Predicting Students' Grades in a Professional Undergraduate Course Using an Ensemble Model

Abstract

The goal of this study is to come up with a way to predict how well first-year college students in a professional program do in their classes (BCA). Tracking students' academic progress is an important area for ensuring the optimal growth of their analytical and logical skills. Being able to predict a student's academic success in the years immediately after graduation is useful for many different groups, including the government, legislators, and educators. An ensemble model is made for this task using a decision tree, a gradient boost algorithm, and some Naive Bayes techniques. This model gives the most accurate and reliable results. A questionnaire was created to find the factors that affect students' academic, social, behavioral, and demographic performance in school. Then, based on how well each of the three approaches performed, an ensemble model was created. The quality of the outcomes from the suggested ensemble model was evaluated using a 10-fold cross-validation technique. The output of an ensemble model allows for accurate and efficient prediction of student performance, and can help pinpoint students who are at risk of failing or dropping out of school. In order to create the current model, we employ both classification and regression techniques. With the current data set, the model achieves 99.1% accuracy in determining the important factors influencing students' academic success. As the suggested methodology allows for early identification of students who are at danger, it can also offer preventative and remedial strategies to boost students' overall academic performance.