Many schools are currently moving towards a more data-driven instructional environment as a way to better monitor progress and provide for student instructional needs. Although many teachers do not realize it, most are already leveraging the principles behind data-driven instruction on a daily basis. For example, each time a teacher modifies a lesson on the spot based on student body language or previous answers, they are using data-driven instruction. When a teacher provides post-test review based on student performance, they are also making data-driven decisions.
The process of data-driven instruction can easily be understood as a set of three steps.
1. Get Specific
- Use lesson objectives to specifically determine what you want to measure.
- Show examples of what mastery looks like as a point of reference.
2. Collect and Analyze Data
- Prepare and administer an exit ticket at the end of the lesson. Exit tickets, so called because they are often handed in as students leave the classroom, include a couple of questions and are an opportunity for students to prove they know/understand the skill or topic you are measuring. Once collected and checked, sort tickets based on demonstrated mastery and record.
- Observation is one of the best methods of data collection. To keep observations organized create an observation collection sheet. List student names vertically and the specific skills of focus horizontally. Use this sheet to indicate mastery and notes as you observe students working.
- No matter what data collection method you choose (and there are many) the key is to not only make sure you are providing students with an opportunity to show what they know, but that you are also prepared to record notes of their performance.
3. Plan with the Data in Mind
- Address demonstrated class needs with short activities and lessons at the beginning of the following class.
- Use recorded data to create groups for small group instruction.
- Modify homework to provide students with opportunities for additional practice.
By following these three steps it is possible for a teacher of any subject to bring more data-driven instruction into their classroom. Given the nature of data-driven instruction, you are never too late to start.