Limiting data collection to a sample group is one way to increase effectiveness in dealing with data. The paper describes how to draw a sample group (random sampling, stratified random sampling, purposeful sampling, and convenient or opportunity sampling) and discusses how to determine the size of the sample group. (SM)

Getting answers to questions about schools and student learning requires that educators know how to collect, analyze, and interpret data. Six critical steps in this process include: posing questions, establishing judgment criteria, making a plan, gathering data, analyzing data, and interpreting results. These steps can serve as a beginning guide…

Presents a model to help schools pose and answer data-based questions about teaching and learning, describing different types of data that educators can use to piece together pictures of their schools (e.g., student learning statistics, demographic data, and school process data) and discussing how educators can cross data from different categories…

Teachers' quests for more data-based planning, problem solving, and decision making often stumble against limited capacities for engaging in thoughtful interactions, with groups typically lacking process tools, collaborative communication skills, and reflective habits. This article presents a three-phase model for guiding data-driven dialogue in…

This article discusses how to use technology to collect, store, sort, and reformat assessment data on the school improvement process, explaining the use of desktop computers with databases and the use of portable devices in the assessment process. (SM)

A three-step framework to help teachers examine and modify teaching practice based on data analysis includes: specify current levels of student performance, create action plans, and evaluate and communicate progress. A case study of a teacher who used the framework to pinpoint one student's writing problems, create change in practice, and assess…