  A learning algorithm based on primary school teaching and learning is
presented. The methodology is to continuously evaluate a student and to give
them training on the examples for which they repeatedly fail, until, they can
correctly answer all types of questions. This incremental learning procedure
produces better learning curves by demanding the student to optimally dedicate
their learning time on the failed examples. When used in machine learning, the
algorithm is found to train a machine on a data with maximum variance in the
feature space so that the generalization ability of the network improves. The
algorithm has interesting applications in data mining, model evaluations and
rare objects discovery.
