Generating a Model to Predict Secondary School Students at Risk in Mathematics

Abstract

Mathematical courses aid individuals to deal with any problem that they might encounter in their life on the ground that Mathematics familiarize them with a problem-solving process. Additionally, people who have stood out in their profession are deemed to be those who have mastered their mathematical skills. Though, a lot of students encounter insurmountable difficulties and as a consequence they fail their Mathematical courses. That holds true particularly on the case of secondary school students. Thereby, controlling the risk of students’ failure in Mathematics is of utmost importance. This paper proposes a way to predict secondary school students’ critical performance in Mathematics through the generation of a potent model by means of a proper analysis of students’ engagement data. A discriminant function analysis has been carried out on the respective data and the generated linear discriminant functions constitute the prediction model. The prediction model generation process is demonstrated through a case study on a specific Mathematical course delivered at a Greek private secondary school. The research outcome is very promising given that our model could potentially be used to develop an early warning system for secondary school students at risk in Mathematics.