The new and rapidly growing field of educational data mining is using the chaff from data collected through normal school activities to explore learning in more detail than ever, and researchers say the day when educators can make use of Amazon.com-like feedback on student learning behaviors may be closer than most people think. Educational data mining uses some of the typical data included in state longitudinal databases, such as test scores and attendance, but researchers often spend more time analyzing ancillary data, such as student interactions in a chat log or the length of responses to homework assignments--information that researchers call "data exhaust." Analysis of massive databases is not new to fields like finance and physics, but it has started to gain traction in education only recently, with the first international conference on the subject held in 2008 and the first academic journal launched in 2009. Experts say such data mining allows faster and more fine-grained answers to education questions that ultimately might change the way students are tested and taught. Data resources one would not necessarily think would be useful can turn out to be very powerful for making inferences. For example, research from the Pittsburgh-based Carnegie Mellon University found small changes in the length of time a student took to answer individual test questions signaled the student was struggling, cheating, or had given up in favor of filling in answers randomly. Research that draws on educational data mining may also compress the lag time between undertaking a study and getting usable results, addressing a common critique from educators.