119 results about "Online teaching" patented technology

The invention discloses an online teaching interaction method based on a multi-modal knowledge graph, a medium and equipment, and the method comprises the steps: firstly collecting original teaching information, and carrying out the cross-modal semantic alignment processing, and obtaining structured teaching information; then constructing a multi-modal knowledge graph containing text concept entities, video key frame feature vectors and voice text transcription contents, and forming a concept-visual feature association edge, a concept-voice segment association edge and a cross-modal similarity association edge; converting the user interaction behavior data into a knowledge graph query vector, extracting a three-dimensional teaching situation sub-graph from the multi-modal knowledge graph, and generating progressive or comparative teaching content according to a user operation type; and finally, dynamically adjusting a teaching strategy by constructing a cognitive state tracking matrix, and generating a personalized learning navigation map. The problems of knowledge fragmentation and insufficient interaction intellectualization in virtual simulation teaching are solved, and the teaching effect of ideological and political education is improved.

The invention relates to the technical field of online network teaching, and discloses an online network teaching practice method based on a virtual technology, and the method comprises the steps: obtaining virtual scene data, and carrying out the integration and building of a dynamic knowledge graph and a multi-modal teaching resource library; learning behavior features are extracted according to the cognitive features of the learner, knowledge difficulty levels are divided, and immersive learning is guided; constructing a cognitive evaluation model, optimizing a learning path through hierarchical reinforcement learning, and generating a self-adaptive teaching instruction; collecting interaction track data to generate an operation thermodynamic diagram, and evaluating the skill mastering degree; complexity parameters of the virtual scene are dynamically adjusted, a virtual-real linkage experiment simulation environment is constructed, and skill training closed-loop feedback is achieved. The invention further relates to specific implementation modes of resource integration, cognitive feature extraction, learning path optimization and the like. According to the method, the personalized and immersive experience and skill training effect of online teaching are improved, and the method is suitable for the field of online network teaching.

The invention relates to the technical field of online teaching management, and discloses an online teaching optimization method and system based on emotion recognition, and the method comprises the steps: collecting the real-time emotion data of a learner through a multi-modal sensor, carrying out the fusion of a graph convolutional neural network to generate a feature vector, and optimizing a teaching strategy parameter through a meta-reinforcement learning model, the dynamic course generation model combines an improved genetic algorithm and a knowledge graph to optimize a teaching content sequence, and the hierarchical teaching control model realizes knowledge path planning, interactive adjustment and learning state evaluation, and generates a teaching control instruction. According to the method, personalized online teaching optimization is realized, the state of a learner can be accurately grasped, the teaching interaction mode is optimized, the course content is dynamically adjusted, the learning effect is comprehensively and accurately evaluated, the problems that traditional online teaching lacks personalization, the state of the learner is difficult to grasp and the like are effectively solved, the online teaching quality and the learning experience are improved, and the learning experience is improved. And the online education development is promoted.

The invention discloses a nursing medicine education system based on the Internet, and relates to the field of computer application technology and education management, and the system comprises a theory learning module which is used for achieving the online teaching and personalized learning pushing of nursing theory knowledge, the nursing skill training module is used for training and evaluating the standardized nursing operation ability of students, the virtual simulation practical training module is used for providing an immersive virtual clinical training environment, and the structured nursing document training module is used for improving the ability of the students to write standard nursing documents. The intelligent evaluation module is used for carrying out automatic, multi-dimensional and structured comprehensive evaluation on the learning and training process of students, and the system integration interface module is used for realizing data exchange and interaction between the teaching platform and an existing information system of a hospital. The system has the advantages of complete functions, closed-loop data and accurate intelligent evaluation, and can effectively improve the standardization, practicability and intelligent level of nursing education.

