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)

Although the Great Flood of 1993 has dramatically reminded us never to take rivers for granted, it has also underlined the need to learn more about rivers and the environment in general. Rivers Project, an interdisciplinary high school curriculum, allows science, social studies, and English teachers to integrate curriculum in a way that encourages…

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…

Despite countless networking and interactive possibilities, the most commonly assigned use of classroom technology during 1997-98 was word processing, followed by Internet research and information gathering. Schools' reliance on general-purpose computing devices will eventually yield to lower cost, hand-held, and networked devices. (Contains 10…

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)