Rough set theory (RST) is an emerging technique used to deal with problems in data mining and knowledge acquisition. However, the RST approach has not been widely explored in the field of academic achievement. This investigation developed an improved RST (IMRST) model, which employs linear discriminant analysis to determine a reduct of RST, and analyzed the academic achievements of junior high school students in Taiwan. An interactive interface was created so that students could answer questions to predict their academic achievement and they could learn essential skills for improving their academic achievement. Empirical results showed that the IMRST model selects crucial information from data without predetermining factors and can provide accurate rates for inference rules. Hence, the developed IMRST model is a promising alternative for analyzing academic achievement data. (Contains 10 tables and 4 figures.)