Data driven differentiated Instruction by Gloria Y. Niles is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Differentiated instruction is a data-driven cycle used by educators to ensure that they are reaching each learner. In every learning community, learners enter with different degrees of background knowledge, different prior experience with content, and different strengths, challenges and abilities. Formative assessments are commonly used by educators to measure the impact of their instructional strategies. The data-driven differentiated instruction framework is a process of analyzing data sets individually for each learner.
Each formative assessment yields a data set. The data is analyzed to determine the success criteria each student has mastered, and more importantly which success criteria need further instruction, and why. The outcome of this analysis yields an educational diagnosis. This diagnosis, identifies for the teacher exactly what the learner needs in order to master the success criteria of the learning outcome. This diagnostic information is used to individualize the knowledge or skills the learner needs, in order to meet the success criteria. This diagnostic information is used to differentiate the lesson to meet unique learning needs. The teacher designs instructional strategies a learner needs in order to master the success criteria of the lesson. Following this personalized learning opportunity, the teacher closes the loop with a new formative assessment.
Using the data-driven differentiated framework improves the learning of a diverse group of students. This framework provides the teacher with a systematic process that allows the teacher to teach to reach every learner.