The invention discloses an online programming system based on large language model assistance and a teaching recommendation method, the system comprises a base model acquisition module, a problem decomposition module, an abstract modeling module, an algorithm design module, a code analysis and assistance module and an evaluation module, and is used for helping students to finish a programming task of teaching design online. And performing problem decomposition, abstract modeling and algorithm design on actual problems by using a large language model, and solving the problems encountered by students during programming. Based on the system, the invention further designs a teaching recommendation method, multi-modal data in the information classroom teaching process is collected, a dynamic three-level teaching interaction diagram of the space-time environment, knowledge resources and cognitive behaviors is established through man-machine, life and teacher-student interaction behaviors, and accurate teaching recommendation and intervention strategies of the human-in-the-loop are provided. According to the invention, a three-dimensional comprehensive teaching field for learning environment intellectual computing is established in a programming scene, and a guarantee is provided for high-quality modern education development.

The invention relates to a method and device for predicting online open course learner satisfaction and electronic equipment, and the method comprises the steps: predicting the online open course satisfaction of students through an MLP and RBF neural network model by using a virtual learning environment of a large-scale online teaching and learning platform and combining learning behavior data in a learning management system (LMS); the model comprises a data acquisition and processing module, a multilayer perceptron (MLP) module, a radial basis function (RBF) neural network module, a classification tree module and a control block, the data acquisition and processing module is used for generating a training and testing data set, and the MLP and RBF neural network model predicts the satisfaction degree of a learner. The MLP model carries out feature extraction through a multi-layer perceptron structure and different activation functions, the RBF model measures the distance between input data and a center by using a radial basis function to realize feature extraction, the classification tree is used for judging a prediction model to which a data point belongs, the control block integrates features from the MLP and RBF neural network models, and the RBF model is used for determining a prediction model to which the data point belongs. Experimental results show that the prediction accuracy of low-satisfaction-degree learners and high-satisfaction-degree learners can be improved at the same time through the combination scheme of the MLP and the RBF, the method can be applied to learner satisfaction degree prediction of various online open courses, an educational institution is helped to know the satisfaction degree condition of students in time, course design and teaching strategies are optimized, and the teaching efficiency is improved. And important support is provided for teaching reform and optimization in the field of online education.

The invention relates to the technical field of teaching systems, and particularly discloses an education quality evaluation model construction method and system, and the method comprises the steps: S1, constructing a multi-source interface; s2, constructing a pretreatment layer; s3, constructing a feature calculation layer; s4, constructing a dynamic evaluation logic layer; s5, constructing a suggestion generation layer and a structured teaching suggestion library; s6, constructing a feedback evaluation module and an optimization module; the core of the method is that a structured teaching suggestion library is introduced and a feedback evaluation and optimization module is constructed, so that education quality evaluation, teaching intervention suggestion generation, effect tracking and suggestion library optimization form a closed-loop system; therefore, the established education quality evaluation model can realize real-time and dynamic education quality evaluation of an online teaching process, and structured and personalized teaching adjustment suggestions can be generated according to an evaluation result.

The invention discloses an online teaching video production method and device based on a virtual scene, and a medium, belongs to the technical field of teaching video production, and is used for solving the technical problem of high curriculum abandoning rate caused by low construction efficiency, low content updating rate and low knowledge transmission accuracy of an existing online curriculum. The method comprises the steps of customizing a digital teacher image of a target teacher based on modeling software; wherein the digital teacher image comprises a 3D digital teacher image and a 2D digital teacher image; constructing a virtual teaching scene, and training a virtual scene model to identify a user instruction so as to adjust features in the virtual teaching scene; based on the made teaching courseware, generating a corresponding digital human control script and a voice explanation script; placing the digital teacher image and the virtual teaching scene into a virtual environment, and fusing the digital teacher image and the virtual teaching scene with the digital human control script and the voice explanation script to form a virtual teaching model; and performing video rendering on the virtual teaching model to obtain a teaching video.

The invention relates to the technical field of data processing, and provides a teaching interaction data processing method and system based on cross-device collaboration, which are used for guaranteeing the fluency and timeliness of online teaching interaction. The method comprises the following steps: acquiring a real-time interaction data set of a plurality of heterogeneous teaching terminal devices; performing cross-device cooperative task division processing on the real-time interaction data set to obtain a plurality of sub-task sets, each sub-task set corresponding to a data processing task in one teaching interaction scene; according to the device resource demand characteristics of the plurality of subtask sets and the real-time resource capacity characteristics of the heterogeneous teaching terminal devices, executing resource scheduling processing, and generating a cross-device resource allocation strategy; and based on the cross-device resource allocation strategy, performing a co-processing operation on the plurality of sub-task sets, generating integrated teaching interaction data, and distributing the integrated teaching interaction data to corresponding terminal devices.

