Data-Driven Instruction

            Chapter nine is about the analysis of data that drives instructional decisions.  Chapter nine defines sources of data and identifies how to analyze the data to make curricular decisions.  Collecting, analyzing, and understanding how to use data effectively is a continuing process that is discussed.  This chapter discusses sources of data, analyzing data, and how to use data effectively.

            There are four sets of data which include student learning data, demographic data, program data, and perception data, that is examined to create a clear picture of what schools must do to meet the needs of all their students.  Before implementing any process for collecting and analyzing the data, teachers and administrators must understand what each of these four sources involves.  Student learning data can be derived from standardized tests, criterion-referenced tests, teacher observations of student abilities, and authentic assessments.  Standardized testing is able to provide assessments that are psychometrically valid and reliable.  Criterion-referenced tests are intended to measure how well a person has learned a specific body of knowledge and skills.  Teacher observation of students’ abilities is making note of how students are responding to the material being taught.  Authentic assessment incorporates real-world situations.

            Demographic data includes enrollment, attendance, ethnicity, and gender figures, grade levels, dropout rates, and socio-economic information.  The main reason for collecting this type of data is to have a clearer picture of a district’s students.  It is to understand who the students are, what trends are seen in the student population, and what factors outside of school may assist administrators and teacher to better understand students.

            Program data are a description of school programs, instructional and assessment strategies, and practices in the classroom.  It is seen as action research, which involves gathering data that will enlighten future decision making about programs and curricula.  Perception data consists of individual views, beliefs, and values about systems in the workplace and in academic settings.  Perception data can be collected through questionnaires, interviews, and observations.  Through this data, educators can recognize and respond to the opinions and ideas of the wider school community.

            Once all types of data have been collected, it can then be analyzed.  Analyzing data can be used to help improve and facilitate student learning.  Also, once data has been analyzed, educators will be able to use the information to place students in appropriate courses, to monitor quality of instruction for student learning, and to develop working relationships within the community in order to enhance the business program.

            Data is very beneficial in the school system.  The different types of data are used regularly and educators learn about their students and their own teaching styles.  Data can be used for many different reasons and it is important that educators take advantage of the benefits of data.

White, Raholanda. (2007). Data-Driven Instruction. In M.L. Bush (Ed.), Assessment for an Evolving Business Education Curriculum (pp. 117-129). Reston, VA: National Business Education Association