Learning a more distributed representation of the input feature space is a powerful method to boost the performance of a given predictor. Often this is accomplished by partitioning the data into homogeneous groups by clustering so that separate models could be trained on each cluster. Intuitively each such predictor is a better representative of…

This document presents four issues of the Texas Association for the Gifted and Talented's quarterly publication, each of which focused on a particular theme: (1) instructional grouping options; (2) humanities and gifted students; (3) math and science; and (4) a 25th anniversary issue, "Silver Legacy: Shining on the Future for Gifted…

The Growing Giftedness Model for teaching gifted students is presented, which includes the following features: identification that relies entirely on scores and on demonstrated performance; cluster grouping during elementary school and classes dominated by gifted students at the secondary level; acceleration and enrichment; creative expression…