Data, data, data -- that word is everywhere in education these days.
What is it, how can it be used effectively, and how can it be misused are important questions for teachers to consider.
What is it, how can it be used effectively, and how can it be misused are important questions for teachers to consider.
What are data?
It could be argued that people in education could learn a few things from the business community. One of these lessons would be to make timely adjustments to relevant data. Successful businesses adapt quickly and effectively to changes in consumer demands and market fluctuations. If a business decides that it will ignore clear signs that the landscape has changed, it will likely not survive. Corporate victims of change include Blackberry, Blockbuster and Beta - need we go through the whole alphabet?Teachers can use data to determine the effectiveness of instruction or the impacts of a change, while embedding a philosophy of adaptability into their practice.
Why is it important to use data?
Data deliver to us the facts, impartially, honestly, and sometimes harshly. If we don't follow the facts, we're following hunches - and we use "cardiac assessment" (e.g., "I believe in my heart that the lesson was effective") rather than true assessments of student progress. Without data, we're often left doing things based on hunches. Although hunches are sometimes right, they are sometimes wrong, too--which can lead to actions that may only treat a symptom of a greater underlying cause as to why our students (or a sub-population of our students) are not performing as well as we would like. To get a more informed and complete view, and one that has the highest chance of being accurate, we need to look at data.
What are two common sources of data for teachers that can be used effectively?
There are at least two sources of data that can influence a teacher's instructional decision-making: (1) results from standardized assessments such as national, state, or district-level tests or professionally-designed benchmark assessments, and (2) results from classroom-based assessments designed by individual or groups of teachers.
Standardized assessments can provide a valuable starting point for planning and evaluating student growth, if the results are available in a timely manner. Teachers might notice general patterns of strength and weaknesses in particular skill areas at or across grade levels or among their own students. Such data can gives teachers clues about where students might need more support or more challenge. Because teachers usually aren't privy to questions that were asked, how students answered those questions, or why students responded the way they did, such assessments have limited potential for giving teachers insights about what individual or groups of students need to move forward in their learning (a notable exception would be individually-administered assessments such as diagnostic tools for reading comprehension). The further away from the time the assessment was given, the less relevant the results become. For example, if students performed poorly on an benchmark assessment administered in September, those results are too "old" to consult for designing lessons in December.
Classroom-level pre- and formative assessments are a far more useful and important source of data that can actually inform instruction. Designed well, student responses to prompts on pre- and formative assessments can give teachers true insights about what students are and aren't grasping and why.
These assessments should be aligned with specific unit or lesson goals and be given and analyzed in time for the teacher to actually use the results. Teachers should be able to point to patterns in ongoing assessment data to "explain" (to students, to administrators, or to themselves) the instructional decisions that followed. For example, after examining a set of exit slips, a teacher might explain to students, "We were all in different places with our learning yesterday, so I've placed you in groups so that you can concentrate on your particular learning needs before we move on together." The teacher might make a note in his/her lesson plans describing the patterns noted and how they were addressed by differentiated instruction.
Similarly, after administering a pre-assessment, a teacher could explain to the class and to colleagues that she needed to review - or skip - a certain concept with the whole class, which changed how she originally planned to begin the unit.
This doesn't mean standardized assessment data is useless, or that classroom assessment should replace or compete with it. The ideal relationship between the two is that they inform one another, with teachers using what they see from standardized tests to focus pre- and formative assessments on uncovering what's really going on in students' minds, and responding in kind with instruction that targets those learning needs.
Standardized assessments can provide a valuable starting point for planning and evaluating student growth, if the results are available in a timely manner. Teachers might notice general patterns of strength and weaknesses in particular skill areas at or across grade levels or among their own students. Such data can gives teachers clues about where students might need more support or more challenge. Because teachers usually aren't privy to questions that were asked, how students answered those questions, or why students responded the way they did, such assessments have limited potential for giving teachers insights about what individual or groups of students need to move forward in their learning (a notable exception would be individually-administered assessments such as diagnostic tools for reading comprehension). The further away from the time the assessment was given, the less relevant the results become. For example, if students performed poorly on an benchmark assessment administered in September, those results are too "old" to consult for designing lessons in December.
Classroom-level pre- and formative assessments are a far more useful and important source of data that can actually inform instruction. Designed well, student responses to prompts on pre- and formative assessments can give teachers true insights about what students are and aren't grasping and why.
