Response: How To Use Data - & How Not To Use It - In Schools
Robert Thornton asks:
"How can teachers demonstrate to their students and administrators that data is informing the decisions they make regarding their instruction?"
By this I mean how does a teacher go beyond a change in mindset and towards becoming a leader in changing the culture of a school by making it clear beyond doubt that data is indeed informing their decisions.
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 questions we'll consider today with commentaries from Nancy Fichtman Dana, Dr. Jenni Donohoo, Myron Dueck, Pete Hall, Andrew Miller, Jessica A. Hockett, Kristina J. Doubet, and Kimberly Long. You can listen to a ten-minute conversation I had with Jenni, Myron and Nancy on my BAM! Radio Show. You can also find a list of, and links to, previous shows here.
I've written elsewhere about my concerns that "data" in schools is often narrowly confined to standardized text results, and the dangers of being "data-driven" instead of being "data-informed." You can read more at The Best Resources Showing Why We Need To Be "Data-Informed" & Not "Data-Driven."
Response From Nancy Fichtman Dana
Nancy Fichtman Dana is Professor of Education in the School of Teaching and Learning at the University of Florida. Her research focuses on teacher and administrator professional development with a particular focus on practitioner inquiry. She has published 10 books and over 60 articles and book chapters on the topic:
In this era of high-stakes testing and accountability, data (particularly quantitative measures of student performance) are abundant in schools everywhere. While it is clear that data are critical to making informed, instructional decisions, an overabundance of data collected without specific purpose can easily leave teachers feeling overwhelmed, with data having little impact on instruction. To avoid drowning in the sea of data available in today's schools, teachers can position themselves as "students of their own teaching" by engaging in a powerful mechanism for professional development called teacher inquiry.
Teacher inquiry is defined as systematic, intentional study of one's own professional practice. Inquiring professionals continuously improve teaching by posing "wonderings" about their practice, collecting data to gain insights into those wonderings, analyzing the data along with reading relevant literature, making changes in practice based on new understandings developed during inquiry, and sharing their findings with others.
A wondering is a burning question an educator has about practice. Wonderings can be individual, such as "How will using role play and simulations increase my students' understandings of historical events?," or collective, such as "How do we create more culturally responsive teaching across all of the classrooms in our school and what happens to student achievement as a result of implementing culturally-responsive pedagogy?"
Once a question is formulated, data collection begins. While some of the most pervasive data in schools today include quantitative measures of student achievement such as performance on standardized tests, progress monitoring tools, grades, and other assessment measures, data collection strategies come in many additional "flavors" as well and may include the following:
- Field Notes
- Scripting dialogue and conversation
- Diagramming the classroom or a particular part of the classroom
- Noting what a student or group of students are doing at particular time intervals
- Recording what a teacher is saying
- Student Work
- Documents (Such as Lesson Plans, IEPs, etc.)
- Digital Pictures
- Reflective Journals
- Colleague Feedback
Given the complexity of teaching and learning, it is important for educators who engage in inquiry to collect multiple forms of data to gain insights into their wonderings.
Data analysis is an ongoing and critical component of the inquiry experience, defined as teachers' developing an understanding of data based on a close, careful, and critical examination of it overtime and subsequently, creating stories of teacher learning that are data-rich. Data-rich stories of professional learning are carefully crafted to provide sufficient evidence to warrant the claims teachers wish to make from their inquiries as well as the actions they plan to take in their practice as a result of their investigations.
Once data are analyzed, educators take action based on what they learned through the process and share their learning with others. Leading change can be accomplished by sharing results in such venues as faculty meetings, grade-level meetings, publications, blogging, and conferences, creating opportunity for teachers to demonstrate to their students and administrators that data is informing the decisions they make regarding instruction, and perhaps, even more importantly, creating the opportunity for teachers to learn with and from one another for the benefit of student learning by making meaning of data together.
Response From Jenni Donohoo
Jenni Donohoo is a Provincial Literacy Lead with EduGAINS (Curriculum and Assessment Policy Branch, Ministry of Education) inOntario, Canada. Jenni is also a Consultant for Corwin. She is the author of Collaborative Inquiry for Educators, a Corwin bestseller. Connect with Jenni on Twitter @jenni_donohoo:
"I used to think that it would be difficult to show evidence of my effectiveness as a teacher but now I know that student work is the best way to show my impact."
This statement was shared by Joan (pseudonym), an 8th grade teacher, after engaging in a collaborative inquiry cycle in which she and her colleagues sought answers to their questions related to classroom instruction and assessment.
Collaborative inquiry is a structure in which members of a professional learning community (PLC) come together to systematically examine their educational practices. During a collaborative inquiry cycle, teams identify student learning needs and develop a question about a particular link between professional practice and student results. Teams gather and collectively analyze data to assess the impact of their actions and determine next steps.
