This post is a continuation of a series exploring instruction through reading individualization. Check out the other posts as well!
Mastering Student Data for Differentiation
Of course, individualization cannot happen unless data can be easily amassed and collated. The first task is rather easy; the second is not. Increasingly, technology is assisting us in understanding student strengths and weaknesses, but we must continue to recognize the need for effective data in making individualization possible.
It works like this:
- The student is assessed using a standards-aligned diagnostic test.
- The test produces data that shows the strengths and weaknesses of the student based on each content standard.
- The teacher reflects on that data to make instructional decisions for the student.
- The teacher administers individualization activities and experiences to the student based on the data.
- Student work and/or repeated diagnostic testing provide data updates.
- The teacher modifies student learning activities based on updated data.
In theory, this will lead to a student addressing weaknesses in step 4 and that work will produce growth. Some difficulties may arise, though. Step 3 may be difficult because the data may be overwhelming. If a teacher cannot quickly and easily understand student strengths and weaknesses based on the data, he or she may give up on careful individualization. So, systems must be in place to produce data that teachers can easily understand and use. Step 4 may become overwhelming as well if the teacher feels he or she must create a vast amount of materials to meet the needs of individualization. Step 5 may include such frequent diagnostic testing that students become exhausted.
Two solutions to the data burden present themselves: professional development and automation. Teachers may not know how to access data through technology, interpret that data, or connect that data to relevant learning activities. Professional development activities that provide instruction in the management of data and best practices in the use of data are essential to implementing differentiation through leveled readings.
Automation is increasingly possible and incredibly attractive. Digital resources that automatically complete any of the above steps are extraordinarily useful. For instance, if a resource serves content for students based on diagnostic reports, this can help a teacher with step 4. Or, if a resource aligns diagnostic data to standards, that can ease the burden of step 3. In evaluating and shopping for individualization resources, attention to the level of automation is key for successful data management, and ultimately of implementation.