The invention relates to the technical field of digital twinning, and provides an online teaching interaction enhancement method and system based on digital twinning, and the method comprises the steps: obtaining a user behavior data set through a multi-modal data collection device; performing space-time alignment processing on the user behavior data set, generating a virtual avatar driving data set synchronized with the user behavior, inputting the virtual avatar driving data set into a digital twinborn model of the virtual teaching scene, and driving a virtual avatar in the digital twinborn model to execute a real-time action corresponding to the user behavior; based on the real-time action of the virtual avatar and the spatial relationship data between the teaching elements in the virtual teaching scene, an interaction instruction set is generated, and the interaction instruction set is used for indicating response operation of the teaching elements in the virtual teaching scene; and adjusting interaction feedback configuration information of the virtual teaching scene according to the interaction instruction set, and updating rendering output data of the digital twin model based on the adjusted interaction feedback configuration information.

The invention relates to the technical field of online education, and discloses an online training education informatization teaching method based on big data, which comprises the following steps: S1, collecting interaction log data of an online teaching platform, a real-time evaluation system, emotion monitoring equipment and a user, and performing cleaning, standardization, noise filtering and feature extraction on the data; s2, constructing a student state vector according to the data in the step S1 so as to implement dimension reduction processing; and S3, establishing a state action model. The personalized teaching recommendation system based on big data is adopted, learning data of students are collected in real time, a learning path is analyzed and optimized through the big data technology, the technical effect of accurately matching student requirements is achieved, compared with a technical scheme in which the states of the students cannot be responded in time in a traditional education platform, recommendation content can be adjusted in real time, and the recommendation efficiency is improved. The problem that personalized teaching resource configuration is not accurate is solved.

The invention relates to an online education interactive teaching method and device, equipment and a medium. The method comprises the following steps: acquiring student face image data and current teaching knowledge point data, and calculating student attention parameters according to the image data to obtain student attention parameter data; knowledge point association degree data is generated through the current teaching knowledge point data; dynamic teaching adjustment strategy data is generated based on the student attention parameter data and the knowledge point correlation degree data, teaching feedback operation is executed according to the strategy data, a teaching feedback execution result is obtained, and the teaching feedback operation comprises interface thermodynamic diagram superposition and micro-test insertion. According to the method, through the facial image data of the students and the teaching knowledge point data, not only can the attention of the students be accurately quantified and the association degree of the knowledge points be scientifically evaluated, but also a dynamic teaching strategy can be generated and feedback operation including thermodynamic diagram superposition and micro-test insertion can be executed, so that the pertinence, interactivity and effectiveness of online teaching are remarkably improved.

The invention discloses an intelligent teaching effect evaluation system based on student behavior data, and relates to the technical field of teaching effect evaluation, and the system specifically comprises a behavior data extraction module, a course classification module and a teaching quality evaluation module, the course classification module is used for extracting teacher behavior characteristics according to classroom speaking duration, speed and blackboard-writing frequency of teachers, setting teaching indexes based on the teacher behavior characteristics and classifying offline behavior data through the teaching indexes, and the teaching quality evaluation module is used for evaluating teaching quality of the teachers. The system is used for integrating offline behavior data of different curriculum types, setting offline teaching evaluation indexes, setting online teaching evaluation indexes according to online behavior data and comprehensively evaluating the teaching quality of teachers, and intelligent teaching quality evaluation is achieved by integrating online and offline data.

The invention discloses an online teaching method and system suitable for laser cutting, and relates to the technical field of laser cutting. The method comprises the following steps: S1, running a generated G code cutting file, pausing and recording a line number at a deviation position between a theoretical cutting point and an actual cutting position, selecting a preset operation mode for adjustment according to displayed data, and after adjustment is completed, covering an original file with edited data and reloading; or S2, a cutting program is input in a software editing interface, cutting parameters are set, the cutting parameters comprise the cutting speed and the process type, a cutting head is moved to a designated position to execute a cutting instruction, and by inserting related instructions and macro programs in a display list, definition and editing of all cutting contours are completed, and a G code program is generated. Through real-time deviation adjustment, a simplified operation process and a flexible adjustment mode, the precision, the adaptability and the operation convenience of the laser cutting process are improved, and the working efficiency and the cutting quality are remarkably improved.