These assessments should be aligned with specific unit or lesson goals and be given and analyzed in time for the teacher to actually use the results. Teachers should be able to point to patterns in ongoing assessment data to "explain" (to students, to administrators, or to themselves) the instructional decisions that followed. For example, after examining a set of exit slips, a teacher might explain to students, "We were all in different places with our learning yesterday, so I've placed you in groups so that you can concentrate on your particular learning needs before we move on together." The teacher might make a note in his/her lesson plans describing the patterns noted and how they were addressed by differentiated instruction.
Similarly, after administering a pre-assessment, a teacher could explain to the class and to colleagues that she needed to review - or skip - a certain concept with the whole class, which changed how she originally planned to begin the unit.
This doesn't mean standardized assessment data is useless, or that classroom assessment should replace or compete with it. The ideal relationship between the two is that they inform one another, with teachers using what they see from standardized tests to focus pre- and formative assessments on uncovering what's really going on in students' minds, and responding in kind with instruction that targets those learning needs.
Which data actually drive decision-making processes in the classroom?
Quite plainly, ongoing formative assessments should be driving our in-class decisions. Are students getting it? Which students are? Which ones aren't? Which learning objectives has each student mastered? To what degree?
If we have identified the outcomes of a course, a unit, a lesson, or a lesson segment, then we should be able to determine what success looks like for our students. Then we back up and measure the bits and pieces that contribute to that larger learning. We might use exit tickets, entry tasks, quizzes, pre-assessments, impromptu performances, labs, writing samples, or even simple thumbs-up or the looks on students' faces to tell us if they're learning or struggling.
When students struggle, that means they need our help: more time, alternative instruction, deeper modeling, additional practice, or some other form of intervention. When students are mastering the material, that means they need our help, too: adding extensions, deepening their thinking, encouraging them to support their peers, or applying their knowledge in new contexts.
If we have identified the outcomes of a course, a unit, a lesson, or a lesson segment, then we should be able to determine what success looks like for our students. Then we back up and measure the bits and pieces that contribute to that larger learning. We might use exit tickets, entry tasks, quizzes, pre-assessments, impromptu performances, labs, writing samples, or even simple thumbs-up or the looks on students' faces to tell us if they're learning or struggling.
When students struggle, that means they need our help: more time, alternative instruction, deeper modeling, additional practice, or some other form of intervention. When students are mastering the material, that means they need our help, too: adding extensions, deepening their thinking, encouraging them to support their peers, or applying their knowledge in new contexts.
Why should teachers and PLCs "data informed" or "evidence informed" and not "data driven?"
Everything that matters cannot be measured. Students cannot simply be reduced to a singular data point. Human beings are far more complex than that. When we think of student data, we sometimes go immediately standardized tests. We know this doesn't tell us a story of the whole child, and we need to redefine "data" to include aspects of the whole child.
Data-driven instruction obliterates the crucial fact that to be effective, educators have to use many different kinds of information to think about what they are doing in classrooms. While statistics can be immensely useful, they do not automatically point to which instructional approaches will work best with the diverse learners that make up a school’s classes, or a nation’s schools. One child may struggle with under-performance because she has difficulties with reading, a second because he has a turbulent home life, and and a third because she is a recent immigrant learning English as a second language. Faced with such diversity, teachers and educational leaders have to be intelligently informed by evidence, not blindly driven by it to teach a certain way.
Our instructional choices should be based on all kinds of evidence and experience, processed together in professional learning communities that help us identify common problems, swap ideas and strategies, and develop and deploy our own school-based assessment instruments. Mindful teaching should to be evidence-informed, not data-driven. But remember that thoughtful, strategic, and good uses of data have a key role in both effective teaching and student learning.
Data-driven instruction obliterates the crucial fact that to be effective, educators have to use many different kinds of information to think about what they are doing in classrooms. While statistics can be immensely useful, they do not automatically point to which instructional approaches will work best with the diverse learners that make up a school’s classes, or a nation’s schools. One child may struggle with under-performance because she has difficulties with reading, a second because he has a turbulent home life, and and a third because she is a recent immigrant learning English as a second language. Faced with such diversity, teachers and educational leaders have to be intelligently informed by evidence, not blindly driven by it to teach a certain way.
Our instructional choices should be based on all kinds of evidence and experience, processed together in professional learning communities that help us identify common problems, swap ideas and strategies, and develop and deploy our own school-based assessment instruments. Mindful teaching should to be evidence-informed, not data-driven. But remember that thoughtful, strategic, and good uses of data have a key role in both effective teaching and student learning.
Examples of ways teachers can strategically use data: