In alignment with the Forum's mission to support the development of comprehensive data systems, improve data coordination, and lead discussions on key data issues, this publication has the following four objectives:
High-quality attendance data serve as the foundation for understanding where students are during the school day. These data provide the information needed for schools to formulate practices, programs, and policies to improve attendance rates. Comparable data also allow comparisons between schools, districts, and states—which is necessary for educators to identify relationships between student attendance and student achievement, promotion from grade to grade, and high school graduation. Districts and schools depend on accurate attendance data for a number of other reasons as well. For example, staff need to know which students are under the schools' supervision each day as a part of the district's general building, staff, instructional, and fiscal management responsibilities. This information is also necessary on testing days for determining whether schools and districts are meeting adequate yearly progress (AYP) targets under the No Child Left Behind Act of 2001 (NCLB). Moreover, these data become critical in the case of national, local, or family crises.
This attendance code taxonomy is an exhaustive, mutually
exclusive set of codes that document a student's
attendance status at any given time.
The taxonomy presented in chapter 2 contributes a basis for standardizing student attendance data, which are currently documented and coded in a number of different ways across the nation. Because of these differences, there is a common need for the means to translate state or local definitions into a universal accounting of attendance data. The need for this coordination is compounded by the growing variety of educational settings in which students spend their time, including virtual schools, institutions of higher learning, and work-study settings. In the absence of such a national standard, attendance data cannot be compared across schools, districts, or states, making the comparison of different attendance interventions and programs difficult, if not impossible.
The taxonomy also allows users to compare absenteeism, average daily attendance (ADA), and other high-interest statistics because it facilitates the exchange of transcripts across districts that may have different attendance policies. Finally, the taxonomy increases the trustworthiness of data reported to community and policy groups because of its foundation of standard terms, categories, and definitions.
Improving the quality of attendance data involves many functions within districts and schools. These include local technology capabilities and procedures, as well as services targeted at student populations that may otherwise be difficult to track. Procedural concerns highlighted in chapter 3 include administrative guidance such as clarifying how attendance data are coded in the school and district. Other concerns are related to organizational challenges like establishing a culture of data quality for all staff. Management issues are also of concern including dealing with students who are regularly scheduled to be off site or participating in virtual education. Technological challenges include automating data systems, upgrading technology resources, training staff to use technology to manage data, and integrating data and systems that are otherwise not interoperable because of hardware or software limitations.
The taxonomy presents a detailed way to collect attendance data but, for the information to be useful, schools and districts need to be able to act on the data. Many schools and districts use detailed data on attendance to guide policies or practices that improve student attendance. The examples presented in chapter 4 may inspire other education organizations to examine their local attendance policies, analyze student data, and identify new strategies for improving student attendance rates.