The invention discloses a teaching assistance system and method based on online learning behavior data analysis, and relates to the technical field of online teaching assistance, and the method comprises the steps: obtaining online learning platform related data and historical classroom management data, calculating a student classroom concentration risk index according to the data, and judging whether a special mark exists or not; setting a monitoring period, adjusting according to the student marking condition, counting the duration of a first abnormal behavior by using face recognition, constructing a model to recognize the abnormal behavior, recording the screen content switching condition, counting related data after class, and calculating a first adjustment index; counting the post-class job completion rate and accuracy of the history, calculating a second adjustment index, and combining the second adjustment index to obtain a comprehensive adjustment index to adjust the post-class job difficulty; and finally, counting chapter throughout data, calculating a knowledge throughout proportion, and distributing corresponding after-class exercises for the students according to the knowledge throughout proportion. According to the teaching assistance method, on the basis of online learning behavior data analysis, the difficulty of after-class homework is dynamically adjusted, and after-class exercises are accurately distributed.

The present invention discloses an automatic extraction system and method for online teaching information based on cloud computing, which relates to the technical field of online course knowledge extraction, and includes the following steps: collecting the video subtitle file of the course video and the comment data of the course video to obtain the course-related text data; and performing keyword extraction to obtain the course-related knowledge point data; obtaining the playback-related data of historical users learning the course video, and performing classification and screening processing on the historical users to obtain relevant user data; and performing level division processing on the course-related knowledge point data to obtain the course-level knowledge point data; The present invention is used to solve the problem that the existing online course video knowledge point extraction technology cannot accurately divide the difficulty and importance of professional terms according to the actual situation of users when extracting and dividing the key points and difficulties of professional terms in the course video, and cannot be adjusted in time according to the change of the user state based on the teaching content.

The application discloses a kind of teaching and learning behavior data support under causally driven dynamic organization method and system of teaching resources, and the method comprises the following steps: obtaining online teaching and learning behavior data, the online teaching and learning behavior data include teaching activity data, student learning data and online education resource information;According to the online teaching and learning behavior data, resource access dynamic time sequence causality is modeled, and the causality information of online education resource access is determined;According to the causality information, the online education resource access heat is predicted by neural network;According to the online education resource access heat obtained by prediction, the file level teaching and learning behavior data support under causally driven dynamic organization process of teaching resources is constructed, to realize the causally driven dynamic organization of teaching resources under teaching and learning behavior data support.The application can effectively improve the education user data access efficiency, and can be widely applied in the field of computer technology.

The present invention discloses a webcam and alarm system for online teaching, specifically in the field of online teaching, which is used to solve the problem of online teaching quality analysis. Through image processing and deep learning technologies, the facial expressions and upper body movements of each student can be completely captured in the teaching video, improving the image processing efficiency and the accuracy of feature recognition, ensuring data integrity, and providing a reliable basis for behavior analysis and engagement assessment. Combining Haar cascade classifier, VGG-Face and SVM classification technologies, it accurately identifies and analyzes the emotions and behaviors of students, optimizes teaching content and methods, and enhances students' engagement and satisfaction. Using clustering algorithms to analyze the student participation time series, identify teaching peaks and troughs, evaluate the teaching effect through the teaching quality index, guide teaching adjustments, enhance the pertinence and effectiveness of curriculum design, improve teaching transparency and adjustability, and better respond to students' needs.

This invention discloses a method and system for application design transformation based on technological experience, relating to the field of digital technology. The method includes: collecting open-source technology product data via the internet to construct a digital technology experience center; providing online technology experiences for students to obtain experience monitoring data; designing online courses based on a technology application creation platform to obtain online teaching courses; teaching students to obtain technology application concept design proposals; conducting simulation verification and evaluation through a mechanism simulation system to obtain simulation evaluation results for the design proposals; presenting the technology application concept design proposals in a roadshow to obtain a design demonstration dataset; conducting online expert evaluation; and transforming the technology application concept design proposals into innovative achievements based on the evaluation results. This addresses the technical problems of limited teaching resources, limited practical scenarios, and restricted technological innovation capabilities in existing science and technology education, achieving the technical effect of enriching technological experiences and enhancing technological innovation capabilities.

