127 results about "Online course" patented technology

The invention discloses an online course learning management method and system based on a knowledge graph, and the method comprises the steps: S1, analyzing course data, extracting knowledge points and an association relationship, and generating an initial knowledge graph signal containing knowledge point nodes and association edges; s2, collecting user interaction behavior data and extracting behavior features to generate behavior feature vector signals associated with knowledge point nodes; s3, detecting an abnormal learning mode, triggering a knowledge graph weight updating signal, and dynamically adjusting the associated edge weight; s4, updating the signal according to the initial knowledge graph signal and the knowledge graph weight; and S5, responding to the path optimization signal, and generating an interface sorting adjustment signal and a content push signal. According to the air quality intelligent monitoring system based on sensing data feedback, the problem that a traditional online course management system is single in data acquisition dimension and static and solidified in knowledge dependency relationship can be solved.

The invention discloses an online curriculum recommendation method and system based on a large language model, and relates to the technical field of curriculum recommendation. The method is based on multi-level analysis of policy texts, the association between dominant changes and hidden skill requirements is accurately extracted, and the one-sidedness of traditional keyword matching is avoided; curriculum features with attenuation weights are generated through cross-modal alignment, recommendation priorities are dynamically adjusted in combination with policy release time and content update frequency, and recommendation deviation caused by curriculum update lag in the policy transition period is solved; an LLM is used to construct a cross-skill domain incidence matrix, and the secondary influence of policy change is automatically deduced; a virtual interaction data generation module is adopted to simulate a typical user behavior path, the stability of a recommendation system is maintained in the absence of real data, and the model iteration period after policy mutation is shortened.

The invention provides an environment art design major online education learning path recommendation system, and relates to the technical field of online education, and the system comprises a user information obtaining module which is used for obtaining the basic information, learning background, interest preference and learning target of a user; the course resource database module is used for storing various online course resources of the environmental art design major, including course videos, teaching courseware, practice items and case analysis; and the learning behavior analysis module is used for analyzing behavior characteristics of the user in the learning process, including learning styles, learning habits and content preferences, and acquiring a learning behavior mode of the user. According to the method, the user information is comprehensively acquired, the learning behaviors are analyzed, the personalized learning path is determined, and real-time evaluation and adjustment are performed, so that the learning efficiency, satisfaction and achievement of the user are effectively improved.

Systems and methods are provided for custom online course based on matching content with a course syllabus by parsing a syllabus document submitted by a teacher into course segments and matches course resources with appropriate course segments. The course segments can be automatically placed into an online course structure. The appropriate course materials can then be automatically matched to each course segment. Teachers may then approve, edit, or delete course segments and their matched materials. This approval process may improve the syllabus parsing and content matching and content generation using machine-learning models, such as large language models.

The invention relates to the technical field of online education, and discloses an online course learning management method based on a knowledge graph. The method comprises the following steps: acquiring multi-modal learning behavior data of a learner, and extracting a deep learning state vector reflecting knowledge understanding depth, learning input degree and cognitive confusion through semantic fusion; and dynamically calculating and updating the logical relationship strength among the knowledge points in the course knowledge graph by using the vector, so that the knowledge structure can adaptively evolve along with the actual cognitive state of the learning group. And generating a real-time personalized learning path based on the updated knowledge graph and the current state vector of the learner. Meanwhile, according to cognitive confusion features in the state vector, intervention measures such as pushing of remedial resources, adjusting of content sequence or starting of self-adaptive testing are triggered in real time. According to the method, the dynamic optimization of the knowledge graph and the accurate and immediate response of learning intervention are realized, and the adaptability and management efficiency of online learning are improved.

The invention relates to the technical field of examination and evaluation, in particular to a student examination and evaluation method and system based on an AI big data model. Comprising the following steps: S1, collecting historical examination and evaluation data, establishing an AI big data model, and then dividing the AI big data model into a learning distribution module, a training distribution module, an examination module and a comprehensive scoring module; s2, a learning distribution module analyzes the predicted learning duration of each student, performs learning course generation according to the predicted learning duration in combination with the student basic data, and divides the learning courses into online courses and offline courses; the AI big data model is used for analysis and prediction, students can be evaluated more accurately, a more reasonable learning plan can be made for the students by predicting the learning duration, meanwhile, a teacher can better arrange teaching resources, and the teaching efficiency is improved. The learning efficiency score and the examination score can accurately reflect the knowledge mastering degree and the application ability of the student.

