Disagrees with Harold Stevenson's article in same "Educational Leadership" issue; Stevenson compares incomparable groups of students and misinterprets data. Although U.S. students rate ability higher than Chinese students, they also appreciate value of effort. Chicago kids are poorer and come from larger families than their Chinese…

Excoriates Gerald Bracey's "broadsides" against the author's own article in same "Educational Leadership" issue for misinterpreting his conclusions and methodology. Stevenson's learning gap results did not oversimplify ability-effort relationship in U.S. and Asian students; results were similar for U.S. cities with both large…

Asserts that collecting assessment data from students is key to shaping effective instruction. Both informal and formal data about student learning not only shape instruction but also determine its effectiveness. Contends that continuous assessment that drives curriculum is a means of enhancing student and teacher performance. (Contains seven…

Discusses how connecting instruction, professional development, and student learning can make the difference between successful and unsuccessful data use. Uses a case-study approach to show that by acknowledging that implementation occurs over time, schools can safeguard against discarding new instructional practices prematurely. (Contains one…

Calls for simplicity when presenting data on student achievement. Data should help teachers improve teaching and learning, and focus on specific goals such as determining how many students are succeeding in a subject and, within that subject, what are the areas of strength or weakness. (Contains 22 references.) (WFA)

Expresses the importance of scientifically based research in education reform and explains how to judge the validity of educational research. Describes control groups, randomized and matched experiments, statistical educational significance, sample size, and the difference between scientifically based and rigorously evaluated research. (Contains…

A primer for schools attempting to analyze the data they collect. Describes ways schools can get a better picture of how to improve learning by gathering, intersecting, and organizing four categories of data more efficiently: (1) demographic data; (2) student-learning data; (3) perceptions data; and (4) school-processes data. (WFA)

Discusses how schools can avoid mistakes in building their school-improvement plans. The two most common mistakes are (1) using measures of student learning that are not sensitive to the actual learning occurring, and (2) having no system for interpreting and using the data. (Contains 1 figure and 11 references.) (WFA)

Discusses data warehousing, which provides information more fully responsive to local, state, and federal data needs. Such a system allows educators to generate reports and analyses that supply information, provide accountability, explore relationships among different kinds of data, and inform decision-makers. (Contains one figure and eight…

Discusses which kinds of data educators should respect and which they should reject. Asserts that most state accountability tests fail to produce the kinds of data that will improve teaching and learning. Teachers can get the data they need from their own instructionally useful classroom assessments. (WFA)