Leveraging Deep Learning for STEM Education in Higher Secondary Grades

This study introduces a deep learning-based framework to help upper secondary pupils learn STEM subjects more effectively by tailoring their learning. Predicting conceptual success, providing tailored feedback, and improving each student's learning path are the recommended steps. The system uses robust neural networks and adaptive algorithms to predict mastery and engagement levels from student behavior, exam answers, and performance logs. A predictive model explains complex learning processes using deep neural networks that have many hidden layers, nonlinear activation functions, and optimization methods like backpropagation. This system makes identifying students who require specific aid straightforward. Based on these assumptions, the framework continuously monitors student engagement and adapts teaching to their cognitive and motivational states to provide tailored feedback. Finally, reinforcement learning, individualized pacing, and cognitive load assessments make learning tougher and better. This system gives each student a real-time adjustable learning path. Real-world studies show the framework outperforms alternative teaching methods in technical and usability criteria. With 95.2% accuracy and 90 millisecond latency, it may be utilized by 12,500 people at once and maintain attention and satisfaction (above 90%). Many educational settings, including those with limited resources, can use it due to its simplicity and modest model size. These findings demonstrate that this deep learning strategy is a beneficial, scalable, and learner-centered way to improve STEM instruction by supporting mastery, engagement, and long-term memory in secondary school students.