness that we became convinced that we had to take a closer look at what we were teaching and how.” (Love, Nancy, Ed., Using Data to Improve Learning for All, p.9)
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. Triangulation 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: Read More...
Data Tip #9 "Disaggregate Your Data to Make the Invisible Visible"
“Disaggregation is a practical, hands-on process that allows a school’s faculty to answer the two critical questions: ‘Effective at what? Effective for whom?’ It is not a problem-solving (process), but a problem-finding process.” (Lezotte and Jacoby, Sustainable School Reform, 1992)
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 is aggregated, or 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 showed that eighth-grade math scores steadily improved over three years. When the data team disaggregated those data, they discovered that the math scores for boys improved, while the scores for girls actually declined. Another school noticed increased enrollment in their after-school science club. However, disaggregated data indicated that minority students, even those in advanced classes, weren’t participating.
Here are some examples of questions that disaggregated data can help to answer: Read More...
To learn more about TERC's Using Data professional development, please fill out a contact us form, or call us at 774 993-2005.