Confessions of a Data Train Wreck...
I'm going to say something here that I've said dozens of times over the past few years even though no one seems to take me seriously:
I'm completely useless when it comes to making "data driven decisions."
And that's putting it gently! I literally don't even know where to begin, even though administrators and colleagues regularly dismiss my assessment of my abilities in this area. "Oh, Bill," they'll say, "You're just being humble. As a master teacher, you use data without even knowing it."
My response is always the same----As a master teacher, I'm also fully aware of my own personal strengths and weaknesses! I don't know what kind of data to collect or what kinds of actions to take once I've collected it. You could drop a pile of data in my lap and I wouldn't know what to look for? Analysis sounds good, but is superficial whenever I'm at the data table.
That's why I was completely geeked by the first session that I attended here in Denver. Nancy Love of Research for Better Teaching facilitated a session titled Using Data and Getting Results through Collaborative Inquiry. In it, she outlined a four phase process that teachers could use during data driven dialogue.
In the first phase of data conversations, teams make predictions about what they believe that they will find when looking at the data. This predictive phase serves to "surface experiences, possibilities and expectations." Essentially, predictions engage participants in the process. Much like having students make predictions about what will come next in a story to provide motivation for continued reading, predictions in data conversations keep teachers motivated and engaged.
Perhaps more importantly, however, predictions also bring to the surface assumptions that teachers and teams have about student performance---assumptions that often drive the actions and decisions of teams. By surfacing assumptions early in the process, teams can either verify or revise their long held beliefs.
After predictions have been made, teams move into Phase 2, which Love calls "Go Visual." In the go visual stage of a data conversation, teachers are encouraged to make simple graphs of the data that has been collected. This might include line graphs showing progress made by various student populations over time or bar graphs showing mastery levels of different objectives.
The key to going visual, Love would argue, is allowing teachers to make large, colorful representations of the data to be analyzed---which can then be hung up and looked at from a distance. While it may seem simple, this physical separation of the data from the group helps to mentally separate people from practices. Conversations focus on "that data over there" rather than on individuals----helping to increase the level of safety felt by participants.
Phase 3---Observations---ask teachers to list measurable conclusions that can be drawn from the data available. Teams are asked to consider questions like "What are some patterns or trends that are emerging," and "What seems to be surprising or unexpected." The key to stage three is resisting the temptation to attach meaning to the observations. Words like "because" and "why" are forbidden in phase three!
In Phase 4, teachers generate a list of possible explanations for the observations that they have made as a team. These questions become a "To-Do" list for teams, serving as starting points for continued research and study about instruction. A team's goal is to look for answers to their own questions----which are based on observations drawn from data.
During our session, I found myself excited about data for the first time. You see, I finally have a structure that I can grab on to and use when talking about data with my learning team. No longer are "data driven decisions" an unsolvable mystery.
I also found myself frustrated because my learning team has been stalled by our inability to use data effectively for years now. "How would our instruction have changed had we used a structured process all along," I thought, "Would we have reached more students? Experienced less frustration? Whose job was it to show us what 'data-driven' looks like?"
I think what I'm learning is that we have a false assumption that teachers will automatically know what to do with data---and this assumption is limiting the impact that collaborative teams can have on student achievement. Until we build the capacity of our teams to be critical manipulators of data, frustration is likely to be the only by-product of data driven conversations.
What kinds of good things are being done in your schools and districts to support the effective use of data by teachers and teams?
I'm going to say something here that I've said dozens of times over the past few years even though no one seems to take me seriously:
I'm completely useless when it comes to making "data driven decisions."
And that's putting it gently! I literally don't even know where to begin, even though administrators and colleagues regularly dismiss my assessment of my abilities in this area. "Oh, Bill," they'll say, "You're just being humble. As a master teacher, you use data without even knowing it."
My response is always the same----As a master teacher, I'm also fully aware of my own personal strengths and weaknesses! I don't know what kind of data to collect or what kinds of actions to take once I've collected it. You could drop a pile of data in my lap and I wouldn't know what to look for? Analysis sounds good, but is superficial whenever I'm at the data table.
That's why I was completely geeked by the first session that I attended here in Denver. Nancy Love of Research for Better Teaching facilitated a session titled Using Data and Getting Results through Collaborative Inquiry. In it, she outlined a four phase process that teachers could use during data driven dialogue.
In the first phase of data conversations, teams make predictions about what they believe that they will find when looking at the data. This predictive phase serves to "surface experiences, possibilities and expectations." Essentially, predictions engage participants in the process. Much like having students make predictions about what will come next in a story to provide motivation for continued reading, predictions in data conversations keep teachers motivated and engaged.
Perhaps more importantly, however, predictions also bring to the surface assumptions that teachers and teams have about student performance---assumptions that often drive the actions and decisions of teams. By surfacing assumptions early in the process, teams can either verify or revise their long held beliefs.
After predictions have been made, teams move into Phase 2, which Love calls "Go Visual." In the go visual stage of a data conversation, teachers are encouraged to make simple graphs of the data that has been collected. This might include line graphs showing progress made by various student populations over time or bar graphs showing mastery levels of different objectives.
The key to going visual, Love would argue, is allowing teachers to make large, colorful representations of the data to be analyzed---which can then be hung up and looked at from a distance. While it may seem simple, this physical separation of the data from the group helps to mentally separate people from practices. Conversations focus on "that data over there" rather than on individuals----helping to increase the level of safety felt by participants.
Phase 3---Observations---ask teachers to list measurable conclusions that can be drawn from the data available. Teams are asked to consider questions like "What are some patterns or trends that are emerging," and "What seems to be surprising or unexpected." The key to stage three is resisting the temptation to attach meaning to the observations. Words like "because" and "why" are forbidden in phase three!
In Phase 4, teachers generate a list of possible explanations for the observations that they have made as a team. These questions become a "To-Do" list for teams, serving as starting points for continued research and study about instruction. A team's goal is to look for answers to their own questions----which are based on observations drawn from data.
During our session, I found myself excited about data for the first time. You see, I finally have a structure that I can grab on to and use when talking about data with my learning team. No longer are "data driven decisions" an unsolvable mystery.
I also found myself frustrated because my learning team has been stalled by our inability to use data effectively for years now. "How would our instruction have changed had we used a structured process all along," I thought, "Would we have reached more students? Experienced less frustration? Whose job was it to show us what 'data-driven' looks like?"
I think what I'm learning is that we have a false assumption that teachers will automatically know what to do with data---and this assumption is limiting the impact that collaborative teams can have on student achievement. Until we build the capacity of our teams to be critical manipulators of data, frustration is likely to be the only by-product of data driven conversations.
What kinds of good things are being done in your schools and districts to support the effective use of data by teachers and teams?
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