An Implementation of Stacked Deep Learning Model for Enhancing New Engineering Intelligent Course System based on MVC Framework

Engineering education rarely accommodates the various learning rates and routes of today’s students. Personalization and dynamic curriculum management do not exist in conventional course systems. This paper introduces an intelligent course system with the MVC (Model-View-Controller) architecture combined with stacked LSTM networks. The MVC architecture provides a clean separation between user interface, business logic, and data, making it easy to update and scale. The stacked LSTM networks are applied to learn long-term learning patterns from past student interaction history. The system dynamically personalizes course material, quizzes, and feedback. The suggested architecture is trained based on quiz scores, attendance, and interaction time. The comprehensive experimentation is carried out and performance measures are predicted and analysed. Experiments yield a 98.62 % accuracy in student retention and engagement rate improvement compared to static systems. This model fosters adaptive, outcome-based engineering education that conforms to New Education Policy (NEP) goals.