The invention discloses an online curriculum homework scoring system based on multi-modal multi-level feature joint learning, belongs to the technical field of information processing, and solves the problems that most of current mainstream homework automatic scoring methods are based on text content, and results obtained by the homework automatic scoring methods may be incomplete in evaluation and poor in homework scoring efficiency. And the learning motivation of students is reduced. Comprising a text feature extraction module, a multi-modal feature matching module, a multi-level feature fusion module, a job analysis module and a learning behavior application module, and the output end of the text feature extraction module and the output end of the multi-modal feature matching module are connected with the input end of the multi-level feature fusion module. The output end of the multi-level feature fusion module is connected with the input end of the homework analysis module, and the homework analysis module is connected with the learning behavior application module; the text feature extraction module is used for extracting feature vectors of text content from different levels. The method supports more comprehensive formats, and the obtained homework score is more comprehensive and more humanized.

The invention discloses a classification retrieval method for an online course learning system, which comprises the following steps of: acquiring text data of all courses from an online course platform, and carrying out data preprocessing on the collected text data; extracting course keywords from the text data after data preprocessing, and distributing corresponding course tags according to the course keywords; converting the text data of all courses and the user query text data into vectors, and calculating the text similarity; screening out candidate courses according to the course labels and the text similarity; calculating a comprehensive score of each candidate course for the screened candidate courses in combination with user behavior data; and sorting the candidate courses in a descending order according to the comprehensive score, and displaying the sorted retrieval results to the user in sequence. According to the method, efficient and accurate course retrieval and personalized recommendation are realized, and the learning experience of the user is improved.

A dynamic preference perception online course recommendation method based on a Hawkes process and a TimeXer mechanism comprises the steps that user data and course data are acquired, and the data comprise user attributes, historical interaction sequences and course attributes; generating a context semantic embedding vector by adopting a pre-trained BERT language model; projecting an embedded vector to a unit hyperspherical space by using a spherical Gaussian model, and measuring similarity by using a spherical distance so as to relieve the problems of data sparsity and cold start; a user dynamic preference model integrating a Hawkes process and a TimeXer attention mechanism is constructed, the Hawkes process calculates event occurrence intensity to capture self-excitation characteristics, the TimeXer attention mechanism is adjusted through the intensity information, and weighted user representation reflecting preference dynamic changes is generated; and according to the similarity between the weighted user representation and the projected course embedding vector, calculating a recommendation score, and generating a personalized online course recommendation result. According to the method, the accuracy of online course recommendation is improved by accurately capturing the dynamic preference of the user and effectively processing the sparse cold start problem.

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 provides an online course MOOC learning prediction method based on heterogeneous feature fusion, and belongs to the field of computer-aided intelligent education. The method comprises the following steps: acquiring an MOOC data set, and preprocessing the data set to obtain a test set; constructing an IHFNet network comprising a multi-agent adaptive static feature selector module, a hierarchical time sequence feature extractor module and a heterogeneous feature fusion module; a multi-agent adaptive static feature selector module screens key static features; the hierarchical time sequence feature extractor module extracts behavior time sequence features with high discrimination ability; the heterogeneous feature fusion module carries out adaptive fusion on the key static features and the behavior time sequence features and carries out classification prediction; an IHFNet network is trained; and collecting MOOC data of a to-be-predicted learner, and inputting the MOOC data to the trained IHFNet network for learning risk prediction. According to the invention, modeling is carried out by fusing the static features of the learner and the behavior time sequence features, the feature representation ability of the learner is enhanced, and the learning risk prediction effect is improved.

