Machine learning based stream selection of secondary school students in Bangladesh

Abstract

A strong civilization is built on a strong foundation, and education plays a vital role in acquiring the necessary information and skills for success in life. This thesis focuses on the education system in Bangladesh, which is divided into three levels: primary (PEC), middle school (JSC), and secondary school certificate (SSC). The selection of a stream after the eighth grade is crucial for students’ higher studies and career planning, with three options available: Science, Business Studies, and Humanities. To address the challenge of stream selection based solely on PSC and JSC results, we have collected a dataset from various Bangladeshi schools, comprising student records that include subject-wise results, parent’s academic qualification, parent’s profession, parent’s monthly income, sibling information, district, etc. In this study, we employ a series of machine learning regression algorithms to analyze the data.Furthermore, we utilize performance metrics and R2 scores to evaluate and validate the models’ performance. Among the regressors, the gradient boosting algorithm demonstrates superior performance for the Science stream, achieving an R2 score of 0.34540. For the Business Studies stream, the Support Vector Machine exhibits significantly better performance with an R2 score of 0.534092. Finally, the Humanities stream shows excellent results with an R2 score of 0.80337 using extreme gradient boosting.To enhance the interpretability of our models, we leverage the Local Interpretable Model Agnostic Explanations (LIME) technique. The analysis and findings of this research are expected to assist prospective students and stakeholders in making informed decisions regarding stream selection, ensuring alignment with their future goals and aspirations.