To provide a system for supporting an examinee to study an examination by using generative artificial intelligence.SOLUTION: The system includes a user interface means for inputting a school of choice and basic information of an examinee, an examination question generation means for automatically generating examination questions based on the school of choice information of the examinee, an analysis means for analyzing a trial result answered by the examinee and visualizing an unskillful field and a weak point, an answer explanation means for generating and displaying an explanation based on an analysis result, a teaching material recommendation means for recommending an online teaching material suitable for a learning situation of the examinee, and a similar question generation means for automatically generating similar questions based on the unskillful field of the examinee.SELECTED DRAWING: Figure 1

This invention discloses an AI-based online teaching system for intangible cultural heritage (ICH) music, involving the intersection of educational technology, artificial intelligence, and digital preservation of ICH. It aims to address the pain points in ICH music transmission, such as teacher shortages, inaccurate analysis, cultural discontinuity, and poor adaptability. The system architecture consists of a user interaction layer, an AI analysis layer, and a resource support layer. Its core innovations are the collaborative operation of two engines in the AI ​​analysis layer: an ICH music feature analysis engine and a cultural context generation engine, and an adaptive teaching engine in the resource support layer, forming a closed loop of "analysis-feedback-teaching." The system achieves high-precision quantitative analysis of unique ICH skills and deep integration of cultural connotations, supports low-bandwidth and low-end device operation, adapts to grassroots scenarios, and promotes the transformation of ICH music from static archiving to dynamic transmission.

The invention discloses an online teaching interaction method and device, a storage medium and computer equipment. Comprising the steps that learning behavior information generated by a student based on an online teaching course is acquired, the learning behavior information comprises class monitoring information and a homework file, and the homework file comprises at least one homework question and homework content corresponding to the homework question; comparing the homework content with the standard answers of the homework questions, and determining homework completion condition information; processing the homework completion condition information through a scoring model associated with the type to which the homework question belongs, and determining the homework score of the homework question; and based on the homework question, the homework score and the class monitoring information, learning suggestion information is fed back to the student terminal. According to the method, the accuracy of student homework error analysis is improved, the teaching quality can be improved, personalized tutoring and learning suggestions can be made, and the students can know own learning conditions in time and dynamically adjust the tutoring content and difficulty so as to adapt to the requirements of each student.

The present application relates to the technical field of teaching quality evaluation, in particular to a service quality dynamic evaluation method for an online teaching platform, comprising: collecting learning behavior data of an online education platform based on the scene of teaching task access; decomposing the obtained learning behavior data in multiple modal scenes to determine the scene feature matrix under each scene; integrating real-time learning behavior data based on the scene feature matrix under each scene to determine the real-time interaction degree of learning behavior, and constructing a learning behavior portrait according to the response time under real-time interaction; calculating the group response time of each teaching task based on the obtained learning behavior portrait, and determining the interaction mode exhibited by the teaching task according to the task completion status corresponding to the teaching task; and based on the interaction mode exhibited by the teaching task, using the isolation forest algorithm for time series regression verification to determine the evaluation score of each user. The efficiency and accuracy of data processing are improved.

The invention discloses an interactive intelligent teaching system based on multiple modes, and relates to the technical field of intelligent teaching management systems. The system comprises a multi-modal perception layer, a semantic and feature fusion engine, a cognition-emotion two-dimensional analysis module, a strategy decision center and a multi-dimensional interaction execution terminal. Multi-source data of vision, hearing, physiology, behaviors and the like are synchronously collected in real time, and an improved MLP regression network is utilized to construct a cognition-emotion two-dimensional state model of a learner; when the state of the student is monitored to be abnormal, the strategy decision center dynamically generates an optimal teaching path in combination with a knowledge graph and a historical successful case, and drives a virtual digital teacher to execute immersive intervention; according to the method, the problems of emotion perception deficiency, feedback lagging and insufficient individuation in traditional online teaching are solved, and intelligent closed loop and adaptive optimization of the teaching process are realized.