Joan first learned about collaborative inquiry while searching online for a high-quality professional learning design. The fact that through the process, Joan and her colleagues would determine solutions to issues related to the students in their classrooms appealed to Joan. She thought the inquiry cycle would enable her and her colleagues to share their expertise and that together, they could improve their practice.
Collectively analyzing student work was a big step for the collaborative inquiry team and it did not happen over night. Joan was the first to share her students' work and introduced a protocol for examining student work in order to ensure the team stayed focused and objective. This helped the team a) determine which strategies were working with which students and b) identify students who needed additional support and scaffolding. Teachers were able to identify unexpected directions students took in supporting opinions. It became clear to the team, beyond a doubt, that student evidence was needed to inform their decisions.
Below are suggestions to assist collaborative inquiry teams in examining student work.
- Begin with anonymous student work samples - perhaps from a colleague's class in another school (this colleague and the students should remain anonymous). Initially examining work that does not 'belong' to anyone in the group will help to build confidence and ease the transition to the more risky activity of sharing their students' work.
- Use protocols for examining student work. Protocols provide structures and guidelines for looking at and talking about student work. They are designed to help team members reflect on their practice as it relates to student learning and development.
- Select 3-5 students of interest and monitor their progress over time. There is no need to bring student evidence from an entire class. Teachers might select 3-5 students who are performing at different levels of achievement. Collaborative inquiry teams will find it more manageable (and equally informative) to monitor the progress of a few students.
The collective analysis of student data was a pivotal step in moving Joan's team forward as it helped them recognize the link between their actions (causes) and student results (effects). It helped the team refine their instruction and assess the results of implementing different instructional strategies. It also led to team members asking complex questions grounded in critical issues related to student learning. As a result, the collaborative inquiry team came to appreciate the importance of data-informed decision making.
If you would like to learn more about fostering teacher leadership through collaborative inquiry, the cycle is described in greater detail here.
Response From Myron Dueck
Myron Dueck is the author of Grading Smarter Not Harder: Assessment Strategies That Motivate Kids and Help Them Learn (ASCD, 2014). He is currently a vice-principal and teacher in School District 67 in British Columbia, Canada and previously taught in Manitoba and on the South Island of New Zealand. Dueck has presented his student-friendly assessment procedures at conferences worldwide:
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. Here are a few effective ways that data can inform future instructional decisions:
(1) The teacher analyzes each set of unit test results for her class and selects one or two outcomes that were associated with the greatest number of errors. She then uses the next class to tackle these outcomes from a different angle than previously tried. She then uses Google Docs, a short paper-based quiz, exit slips or some other assessment process to re-assess the entire class. The data from the re-assessment will give the teacher a few options:
- Adjust student scores in the grade book for that unit test.
- If changes were successful, incorporate the new strategy the next time that material is taught.
- Look for universal strategies that could transfer into the following unit(s) or to other classes.
(2) Analyze the summative exam or test results from one year to determine the teacher's 'improvement focus' for the following year. Share with students and faculty the changes that you are developing and why. This will send the message that even for the teacher, adaptability is a natural component of learning.
(3) Use exit slips on a regular basis to check that students have grasped the key objective of the day. By checking some or all of the slips, the teacher can return to a concept that was misunderstood by a number of students.
(4) I recently witnessed a teacher introduce a Google Docs unit reflection questionnaire for his students and by doing so he modeled that he is interested in continuous improvement and growth. Students use school computers or BYOD portals to take the questionnaire right after taking a unit test. The data from the online tool can give the teacher the following information:
- Students were able to rank all learning activities from the unit by level of enjoyment and/or engagement.
- Students rank the learning activities according to positive/negative impact on learning.
- Students can indicate with test components were most inviting, engaging or effective.
- Students can suggest what might improve their own experience in future units of study.
Reflection and the adjustment to clear and relevant data are as important in education as in any business environment. Teachers can take concrete steps to send the message to students, parents, colleagues and administration that data truly speaks, and that education is listening.
Response From Pete Hall
Pete Hall (@educationhall) is a veteran school administrator and professional development agent who has dedicated his career to supporting the improvement of our education systems. He is currently a faculty member with ASCD Professional Learning Services. Along with Alisa Simeral, he is a co-author of Teach, Reflect, Learn: Building Your Capacity for Success in the Classroom (ASCD):
As instructional leaders in our schools, it's our responsibility to set the standard for the use of data to drive our decision-making process. Are principals modeling data-digs during staff meetings? Are department chairs and team leaders using assessment data to create intervention groups during team meetings? If that message is communicated loudly and clearly through words AND actions throughout the school, then the stage is set for our teachers to do the same.
There are a couple of key questions that guide this work. Let's tackle them one at a time:
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" (I believe in my heart that the lesson was effective) rather than true assessments of student progress.
What 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?
How do we collect those data? 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.
What do we do with those data? We act on them! 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.
