Construction method and system of multi-modal mixed teaching resources based on cognitive neuroscience

By using a multimodal hybrid teaching resource generation model based on cognitive neuroscience to align multimodal data with EEG signals, personalized teaching resources are constructed, solving the problem of the lack of interactivity and adaptability of existing resources, and effectively reducing cognitive load and improving the accuracy of resource generation.

CN117196908BActive Publication Date: 2026-08-25ZHEJIANG NORMAL UNIV +1
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Patent Information

Application Number
CN202311226217.X
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2023-09-21
Publication Date
2026-08-25
Estimated Expiration
2043-09-21

AI Technical Summary

Technical Problem

Existing teaching resources lack personalization and interactivity, failing to effectively reduce students' cognitive load, and teaching resources generated by deep neural networks cannot adapt to different learning objectives and content.

Method used

Based on cognitive neuroscience, this study utilizes multimodal teaching data embedding and uses EEG signals as modality alignment centers to construct a teaching resource generation model. By combining this model with the backbone network Unet for denoising and consistency loss processing, personalized teaching resources are generated.

Benefits of technology

It improves the accuracy and robustness of personalized teaching resource generation, reduces students' cognitive load, and measures and adjusts the effectiveness of teaching resources through a cognitive load prediction model.

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Abstract

The application discloses a multi-modal mixed teaching resource construction method and system based on cognitive neuroscience, and the method comprises the following steps: acquiring a hidden vector of static and dynamic data in a teaching environment; constructing consistent semantic information with brain data based on the brain data of students and the hidden vector of static and dynamic data; performing denoising consistency loss processing on a backbone network Unet to construct a teaching resource generation model embedded with brain science; constructing a teaching resource generation model for an actual teaching scene based on the actual teaching environment style; inputting the static data in the teaching environment and the brain data of students into the teaching resource generation model to perform load prediction, and obtaining a teaching resource-cognitive load prediction model. The application can generate teaching resources according to learning and teaching targets and teaching contents, and reduce the cognitive load of students. The application can be widely applied to the technical field of intelligent auxiliary teaching as a multi-modal mixed teaching resource construction method and system based on cognitive neuroscience.
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