Exploring the Impact of Time Management Skills on Academic Achievement with an XGBC Model and Metaheuristic Algorithm

Abstract

Estimating a student’s academic performance is a crucial aspect of learning preparation. In order to predict understudy academic performance, this consideration uses a few Machine Learning (ML) models and Time Administration Aptitudes data from the Time Structure Questionnaire (TSQ). While a number of other useful characteristics have been used to forecast academic achievement, TSQ findings, which directly evaluate students’ time management skills, have never been included. This oversight is surprising, as time management skills likely play a significant role in academic success. Time administration may be an ability that may impact the student’s academic accomplishment. The purpose of this research is to look at the connection between college students’ academic success and their ability to manage their time well. The Extreme Gradient Boosting Classification (XGBC) model has been utilized in this study to forecast academic student performance. To enhance the prediction accuracy of the XGBC model, this study employed three optimizers: Giant Trevally Optimizer (GTO), Bald Eagle Search Optimization (BESO), and Seagull Optimization Algorithm (SOA). Impartial performance evaluators were employed in this study to assess the models’ predictions, minimizing potential biases. The findings showcase the success of this approach in developing an accurate predictive model for student academic performance. Notably, the XGBE surpassed other models, achieving impressive accuracy and precision values of 0.920 and 0.923 during the training phase.