10 results about "Smart classroom" patented technology

This invention provides a real-time learning feedback method and system for smart classrooms based on smart classrooms, including: Step S1: collecting and preprocessing data; Step S2: selecting learning characteristic variables based on the data to obtain training variables; Step S3: training a model based on the training variables; Step S4: determining whether the fitting result of the model's input data and output data is greater than a preset value; if the result is yes, deploying the model; if the result is no, repeating step S3; Step S5: outputting the student's academic performance through the model and providing feedback to the target personnel. This invention integrates learning display and the classroom central control system into a learning feedback screen. Through the learning feedback screen display scheme, the interface for teachers to view learning data and initiate teaching activities is separated from the traditional podium screen, avoiding cumbersome screen switching and protecting the privacy of teaching information.

This invention relates to a smart classroom system, electronic device, and storage medium based on a digital textbook platform. The system includes: a classroom environment construction module for building an online classroom environment, including student and teacher classroom environments; a teaching content processing module for generating teaching plans based on uploaded courseware or selected digital textbook content; a teaching activity provision module for matching corresponding teaching activities according to uploaded courseware or selected digital textbook content and generating corresponding teaching activity content; an activity data monitoring module for real-time monitoring of classroom data and processing it into corresponding indicator data according to preset indicators; and a feedback data module for generating teaching feedback data based on the indicator data of the teaching plan and / or teaching activities. This invention simplifies the teacher's operation process, provides rich and easy-to-use teaching resources, improves classroom teaching efficiency, and enhances teaching effectiveness.

The present utility model relates to a classroom noise monitoring device that detects ambient sound levels and provides immediate visual feedback to regulate classroom behavior. The device comprises a sound detection module configured to capture noise, a microcontroller board programmed to classify the detected sound into predefined threshold ranges, and a visual output module that displays indicators corresponding to acceptable, moderately high, and excessive noise levels. In one embodiment, the visual output module employs colored light indicators, while in another embodiment it utilizes a graphic display presenting emoticon icons. An optional wireless communication module may transmit noise data to a remote server or mobile device for monitoring and record-keeping. The device is enclosed in a wall-mountable or desktop casing and powered by a standard low-voltage supply. By providing real-time, intuitive feedback, the utility model offers an affordable and effective tool for promoting discipline and self-awareness in educational settings.

The application provides a kind of wisdom classroom monitoring panoramic attendance method based on deep learning, to solve the problems such as high cost, limited accuracy and monitoring blind area of existing classroom automatic roll call system. The method is to integrate panoramic camera and deep learning technology to improve attendance accuracy and efficiency while significantly reducing system cost. The core of this method is to use panoramic camera to capture the panorama of the classroom, and combine face feature extraction model to accurately identify and verify the identity of students. Compared with existing technology, the application has significant cost-effectiveness, can effectively reduce the monitoring blind area, and improve the efficiency of classroom monitoring attendance through automation. In addition, the flexibility and adaptability of this method make it widely applicable to different teaching environments, which is of great significance to improve the quality of teaching management. These advantages show that the application has broad application prospects in the field of smart education.

The application provides a resonance mode-based high school mathematics classroom learning state feedback method and device, a non-intrusive, high-precision and fine-grained learning cognitive state recognition processing architecture is designed, which realizes the extraction of multiple dimension features on the basis of the video stream, audio stream and behavior event stream collected in the smart classroom, and then combines the resonance mode feature vector obtained by adaptive weighted fusion to realize accurate recognition of multiple cognitive states, so as to provide reliable data support for real-time intervention of high school mathematics classroom teaching and post-class learning situation depth analysis.

This invention discloses a locally deployed intelligent interactive classroom system and method based on an edge intelligence architecture, belonging to the field of digital intelligent teaching technology. It deploys a lightweight multimodal fusion model at the edge, accurately identifying students' cognitive states in milliseconds, providing a basis for personalized intervention. Employing a physically closed architecture, federated learning, differential privacy, and TEE hardware-level protection, it meets data compliance requirements and eliminates the risk of data leakage. Utilizing deep reinforcement learning, it dynamically optimizes learning paths based on real-time status and historical data, achieving personalized instruction and improving learning effectiveness and experience. Leveraging the WebAssembly sandbox, it allows third-party developers to access extended applications while ensuring data security, contributing to the construction of a smart classroom ecosystem.

This invention discloses a smart classroom control method and system based on multimodal cognitive state assessment, relating to the field of teaching technology. The key technical points include the following steps: collecting multi-dimensional perception data of the smart classroom; wherein the multi-dimensional perception data includes student cognitive behavior data and teaching process state data; performing feature analysis on student cognitive behavior data to obtain cognitive features; continuously tracking and analyzing student cognitive feedback data to obtain individual cognitive fit and group cognitive synergy; verifying the cognitive synergy recovery state of the smart classroom based on individual cognitive fit and group cognitive synergy; and judging the cognitive synergy recovery state to determine whether classroom control is needed. The effect is to improve the utilization efficiency of teaching resources and reduce the consumption of ineffective teaching.

The present application relates to the technical field of teaching management, and discloses a kind of smart classroom interactive teaching software and teaching system, comprising: multi-modal data acquisition module, regional feature analysis module, cognitive bias prediction module and teaching strategy scheduling module;Multi-modal data acquisition module obtains the behavior characteristic sequence in classroom environment and the teaching instruction sequence containing knowledge point weight;Regional feature analysis module divides behavior characteristic sequence into regional behavior matrix;Cognitive bias prediction module is aligned sequence by time regularization logic, determines the ratio of response time delay difference of feedback relative to instruction and knowledge point weight, to obtain cognitive transmission factor and generate bias transmission prediction graph;Teaching strategy scheduling module generates teaching intervention signal according to bias transmission prediction graph, the present application quantifies the transmission law of cognitive bias to back sequence node, realizes the evaluation of teaching administrative management liability, and improves the management efficiency of teaching resource scheduling.