By Tom Many, EdD
“Whatever method you choose to organize your data, it needs to be done with intent and purpose.” – Willis (2016)
Analyzing data is an important responsibility of teams in a PLC. Lipton and Wellman (2012) note that, “High performing teams systematically collect and use data to drive cycles of problem solving, planning, action, and reflection to both improve their own collaborative practices and improve instruction that makes a difference in student learning.” (p. 3)
To collect and use data well, teachers often seek out advice regarding the best ways to organize data from common assessments or samples of student work. It is important to get this right because data that is confusing, convoluted or overly complicated contributes to the kind of informational “clutter” that can overwhelm teachers and lead to frustration on the part of collaborative teams.
According to Owen Willis, “Data clutter can prevent teams, schools and districts from fully accessing the student data they have spent countless hours and dollars collecting.” More importantly Willis notes that, “It [data clutter] can also waste a significant amount of teacher time that could be better spent planning or delivering instruction.” Instead of distributing complicated spreadsheets and cluttered printouts that erode a team’s commitment to using data, Lipton and Wellman (2012) advocate for, “well-crafted data displays” that help “clarify and communicate often complex or abstract information.” (p. 65) They point out that a thoughtful and well-designed approach to organizing data can, “make different data types more accessible to group work.” (p. 66)
“Good assessments provide a tremendous amount of raw data, but great analysis is impossible unless that data is recorded in a readily useable form.” – Paul Bambrick-Santoyo (2010, p. 41)
Data conversations are structured group conversations that help teachers understand, develop, and work with their data through a thoughtful, reflective process. The easiest way to eliminate the possibility that “clutter” will negatively impact the effectiveness of data conversations is to adopt a standardized approach to the way results are organized and displayed.
Kim Bailey and Chris Jakicic (2012) have developed practical ways of organizing data that help unclutter data conversations. (p. 112-114) Some teams have also found that frameworks like Bailey’s TAADA (Turn it around, Arrange it, Analyze it, Discuss it, and Act on it) can be useful tools for organizing data. Each step of the TAADA process is described in the paragraphs below.
T – Turn it Around: Timeliness is critical to productive data conversations. Old data is stale data and nothing is worse than working with data that is past its ‘expiration date.’ There are many resources, both human and technological, that allow teams to access their data less than 48 hours after administering an assessment. It’s clear that to be a resource teachers use to drive instructional decisions, assessment data needs to be current and reflective of ongoing instruction; and this requires making a commitment to return data to teams in a timely manner.
A – Arrange it: Schools and districts can promote the regular use of data by creating systems that reduce or eliminate “clutter.” Data is most beneficial when it is arranged in ways that allow teams to look at individual student performance at the target level. Experience has shown while teams may approach this important task in different ways, the most effective way to arrange data is by target, by teacher, by student.
A – Analyze it: Teams engage in a two-step process to analyze data. The first step begins at the macro level with a big-picture overview of the data looking for trends and patterns. Teachers probe for answers to questions like, “Which were the highest and lowest performing targets?” and “Were there any common misconceptions between classes or different groups of students?”
In the second step of the analysis, teachers dig deeper and examine the data at the micro level seeking to understand how to improve teaching and learning. The team works to identify which individual students will require additional time support and which specific learning targets will need more attention. Teachers also reflect on their instructional practice and identify which instructional strategies or parts of the unit need to be retained, refined or replaced.
D – Discuss it: This step is the heart of productive data conversations; it is where teachers make meaning of their practice. By this point in the data conversation, the discussion has transitioned from problem finding to problem solving and the raw data has been converted into information that becomes the knowledge and wisdom teachers use to develop their action plans.
This step is also where teams benefit most from the use of protocols. The regular use of protocols creates a safe, judgement free environment where teachers can publicly discuss the data, reflect on the results, and make collective decisions about what needs to happen next in order to ensure high levels of learning for all.
A – Act on it: The final step is to take action on what the team has learned during the data conversation. Here teams focus on responding to questions three and four and intentionally leveraging their schoolwide and systematic pyramids of intervention. Teachers reach consensus on what needs to be done to ensure students master the essential outcomes for each unit and take action.
Data conversations provide teams with opportunities to make meaning of their practice and inform instructional decision making. The key to making the best use of data is to treat assessments as opportunities to learn. It is also important to reduce “clutter” because data that is well organized increases the chances teachers will engage in productive data conversations and learn more about their students, their teaching, and potential areas for improvement.
“Data provides hints, not answers. But when brought together with context and conversation, data can become actionable insights that translate into powerful changes for students.” – Leo Bialis-White, (2016)
The term ‘data conversation’ suggests that some level of dialogue or discussion occurs among colleagues in order to turn the results of common assessments or samples of student work into actions that improve student learning. As Bialis-White says, “It’s not about the data, it’s what you do with the data that matters.”
Dr. Tom Many is an author and consultant. His career in education spans more than 30 years.
References
Bailey, K. & Jakicic, C. (2012). Common Formative Assessment: A Toolkit for Professional Learning Communities at Work. Bloomington, IN: Solution Tree.
Bambrick-Santoyo, P. (2010). Driven by Data: A Practical Guide to Improve Instruction. San Francisco, CA: Jossey-Bass.
Bialis-White, L. (2016). Using Data Conversations to Accelerate Impact and Improve Outcomes. Downloaded June 24, 2020. https://www.gettingsmart.com/2016/01/using-data-conversations.
Lipton, L. & Wellman, B. (2012) Got Data? Now What? Bloomington, IN: Solution Tree.
Willis, Owen. (2016) 3 Steps to Organize Student Data – and Find Joy. EdSurge. Downloaded July 3, 2020. https://www.edsurge.com/news/2016-10-11-3-steps-to-organize-student-data.
TEPSA News, September/October 2020, Vol 77, No 5
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