What is Data-Driven Instruction?
Data-driven instruction, at its most simplistic, is when teachers use data to drive their classroom instruction. More specifically, when a teacher uses data-driven instruction (or DDI), that teacher regularly gathers and analyzes data from both formative and summative assessments to glean insights into how well their students are understanding and mastering the material. That teacher then uses the insights that the data provides to adjust instructional methods and materials and, therefore, better provide for students’ instructional needs.
Why is Data Driven Instruction Important?
Data-driven instruction is important for several reasons. Most significantly, instruction that is driven by data is tailored to students’ needs instead of adhering to the set scope and sequence of instruction. Teachers who allow data (instead of scope and sequence) to drive their instruction are still able to meet their state’s standards but can do so at a rhythm that more effectively allows for students’ mastery learning.
Second, student data allows teachers, administrators, students, and parents to have an objective point of reference for understanding how a student is performing. Often, good data helps highlight not only specific students who are struggling but the specific content areas those students are struggling with. Many times, teachers form Professional Learning Communities around struggling students. Student data helps these PLCs hone in on the core issues, and alongside the student and sometimes the parent, they create SMART goals as intervention strategies. Data collected in follow-up assessments allows the PLCs to track the success rates of their interventions.
Watch “Driving Change with Data – How Jaffrey-Rindge Cooperative School District Launched PLCs to Focus on Student Growth“ to hear how a school administrator leverages data to improve student outcomes.
Third, when the correct data from students is collected and analyzed well, it can highlight a teacher’s efficacy in the classroom. Excellent teachers see data as an essential element in professional growth. They use a variety of data from student learning to analyze their instructional strategies and approaches and modify what they do in the classroom based on their students’ needs to continue to hone their craft.
Finally, excellent administrators use student and teacher data to find ways to develop and support teachers and look for patterns where their school needs additional support. For example, if data demonstrates that a certain grade level constantly scores low, that team may need additional training and development. If data reveals that students across a grade level always fail in one specific area on a test, that area of the curriculum may need to be revisited.
The 5 Elements of Data-Driven Instruction
1. Reliable baseline data
First, DDI must have reliable baseline data. Teachers and administrators must understand where students are starting from before they can assess how students have grown. It’s essential that the type of data measured remains consistent over the monitored developmental period and that the same types of data are regularly analyzed. Data points should be easily measured. Findings will be inconsistent if teachers and administrators do not have a reliable baseline to measure the trajectory of their future data findings.
2. SMART goal setting
The second element of DDI is a SMART goal based on the data discovered. Once a teacher has uncovered a domain that needs better explanation or a student who needs more practice to achieve mastery, the teacher will create a SMART goal (Specific, Measurable, Attainable, Relevant, and can be achieved in a reasonable amount of Time) around the pain point to increase comprehension and achievement for the student or class.
3. Consistent progress monitoring
After implementing the SMART goal, the teacher will then, in the third element of using data-driven instruction in a classroom, continuously use the same types of formative, summative, and reflective assessments to measure whether or not the goal has had a positive, negative, or neutral effect.
4. Professional Learning Communities
As part of this analytical process, the teacher will lean into the fourth element of data-driven instruction: Professional Learning Communities. For example, if the data reveals a struggling middle school student, all of the teachers who have that student in their classrooms will regularly meet to discuss the intervention and SMART goals they are using to help that student improve and then provide each other with feedback.
5. Targeted interventions
The fifth DDI element is interwoven throughout the third and fourth elements: targeted interventions. As teachers implement formative and summative assessments and process their findings with PLCs, they create targeted interventions based on how successfully or unsuccessfully their SMART goals are performing. All of their choices are driven by the data from the strategic assessment they’ve chosen to help measure the progress.
What is Data Collection in Education?
While standardized test scores can be one helpful data point, effective data-driven instruction is derived from multiple, varied sources of data that surround students and teachers in a classroom. This data can be formative, summative, and even a reflection of students’ lives that impact their classroom behavior and engagement.
Formative data can be anything from a teacher walking around their classroom observing how successful students are in discussing the material to exit tickets requiring students to answer a key question to indicate an understanding of a topic before they leave the classroom.
Summative data can be gathered from standardized test scores, district assessments, test scores from specific subjects, and scores from non-traditional cumulative assessments like semester projects or oral presentations.
Reflective data can be anything from tracking student tardies and absences to tracking the overall pass or fail rates of students across several years. Additionally, reflective data can track how students perform – for example, if they perform better when a new unit is introduced at the end of the week instead of at the beginning, or if they take a test before the weekend instead of after.
Most importantly, data collection in education is not only about the collection. Schools that use data-driven instruction well have processes set up to focus on collecting the right kind of data for their school goals. Then, they have regular times for teachers and administrators to collaborate on analyzing the data. They ask:
- What does this data reveal about students or curriculum?
- What are their strengths and weaknesses?
- How do we continue to emphasize the strengths and address the weaknesses?
Then, they implement strategies and collect additional data to discover if their goals are driving instruction forward or if they need to realign to better serve students.
How do teachers use data to drive and improve instruction?
Data gives feedback to know where a student is in their learning process. Teachers analyze data from a variety of formative and summative assessments to accurately understand what a specific student, group of students, or even an entire classroom needs to achieve mastery of a specific topic or subject.
To use data to drive instruction, teachers must do two things. First, they must understand the requirements of their grade level or subject’s standards. This will allow them to clearly articulate what knowledge or skills their instruction will equip students to master. Second, they must decide on what data they will collect during their teaching unit. It must be data that can be easily, regularly, and consistently tracked throughout the unit. Many teachers make the mistake of trying to collect too much data then don’t know what to do with it or how to analyze it. Teachers must choose quality data over quantity of data.
Once teachers start to collect the data, they should analyze it to target what is happening in their classroom:
- Where are students doing well?
- Where are they falling behind?
- What areas are critical to address?
These categories may apply to an entire classroom or to only one or two students.
As they start to analyze the data, teachers need to collaborate with their Professional Learning Communities. They can share data points, ideas for intervention, and initial progress. This collaboration allows for the most effective instructional strategies to be implemented to target critical issues. Together, they can directly support student growth.
When implementing new strategies based on data in their classrooms, teachers can make a variety of changes. These changes can be small, like adjusting the pacing and depth of their instructional lessons or big, like differentiating instruction in small groups to target specific student needs. The teacher’s goal is always to provide each student with the opportunity to practice the instructional content within their zone of proximal development. Data most accurately helps a teacher understand where each student’s zone is.
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How do you analyze evidence of student learning using data?
There are multiple ways to analyze evidence of student learning using data. When regularly incorporating data into instruction, reflecting and analyzing student growth and learning should be an ongoing part of a teacher’s routine.
Teachers should choose up to three data points to consistently analyze as they monitor one or two learning goals for their students. Data points must be objective and easy to chart or graph. This is why most teachers look to numerical scores to track data.
Then, teachers must choose a starting point for each of their data points at the beginning of a domain, unit, or term. As they instruct, they should be gathering data for each data point through formative assessments like exit tickets.
Additionally, teachers should regularly meet with their Professional Learning Communities across subject or grade levels. Those PLC times can be used to discuss struggling students or instructional approaches to a concept that students across grade levels are not mastering.
Finally, a teacher can give a summative assessment to discover if students grew from their initial starting data points. Furthermore, teachers can use data from summative assessments to compare students to one another. For example, if everyone in the class succeeded on a difficult depth-of-knowledge question but failed a much easier question, the teacher will know that topic must be re-taught before moving on to the next unit or domain.