Auto-Encoder Teaching Material Analysis for Concept Gap Detection

The patent replaces rule-based classification and superficial pattern matching (mechanical systems) with a neural network-based semantic analysis system. The neural network automatically learns semantic relationships and conceptual structures from teaching materials, achieving high-precision concept identification without manual rule engineering. This substitution enables the system to understand the semantic meaning of questions and solutions at a deeper level, resolving the contradiction between analysis precision and system complexity.

The patent applies preliminary action by pre-training the neural network model on large corpora of teaching materials before deployment. The pre-trained model already possesses learned semantic representations and conceptual knowledge, enabling it to quickly adapt to specific analysis tasks without requiring extensive computational resources during actual use. This approach allows the system to achieve high adaptability across different subjects and teaching materials while reducing the computational complexity encountered during real-time processing.

The patent extracts only the essential conceptual elements from learner submissions that are relevant to the specific question being analyzed. Rather than performing exhaustive analysis of all aspects of a submission, the system identifies and focuses on the key concepts that determine correctness and understanding. This extraction approach enables the system to provide high-precision, personalized feedback on conceptual gaps without requiring excessive processing time or computational resources on irrelevant details.