The invention provides a method for identifying and displaying key subsequences of a voice sequence. The method comprises the following steps: S510, acquiring a current voice sequence; s520, identifying a first number of pause points contained in the current voice sequence, and activating a second number of key sub-sequence identification processes; s530, segmenting the current voice sequence into a third number of voice subsequences by taking the first number of pause points as a unit; s540, identifying whether each voice sub-sequence contains a predetermined key sub-sequence or not in parallel by adopting a second number of key sub-sequence identification processes; s550, if certain two adjacent voice subsequences contain the same predetermined key subsequence, combining the two adjacent voice subsequences into one voice subsequence; and S560, playing the voice subsequences in sequence. The invention further discloses the corresponding electronic equipment and system. According to the invention, the key sub-sequence of the voice sequence can be identified and displayed, the key attention during the online teaching of the voice in the chemical field is facilitated, and the system resources are saved.

The present invention relates to the field of online education technology and discloses an online training education informationization teaching method based on big data, comprising the following steps: Step S1: collecting data from an online teaching platform, a real-time assessment system, an emotion monitoring device, and user interaction logs, and performing cleaning, standardization, noise filtering, and feature extraction on the data; Step S2: constructing a student state vector based on the data from Step S1 to implement dimensionality reduction processing; and Step S3: establishing a state-action model. A personalized teaching recommendation system based on big data is adopted. By collecting student learning data in real time and utilizing big data technology to analyze and optimize learning paths, the system achieves the technical effect of accurately matching student needs. Compared to the technical solutions in traditional education platforms that cannot respond to student status in a timely manner, the present invention can adjust recommended content in real time, solving the problem of inaccurate allocation of personalized teaching resources.

The application relates to the technical field of intelligent interaction, in particular to an online teaching interaction system based on a cloud platform, wherein a data acquisition module is used to determine the action disturbance route complexity and action repetition degree of a teaching end; a data analysis module is used to calculate an action complexity representation parameter and divide an action abnormality tendency; an interaction processing module is used to compare the action start frame and the action end frame of each node of the teaching end video and the learning end video for weak abnormality tendency, determine the learning end action state, determine a plurality of extraction intervals for strong abnormality tendency actions, compare the corresponding predicted node action graph of the extraction interval with the actual node action graph in the learning end video frame in the extraction interval, and determine the learning end action state; and a state reminding module is used to determine whether a state correction reminding needs to be sent. The application compares and analyzes learning actions, and improves the interaction efficiency and analysis accuracy.

An online teaching system based on a hierarchical teaching idea belongs to the technical field of online teaching, and comprises a management platform, a teacher terminal and a student terminal, the management platform formulates different hierarchies for each subject and teaching resources and standards that different hierarchies should have for teaching; the teacher terminal submits corresponding teaching resources according to a teaching resource recruitment request issued by the management platform, and the student terminal carries out comprehensive evaluation according to the learning ability, learning basis and learning purpose of learners of students and carries out teaching resource adaptation in combination with likes of teaching styles. According to the online teaching system based on the hierarchical teaching concept, teaching resources meeting the requirements of learners can be screened out for the learners according to the learning characteristics, the learning basis, the teaching mode and the learning purpose of the learners, and the learners can master related knowledge.

The application provides a teaching big data intelligent analysis method. The teaching big data intelligent analysis method comprises the following steps: obtaining teaching history data based on big data technology; obtaining learning characteristic data of each student based on the teaching history data analysis; classifying the students according to a preset classification rule and the learning characteristic data to obtain a student classification result; obtaining student participation data of online teaching and offline teaching of a target teacher; and performing teaching analysis on the target teacher to determine an online teaching and offline teaching evaluation result of the target teacher. The application analyzes the teaching history data of the teacher, considers the whole situation, analyzes the preference of each student for the online teaching and offline teaching, and combines the participation data of the students in the online teaching and offline teaching of the teacher in the actual teaching process to reasonably analyze the teaching of the teacher, so that intuitive teaching feedback information is provided for the teacher, and the teaching method of the teacher is facilitated to be improved.