The Impact of AI Tools on Enhancing EFL Learners' Engagement in Higher Education Using HubSVM Models

BL has become prevalent in higher education as a means of delivering information, managing activities, and executing lessons, thanks in large part to the proliferation of COVID-19 and other technological developments in education. By combining online and offline learning, BL encourages students to be more engaged and flexible than in a typical classroom setting. Engaged learners are crucial for psychometric analysis; they are like energy in action, full of life, focus, and determination. By encouraging mental and physical exertion towards studying, it significantly improves EFL students' involvement in higher education. Using MinMax for feature scaling and the HubSVM, which, similar to the L1-norm SVM, allows automatic feature selection, this study analyses and improves engagement. By highlighting highly connected features, HubSVM improves the selection process and makes computing the complete solution path easier. The results show that when dealing with highly correlated variables, HubSVM performs better than L1-norm SVM. The suggested classifier outperforms the competition with an accuracy of 95.65%. The results show that the concept works well to make BL settings more engaging for students. This research helps make higher education more engaging for EFL learners by incorporating modern machine learning techniques, which means they will have a better, more effective learning experience.