Enhanced Education

An LDS can bring reality into clearer focus. When we "zoom in" on aggregate-level data, we see student-level data. When staring at a static image on the screen, longitudinal data are what we can observe when we press "Play" and follow the data (and in effect, students) through time and space. Therefore, longitudinal student-level data show the "real time" details of what is going on in an education organization. With these data, policymakers and educators no longer need to act on "hunches" or remain in the dark about what effects their decisions and practices have on students. LDS data provide transparency and allow far greater examination into the effects certain policies, programs, schools, principals, teachers, and classroom practices have on individual students.

Beyond simply monitoring student outcomes and reacting to traditional aggregate data, policymakers and educators can use LDS data to be more proactive. By illuminating the influence of the many variables that may contribute to student success or failure, longitudinal student-level data can help agencies identify trends; predict outcomes; and make more informed decisions about policy, administration, and instructional strategies. Education leaders will then be better equipped to create better policies, use resources more efficiently, and pursue the most effective teaching strategies to meet individual student needs. By providing timely information about what works where, and for whom, an LDS allows educators to shift to more proactive uses of data. That is, they can take us from asking, "What went wrong?" to identifying potential problems early on and asking, "What can we do to promote student success and avoid failure before it occurs?" For example, by making student-level longitudinal data accessible at the classroom level, teachers can easily view details of their students' histories (information that may or may not be available in traditional cumulative folders, but is consolidated electronically and readily accessible in a timely fashion through the LDS), follow progress throughout the school year, identify weaknesses, and tailor instruction to address problems before it is too late. They can work with other teachers to identify and learn from effective strategies. And with better data, they can more easily explore whether there are early warning signs for undesirable outcomes like dropping out, and act to keep students on track and in school.

Detailed student-level longitudinal data allow agencies to more accurately answer traditional questions such as, how many students are there in a school district? What percentage of fourth grade African-American students met the state's proficiency standard in math last year? What is the school's graduation rate? More importantly, by showing the educational system in finer detail, these data allow answers to deeper questions than was possible with aggregate-level, cross-sectional, "snapshot," data. For example, with LDS data the often inaccurate graduation rate estimates of the past can be replaced with more precise counts based on student-level data. Moreover, longitudinal data can be used to see how many years it took those graduates to earn their diplomas; and find out what they did after graduation day, in postsecondary education and in the workforce.

Table 2 presents some other examples of the types of questions LDS data can answer.*

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* Answering many of these questions requires a very sophisticated LDS that collects certain detailed data (e.g., unique student and teacher identifiers) and in depth data on students, teachers, classroom practices, school resources and facilities, and expenditures. The LDS must also be capable of linking these data to one another, from year to year; students to their teachers; and K–12 to prekindergarten, postsecondary, and the workforce.

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