Design and Development of an Intelligent Student Learning Prediction Model using Enhanced Deep Learning Principles

The Intelligent Student Learning Prediction Model using Enhanced Deep Learning Principles (EDLP) platform is a major leap in the domain at the intersection of educational technology and artificial intelligence. We present a new, predictive model that combines AlexNet (a state-of-the-art convolutional neural network architecture for image recognition) with a very deep DNN tailored to analyze diverse data types from traditional academic records as well as complex input based on pictures. By taking advantage of these strengths-high-precision image-based feature extraction using AlexNet, and comprehensive data analysis with a DNN-the EDLP model is designed to make the most accurate predictions yet regarding student learning outcomes.Our research provides a comprehensive description of the methodologies used to develop the EDLP model, which includes data preprocessing, feature engineering, and their articulation with machine learning technologies. This is importantly on application of the model instituted a prediction rate of 97.6%, which indeed marked a prompted potential revolution in educational assessment and personalized learning strategies. This level of predictive accuracy, despite being more oriented towards the very effective profiling and prediction of the needs of a single student, places more emphasis on the great value the model offers as a tool to help educators and policy-makers be effective in improving teaching and learning. Thus, the implications of EDLP also relate not only to its ability to predict academic performance but are equally important regarding learning success-influencing factors as well as related intervention strategies.