Whether our goals are on skill acquisition or reducing challenging behaviors, data collection is required to drive instruction decisions. In the business world, they have a special term for this: “Data-driven decision making” or DDDM. It requires that all decisions are backed up by hard data rather than making decisions that are based on intuition or feelings. Every industry today aims to be data-driven, so why should educating children be any different?
Data for special education classrooms is more than standardized test scores. In applied behavior analysis, data is used as the driving factor for all decisions on intervention. For data to be useful, it must be meaningful. To make data collection systems meaningful, think about what questions you want to answer about what and how your students are learning. When has a goal been mastered? How long did it take a student to learn a new concept, target, or skill? When should you change instruction because sufficient progress is not being made? Is a newly learned skill maintaining its fluency across time? What are my students’ strengths and weaknesses? What is a student’s baseline for a particular skill?
Data collection can be used for skills and behaviors we want to increase as well as behaviors we want to decrease. For data to be useful, it also must be accurate, valid, and reliable. By keeping useful data, we can look at what types of teaching strategies work best for a student by monitoring progress as well as factors that influence challenging behavior by understanding patterns.
Behavior is measured continuously by direct observation using count, rate/frequency, celeration, duration, response latency, and interresponse time. Direct measurement can compare environmental variables for the acquisition, maintenance, and generalization of socially significant skills and behaviors.
References:
Cooper, J. O., Heron, T. E., & Heward, W. L. (2007). Applied behavior analysis (2nd ed.). Columbus, OH: Merrill
Prentice Hall.