The use of analytic tools to predict student performance is exploding in higher education, and experts say the tools show even more promise for K-12 schools, in everything from teacher placement to dropout prevention. Use of such statistical techniques is hindered in precollegiate schools, however, by a lack of researchers trained to help districts make sense of the data, according to education watchers. Predictive analytics includes an array of statistical methods, such as data mining and modeling, used to identify the factors that predict the likelihood of a specific result. They have long been a standard in the business world--both credit scores and car-insurance premiums are calculated with predictive analytic tools. Yet they have been slower to take hold in education. Experts in predictive analytics in higher education and business say education may have a long way to go to develop the data infrastructure and staff capacity to make the tools useful on a broad scale. The statistical methods used to calculate credit scores and car-insurance premiums are now being used to predict which students are likely to drop out and which teacher candidates are good fits for jobs.