Image Sensor-Supported Multimodal Attention Modeling for Educational Intelligence.

Area of Science:

• Artificial Intelligence
• Educational Technology
• Machine Learning

Background:

• Current multimodal perception systems in educational intelligence face challenges with low fusion efficiency and limited personalization.
• Existing methods often fail to effectively integrate diverse data sources like images, text, and contextual information for adaptive learning.

Purpose of the Study:

• To propose a novel deep learning framework that enhances multimodal perception for educational intelligence.
• To improve fusion efficiency and personalization in adaptive learning systems through advanced data integration techniques.

Main Methods:

• A cross-modal attention mechanism integrates image sensor data with textual and contextual information.
• A cross-modal alignment module ensures semantic correspondence between visual and textual features.
• A personalized feedback generator uses learner embeddings for adaptive guidance, complemented by a cognitive weakness highlighter.

Main Results:

• The proposed framework achieved high performance metrics, including 92.37% accuracy, 91.28% recall, and 90.84% precision, outperforming conventional baselines.
• Ablation studies demonstrated significant gains from the fusion module (+4.2% accuracy) and attention mechanism (+3.8% recall, +3.5% precision).
• The system showed stability in cross-task and noise-robustness tests, confirming its reliability.

Conclusions:

• The developed deep learning framework offers a high-performance, transferable solution for next-generation adaptive learning systems.
• The method provides precise, explainable, and context-aware feedback by leveraging advanced multimodal perception.
• This approach addresses key limitations in current educational intelligence, paving the way for more effective personalized learning experiences.