How can we institutionalize this sort of professional behavior? Name it. In staff meetings, team meetings, classroom dialogue, and other settings, call these actions "data-driven decisions," and reference the data in question. Model these data investigations in public forums, and encourage colleagues to bring their own classroom data to the table. When schools build a structure that encourages planning to include data-savvy language, they thrive. Here's an example of that language: "Because my end-of-week exit ticket data showed me that 67% of my students failed to grasp the concept of writing a thesis, we're going to spend more time next week emphasizing some strategies. I'm going to recruit the 33% who mastered it to facilitate the investigations at each table-group."
For a lot of people, the "hard" data are scary. Remember, they're our allies. Without data, we're just flying by the seats of our pants. With data, we are strategic, intentional, and more successful.
Response From Andrew Miller
Andrew Miller is on the faculty for the Buck Institute for Education and ASCD, and is a regular blogger with ASCD and Edutopia. He is the author of Freedom to Fail: How do I foster risk-taking and innovation in my classroom? (ASCD, 2015). Follow him on Twitter @betamiller:
The "data-driven" classroom has become oversimplified. Where is the oversimplification? In the word "data." Data does bring its negative baggage. When we think of student data, we 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. We need to look at data on how students feel secure and safe in our schools. We should use surveys and audits to collect data on how students feel at our school and use that to drive change and reflection in the culture of the whole school. We should also collect data on student interest and engagement. If we know the interests and passions of our students, we can use this data to drive our instruction and how to provide assessment choices that truly engage our students.
In addition, we need to include "on the spot" formative assessments as key components of student data. An exit ticket can show much more that a standardized quiz. A one-on-one conversation can be much more powerful for a teacher to reflect upon than a giant list of student data. Graphic organizers, mini-presentations, whiteboards, "clickers" are all examples of collecting data. These formative assessments allow us to make decisions around whole group, small group, and individualized instruction. They allow us to differentiate hour-to-hour, day-today. Larger collections of student data can help us find global errors in terms of classroom, grade level, and even school learning, but regular formative assessment of our students is important data than can immediately make use better teachers in the next part of the lesson or even the next day. Teachers need to be transparent with this daily data and demonstrate how that is making them reflect and make the best choices for their students on a regular basis.
Response From Jessica A. Hockett and Kristina J. Doubet
Jessica A. Hockett, Ph.D. and Kristina J. Doubet, Ph.D. are the co-authors of Differentiation in Middle and High School: Strategies to Engage All Learners (ASCD). They are also members of the ASCD Faculty and consultants who work with practicing teachers of all grade levels - nationally and abroad - on the topics of curriculum, assessment, and differentiated instruction. Follow them on Twitter @DIY_Diff and on Instagram @d.i.y_di:
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.
Response From Kimberly Long
Kimberly Long teaches English language/arts at Daniel Wright Junior High School in Lincolnshire, Ill. She promotes teacher leadership as a member of the Illinois Education Association, Center for Teaching Quality's Ill. Teacher Solutions Team, and the CTQ Collaboratory. Connect with Kimberly on Twitter @LongEDU, or her blog Hallway Murmurs:
In order to convince administrators and students that data is needed to inform decisions within the classroom, you must be able to demonstrate its purpose and value. Transforming the minds of those resistant to adopt a practice is no easy feat. There are many great books out there on this very topic: To Sell Is Human: The Surprising Truth About Moving Others by Daniel H. Pink, Switch: How to Change Things When Change is Hard by Chip and Dan Heath, and The Human Side of School Change: Reform, Resistance, and the Real-Life Problems of Innovation by Robert Evans.
These books offer excellent guidance on change, but when it comes to change we must first start with ourselves. Model change by focusing on your own classroom, and your own practice as a way to set an example for others. By allowing others to learn from your experiences, the discussion surrounding data and its implementation will occur naturally. I've found one of the best ways to bring value to data is by sharing it with the people it impacts the most - students. Recently, I wrote an article on how to empower students using data. As educators we shouldn't be afraid to share the data with our students. They should know how they're doing in our class at all times, their strengths and weaknesses, and be able to recognize their own successes. John Hatti's push for visible learning and feedback, and Robert Marzano's research on student's tracking data, support data sharing. Teach your students how to recognize they are learning.
Start small and stay focused on one specific aspect of your curriculum. You may choose to focus on one specific standard, or one unit. Then, pick a data collection technique to have the students use. This may include pre/post tests, quiz or rubric scores, and/or observational data. Then comes the important part, monitoring the progress and change over time. Have your students update their data using graphs, complete checklists reflecting mastery, and/or engage in reflective activities to help students understand the significance of the data. As a result of sharing data with the very people it impacts the most, students will better understand the "why" behind the curriculum presented to them. When you make data an integral part of the classroom experience, a transformational change is possible for students, teachers, and administrators.
Thanks to Jenni, Myron, Nancy, Pete, Andrew, Jessica, Kristina and Kimberly for their contributions!
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