| District Office: Part Two (PDF) |  |
| New data collection and analysis tools have emerged in the past decade. These new tools, such as data warehousing and data mining, are now making their way into education. Districts can use these new tools to collect important data, which in turn, can inform their planning and decision-making processes. |  |
| Learning Facts (PDF) | |
| In just the last ten years, goaded by broad and still unsettled cultural shifts, education practices have changed dramatically. Schools are no longer just recording and analyzing inputs -- dollars spent, number of days of instruction, numbers of students per teacher -- but pushing their data-gathering and analysis efforts into the brave new world of outcomes. Who is dropping out and why? Which students are reading at grade level, and which are not? How are 4th graders doing on fractions and decimals? Today's educators are deciphering, and using, the results of student assessments better than ever. And it is not a reform at the margins. This article from the Winter 2007 issue of Education Next takes a close look at three schools that have integrated data into their instructional decision making: Evelyn S. Thompson Elementary School in Aldine, Texas; Feaster-Edison Elementary School in Chula Vista, California; and Elm City College Preparatory School in New Haven, Connecticut. Each has concluded that the practice has helped improve student achievement. | |
| The Information Edge | |
| Educators at all levels are more interested than ever in tapping the potential of electronic data to accelerate their students' achievement. | |
| Voices Of Experience | |
| An article describing strategies for designing and implementing a successful data management system | |
| Tapping Into The Power Of Longitudinal Data (PDF) | |
| Presentation that addresses the need for longitudinal data in schools and what principals should do about it. | |
| High School Classroom: Part One (PDF) | |
| The ongoing assessment of student progress can provide a foundation for personalized instructional delivery. Technology tools can rapidly generate feedback from a variety of assessment types. Using technology-based diagnostic assessments, students and teachers can understand what a student already knows and help plan an instructional program to get him or her to the next level. Frequent formative assessments can allow students and teachers to monitor student progress and understand how close they are to their goals. Summative assessments allow students to demonstrate mastery and teachers to document student achievement. Technology-based assessments can also adapt to student understanding, becoming progressively more difficult as students learn more. | |
| Linking Teacher And Student Data To Improve Teacher And Teaching Quality (PDF) | |
| A report that focuses on the importance of establishing data systems that link teacher, school, and student information. Examples of states who are creating and using such systems are highlighted. | |
| Study: Schools Head Toward Enterprise Data Management | |
| An article about a study released by a consulting firm showing that school districts are starting to take an enterprise-based approach to data management. | |
| Improving Achievement Through Student Data Management | |
| On average, there is little aggregation of student data in today's school systems. Information is siloed, redundant and difficult to share. The technologies used -- if any -- are aging and frequently incompatible. An ideal state has complete aggregation and alignment. It is easier to ensure that students meet challenging standards, teachers target instruction, parents know teachers are helping their children, school districts know how to allocate resources effectively and the government knows how schools are doing. | |
| Steps For Ensuring Data Quality | |
| Data quality is more than accuracy and reliability. High levels of data quality are achieved when information is valid for the use to which it is applied and when decision makers have confidence in and rely upon the data. Implement these steps organization-wide to increase and maintain data quality. | |