Optimizing Curriculum for Students: A Machine Learning Approach to Time Management Analysis - Jianmin Dong (Journal of Artificial Intelligence and System Modelling)
Examining how the timing of students' academic involvement might support evidence-based improvements in curriculum quality is critical, especially in light of current changes in medical education and increased responsibility in higher education. Time monitoring is an effective way to gauge how well students are using their academic time, which helps with data-driven curriculum design and development decisions. This study employs Extreme Gradient Boosting Classification (XGBC) and Histogram Gradient Boosting Classification (HGBC) techniques to forecast student time management. Additionally, Tasmanian Devil Optimization (TDO) and Equilibrium Slime Mould Algorithm (ESMA) are integrated to enhance the accuracy of both XGBC and HGBC models. To ensure impartiality, unbiased performance assessors are engaged to objectively evaluate the model outcomes. The study's findings showcase the effectiveness of the prediction model for student time management. Through hybridization with the 2 optimizers, the 2 base models yield the following outputs: XGBC + TDO (XGTD), XGBC + ESMA (XGES), HGBC + TDO (HGTD), and HGBC + ESMA (HGES). In the test section, the XGTD model demonstrates outstanding performance with an accuracy value of 0.9211, while the weakest-performing model, with an accuracy value of 0.8158, is attributed to the HGBC model.