9th International Conference on ICT for Sustainable Development

Authors - Supriya S. Gorde
Abstract - The present study endeavors to improve the relationship between educators and engineering students in India, since it is a crucial element that impacts the job marketability, personal development, and progress of graduates. Due to a lack of customization and efficient communication processes, traditional educational models frequently fail to create strong, lasting connections. Tailored learning as well as effective guidance levels is limited by the general nature of current methods to engineering pedagogy which ignores the requirements and career goals of individual students. The proposed study aims to close these gaps by introducing a complete framework based on machine learning (ML) that would transform the landscape of pedagogical relationships in an academic scenario. We make use of Support Vector Machine (SVM), Logistic Regression (LR) and Extended Gradient Boosting (XGB) as these approaches can improve student performance analysis for custommade learning routes. Leveraging these particular machine learning techniques makes sense because of their demonstrated capacity to analyze large, complicated datasets and derive actionable insights. These insights are essential for customizing education to student profiles, forecasting employment trends, and enhancing communication efficacy. When evaluated across numerous datasets, the suggested model significantly improved precision, accuracy, recall, and other important performance characteristics, demonstrating its profound influence. It makes sense to utilize these specific machine learning approaches since they have a track record of successfully analyzing vast, complex datasets and producing insightful results. These insights are critical for predicting job trends, personalizing instruction to each student's profile, and improving communication effectiveness. The proposed model had a remarkable effect on precision, accuracy, recall, and other key performance parameters when tested on many datasets.