Enhancing e-learning through AI: advanced techniques for optimizing student performance.

Area of Science:

• Educational Technology
• Artificial Intelligence in Education
• Machine Learning Applications

Background:

• E-learning adoption necessitates innovative approaches for improved student outcomes.
• Artificial Intelligence (AI) offers transformative potential for educational methodologies.
• Conventional methods struggle to personalize learning and optimize student performance effectively.

Purpose of the Study:

• To examine AI's role in enhancing e-learning through predictive analytics and performance optimization.
• To develop an AI framework for monitoring student interactions and analyzing learning platform impact.
• To identify optimal strategies for blended learning systems using AI.

Main Methods:

• Implementation of AI algorithms, including machine learning and deep learning models.
• Utilizing Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) for performance forecasting.
• Analysis of student interactions and online learning platform data to evaluate understanding.

Main Results:

• AI models demonstrated substantial improvements in forecasting student performance metrics.
• Convolutional Neural Networks (CNN) achieved superior accuracy in prediction compared to other models.
• The study confirmed AI's capability to create adaptive and effective e-learning environments.

Conclusions:

• AI integration significantly enhances e-learning effectiveness and student academic achievement.
• AI facilitates customized learning experiences tailored to individual student needs.
• Advanced AI techniques, particularly CNNs, show significant promise for future educational applications.