Using Data-Driven Instructional Cycles to Transform Schools

What is DDIC?

An effective academic relationship is a relationship whereby both teachers and students are aware of the specific concepts and skills that are being learned, those that will be learned, and those that need to be reinforced. These academic relationships depend on educators knowing the DNA of their students; that is, knowing their Dreams, their Needs, and their Abilities. To this end, ATC recommends that schools integrate Data-Driven Instructional Cycles (DDIC) into their existing framework for PLC’s and RTI-A. Data-Driven Instructional Cycles synthesize professional collaboration and classroom learning experiences into an effective and efficient process for studying standards, utilizing assessments, analyzing student work, and creating/delivering targeted lessons for every student every day. This process is based on the realization that the most valuable teacher learning experiences are those that they get from their colleagues during the school day in a job-embedded, differentiated, and “just-in-time” way. At least once per unit of study, teacher teams collaborate on the four core components of this teaching and learning cycle. These core components are outlined below in greater detail:

Standards Study Sessions

During these sessions, data-driven teacher teams utilize a systematic process for studying the prerequisite, requisite, and future learning required for mastery of the state standards. Teachers analyze the skills and concepts embedded in the priority and supporting standards for an upcoming unit of study and discuss the instructional implications of those standards. This is vertical alignment in “real-time”.

Assessment Literacy Sessions

During these sessions, data-driven teacher teams utilize a systematic process for becoming educated consumers and creators of formative assessments. Teachers determine the student success criteria, scoring guides, and learning progressions that ensure student mastery of the state standards. They also develop selected response, short-constructed response, and extended response items aligned to the state standards and the progressions of learning.

Data-Analysis Sessions

During these sessions, data-driven teacher teams utilize a systematic process while reviewing student work to determine student strengths, student misconceptions, and the research-based instructional strategies that will best address those misconceptions. Their selected research-based instructional strategies become the foundation for the students’ learning experiences throughout the unit of study.

Lesson Planning and Delivery Sessions

During these sessions, data-driven teacher teams consolidate the results from their standards study, assessment planning, and data analysis sessions to develop and delivery rigorous and relevant learning experiences that meet the differentiated needs of their student populations.