Data-driven intervention-level prediction modeling for academic performance

Abstract

Poor academic performance in final exams at primary school level in Kenya is a strong indicator that the student will not attain the desired career in future. It is therefore important to be able to predict the students who are likely to achieve below average marks and need high intervention early enough for them to improve their marks. This paper reports on a study to classify primary school students into two categories, those that need high intervention and the rest. The prediction can be initiated as early as two years before the final exam. An important highlight of this study is its focus on rural schools in a developing country. A total of 2426 records of students are used to build intervention prediction models. In the first set of experiments all the features are used. An optimal subset of features is then determined and a second set of experiments carried out. Results demonstrate that it is possible to attain reasonably accurate intervention prediction models even with the reduced dataset. The insights obtained will be used to build a mobile prediction tool that can be utilized by education stakeholders in rural regions where there is lack of electricity.