The invention discloses a database-based curriculum data online retrieval method, and relates to the technical field of curriculum data online retrieval, and the method comprises the following steps: S1, constructing a curriculum database table which comprises the following fields: curriculum names, curriculum starting time, difficulty levels and curriculum keywords; according to the course data online retrieval method based on the database, an overall technical system is formed through multi-dimensional retrieval conditions, weight sorting and fuzzy extension to intention perception and performance optimization; intelligent and efficient retrieval is realized by combining fuzzy rule extension, user intention perception and multi-layer index optimization; the problems of inaccurate retrieval and slow response speed in a large-scale course data scene are solved, and the user experience is remarkably improved; the defects of an existing method in the process of processing complex requirements are overcome, and the retrieval accuracy and efficiency are remarkably improved.

The invention relates to a multifunctional network class recorded audio optimization device, and the device comprises an audio signal collection system which is used for collecting audio signals in a teaching environment; the audio preprocessing system is used for converting the analog audio signal into a digital signal and performing gain adjustment; the audio optimization system is used for carrying out noise suppression, echo cancellation and dynamic range control on the audio signal; the voice conversion system is used for converting the optimized audio signals into texts and generating real-time subtitles; the audio data transmission system is used for dynamically selecting a coding and decoding protocol according to a network condition and transmitting audio data; the data secrecy and protection system is used for carrying out encryption and access control on the audio data; and the information display and interaction system is used for providing a visual teaching interface and supporting a teacher-student interaction function. The device can improve audio quality, enhance teaching interactivity and guarantee data safety.

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 relates to the technical field of course review analysis, and provides an online course review sentiment analysis method based on multiple models and related equipment.The method comprises the following steps: calculating the complexity score of user review data, and determining the number of sentiment analysis models corresponding to the user review data according to the complexity score; when the number of sentiment analysis models is multiple, multiple sentiment analysis models equal to the number of sentiment analysis models are called, and the user review data is subjected to sentiment analysis by using each sentiment analysis model to obtain a sentiment analysis result corresponding to each sentiment analysis model; a multi-objective optimization function is constructed based on all the sentiment analysis results, and the multi-objective optimization function is solved to obtain an optimal weight corresponding to each sentiment analysis result; and all the sentiment analysis results are fused according to all the optimal weights to obtain a final sentiment analysis result of the user review data.The method can improve the accuracy of sentiment analysis of user reviews.

The invention provides a method and system for using a machine learning technique for identifying and outputting most relevant articles for a learning program.

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.

The invention relates to the technical field of course evaluation, in particular to an ancient Chinese book online course evaluation system. The course data analysis module determines the learning condition of each section according to the answer data of each section of the course and the viewing data of each section of the course so as to determine the learning condition of the course, and the evaluation unit evaluates the Chinese ancient book online course according to the learning condition of the course. An adjustment direction analysis unit obtains a course answering accuracy change tendency, determines a course difficulty level adjustment direction in combination with a course section viewing data deviation value change tendency, identifies feature sections, determines a feature section adjustment time length according to the occurrence frequency of keywords in remaining sections and in combination with an answering accuracy rate, and adjusts the course difficulty level according to the feature section adjustment time length. The question answering data and the watching data are analyzed, the influence of relevance between the knowledge points is considered, the accuracy of knowledge point difficulty level judgment is improved, and a course is reliably evaluated.

The invention provides an online course recommendation method and device for real-time teaching, and the method comprises the steps: obtaining real-time teaching information which is used for reflecting an online course obtaining demand in a real-time teaching process; based on the real-time teaching information, determining a first course feature of a target online course corresponding to the online course acquisition demand, the first course feature being used for reflecting course content of the target online course; and determining a preset online course with a second course feature matched with the first course feature in a preset online course set, and taking the preset online course as the target online course. According to the technical scheme, the manual operation in the real-time teaching process is simplified, the convenience of electronic teaching is improved, and the course quality can be improved.

The invention relates to an online course learning system for vocational education, and the system comprises an authority management module which is used for managing the registration and login of a user, and carrying out the distribution and authority configuration of a user role; the resource uploading and downloading module is used for uploading and transcoding teaching resources and storing the teaching resources; the knowledge block database is used for storing names of crop subjects and interference factors; the course knowledge graph management module is used for constructing, editing and maintaining a structured graph of a course, extracting the name of each independent teaching resource, extracting a plurality of keywords from the names of the teaching resources to form corresponding knowledge blocks, storing the knowledge blocks, and extracting the keywords from a knowledge block database; the course search module retrieves corresponding courses according to the keywords in the knowledge blocks; and the exercise examination management module is used for providing an exercise mode, examination paper composition and score filing functions according to the teaching resources in the course knowledge graph management module.

