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.
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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
Formative datacan 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
Summative datacan 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
Reflective datacan 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 theirProfessional 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.