The invention discloses a big data configuration method and system for an intelligent campus management service platform, relates to the field of intelligent campuses, solves the problem of poor teaching resource configuration effect of the existing intelligent campus management service platform, and comprises the following steps: S1, respectively obtaining a first learning resource matching coefficient and a second learning resource matching coefficient, the method comprises the steps of S1, obtaining student work and rest monitoring data, S2, carrying out online course concentration evaluation on a target student to obtain a third learning resource matching coefficient, and S3, obtaining a resource allocation evaluation coefficient by analyzing the first learning resource matching coefficient, the second learning resource matching coefficient and the third learning resource matching coefficient, and performing teaching resource configuration on the target student according to the resource configuration evaluation coefficient. According to the invention, the configuration pertinence of the teaching resources can be improved.

The invention relates to the technical field of big data intelligent recommendation, in particular to an online course intelligent recommendation method and system based on big data. According to the method, the sensitive sub-features for each user side can be analyzed based on the change of each sub-feature of the behavior tendency feature of the user side, the online course intelligent recommendation efficiency is improved, sensitive tendency categories are distinguished by analyzing the course sensitive tendency, and the online course pushing frequency of each user side is recommended in a customized manner. According to the method, whether the pushing mode is adjusted or not is analyzed, the accessed online course category can be determined by determining the online course page accessed by the user side, and the course pushing frequency for each online course category can be effectively adjusted through each accessed online course category, so that the efficiency of the pushing frequency is further improved.

To provide a health management system for rationally and organically linking meals, stretching (training) and a trial sequence according to a health management program for 45 days.SOLUTION: In a health management system in which a student can access a server from a student terminal connected to a network, select an online 45-day course, and attend the course online, a health management program for daily meals, sleep, stretching, or training is provided, a meal instruction image and a stretching moving image or a training moving image are provided, and health management is performed by a combination of the meal instruction image and the stretching moving image or the training moving image via the student terminal.SELECTED DRAWING: Figure 2

This invention relates to the field of semantic understanding technology, specifically to a method for semantic understanding and intelligent question answering in online courses for mobile terminals. The method includes the following steps: parsing the question text to identify subject-verb-object semantic relationships and generate corresponding structural expressions; collecting concept and behavioral terms from a knowledge base for semantic comparison and generation of course semantic expressions; reading positional analysis to generate question relationship expressions based on concept associations; collecting subtitles and knowledge structures for comparative association and generation of knowledge correspondence expressions; and extracting subtitle fragments and integrating playback positions to generate a processing scheme. In this invention, deep semantic comparison and positional analysis are performed by linking object terms in the course knowledge base, overcoming the limitations of shallow character matching to achieve deep question intent restoration. Simultaneously, subtitle content and chapter structure are collected for contextual analysis, and subtitle fragments are deeply integrated with video playback progress to construct a response mechanism that highly fits the actual teaching context, completely changing the isolated retrieval mode and eliminating interaction breaks.

The invention discloses a shared intelligent yoga management system, relates to the technical field of intelligent management, and realizes combination of five functions of member management, data analysis, online lesson reservation, operation management and venue management by setting related module combinations. An intelligent management platform with unified data sharing, flow standardization and real-time feedback is formed; the modules are in data intercommunication, so that the accuracy and consistency of key data such as member information, order records and reservation conditions are ensured, and online service and offline operation are closely linked, thereby remarkably improving the response speed and management efficiency of the whole service; through multi-dimensional report analysis, powerful data support and decision basis are provided for managers, resource configuration and operation processes are effectively optimized, and operation risks and management cost are reduced.

The invention belongs to the technical field of teaching platform resource recommendation, and discloses an intelligent teaching platform resource recommendation management method. Comprising the steps of collecting explicit data, implicit data and situation data; according to the implicit data, obtaining a long-term interest value and a short-term interest value of the user for the online courses, according to the long-term interest value and the short-term interest value, obtaining a comprehensive interest value of the user for the online courses, and according to the comprehensive interest value, recommending the corresponding online courses to the user; obtaining a concentration value of the user for the offline course and a knowledge level value of the user according to the situation data, setting a concentration value threshold range, obtaining a knowledge level grade of the user according to the knowledge level value of the user, and when the concentration value exceeds the concentration value threshold range, recommending the corresponding offline course to the user according to the explicit data. When the concentration value is within the concentration value threshold range, recommending a corresponding offline course to the user according to the knowledge level grade; and the accuracy and adaptability of teaching platform resource recommendation are further improved.

The invention discloses an online course generation method, system and device, a medium and a product, and relates to the technical field of artificial intelligence education, and the method comprises the steps: employing a double-link subtitle generation fusing mechanism, and obtaining audio and video subtitles from audios and videos of various types of teaching resources; generating a curriculum introduction and a basic question bank by using a large language model based on subtitles in combination with documents and pictures, and intercepting OCR (Optical Character Recognition) results of first n frames of audios and videos to generate a teacher introduction; determining the knowledge point density of each time point according to the audio and video subtitles, generating interactive test questions in a time period in which the knowledge point density ranks first m%, popping up when the video is played, checking the answering state of the learner and controlling the playing permission; and constructing a three-level knowledge graph, binding graph nodes with teaching resource position information, and establishing a bidirectional mapping relationship with a chapter structure. The problems that in the prior art, information extraction efficiency is low, content quality is difficult to guarantee, interactivity is insufficient, and a graph knowledge structure and a traditional structure are disjointed can be solved.

The invention discloses an online course teaching decision-making auxiliary method and system based on multi-modal artificial intelligence, and belongs to the crossing field of education technology science and artificial intelligence technology, and the method comprises the steps: obtaining student comment data from an online course platform, carrying out the preprocessing, and constructing a labeling data set; based on a pre-trained text classification model, comments are classified according to preset dimensions, and multi-dimensional visual analysis of teaching feedback is realized in combination with sentiment analysis and keyword extraction technologies; aiming at the negative emotion comments, guiding a large language model through prompt language engineering to carry out deep semantic analysis, and identifying specific problems and roots thereof; based on the learning analysis technology framework, a targeted teaching optimization scheme is generated; the system is composed of a data layer module, an analysis layer module and a decision layer module and executes corresponding steps in the method. The system provided by the invention can be embedded into an existing online learning platform to serve as a teacher end auxiliary tool, supports normalized and periodic teaching decision and optimization, and has the characteristics of high practicability and easiness in popularization.

The application belongs to the technical field of cloud computing and information security, and discloses an education resource sharing method and system based on cloud computing. The method carries out structural analysis on online course resources uploaded to a cloud platform, defines a user role attribute model based on an education scene, constructs an access control policy tree based on an attribute-based encryption mechanism, adopts a distributed hash table to store fragments of the resource ciphertext, and generates a resource positioning index. When a course resource access request is received, the education attribute set of the requesting user is verified, and a learning path perception mechanism is combined to determine whether the user meets the decryption condition and chapter unlocking condition of the access control policy tree. The course access record, learning duration, test score and credit acquisition record of the learner are recorded to an education alliance chain to generate a verifiable learning record certificate, thereby realizing access tracing and auditing. The technical problems of coarse education resource access control granularity, weak anti-stealing broadcast capability and low learning record credibility are solved.

The invention discloses an online course quality evaluation system and method based on a knowledge graph. The method specifically comprises the following steps: S1, constructing a semantic relation graph; s2, generating a hyperbolic node vector and a hyperbolic distance; s3, extracting structure connection attributes; s4, generating a path consistency scoring factor; s5, calculating a basic attention weight; s6, generating a behavior disturbance offset; s7, constructing a structure disturbance factor and generating a disturbance fusion attention weight; s8, entropy regularization updating is executed; and S9, generating a structural robustness index and outputting an evaluation result. Unified improvement of course structure relation accurate expression and behavior association evaluation is achieved, and the method can be applied to various online course quality analysis scenes.