61 results about "Online course" patented technology

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.

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 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 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 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.

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 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.

This disclosure provides a method, apparatus, electronic device, and storage medium for analyzing learning progress. The method includes: acquiring a user dataset from a platform; determining an instant learning performance score for each learner based on their learning outcomes and learning objectives within the user dataset; acquiring valid course selection information for all learners; determining a dynamic relationship network based on the valid course selection information and the instant learning performance score; acquiring the clustering characteristics and community structure within the dynamic relationship network; and determining the learning progress based on the clustering characteristics and community structure. Determining the learning progress through a dynamic network based on the instant learning performance score effectively grasps the learning progress patterns of open online courses, thereby enabling real-time monitoring of the learning progress of all users on the platform. This plays a crucial role for educators in real-time evaluation of teaching quality, control of teaching direction, course recommendations, and learning progress early warnings.

The application provides an online course dropout prediction method based on context-aware timing deep network, and belongs to the technical field of computer-aided intelligent education. A prediction data set containing static features and dynamic behavior features is collected; global statistical features are extracted from the dynamic behavior features, and a dynamic behavior matrix is constructed according to the dynamic behavior features; an online course dropout prediction network is constructed; the online course dropout prediction network is trained by using a training set, a binary cross-entropy loss is used as a loss function, and an L2 regularization term is introduced; data of a learner to be predicted is collected and input into the network for dropout risk prediction. The application realizes learner-course context and timing behavior feature fusion based on early data to improve the dropout risk prediction effect.

The invention discloses an online Chinese character learning system for overseas students, which comprises a teacher end, a student end and a cloud server, and is characterized in that the teacher end comprises a password login module, an online class teaching module, a homework push-down module and a batch paper review module; the password login module is used for providing a login path for the teacher; the online class teaching module is used for carrying out online teaching on the overseas students; the homework push-down module is used for pushing after-class homework to the overseas students; the batch paper review module is used for examining and approving after-class homework uploaded by the overseas students; according to the invention, students can conveniently learn Chinese characters on line, a teacher can carry out one-to-many teaching through the teacher terminal, can carry out network class interactive teaching and push down homework, the teacher can review the homework, the students can conveniently review and consolidate learning contents, and the learning effect is better.

The system described in this document consists of a student terminal, an instructor terminal, and a content server connected through a wide area network. The content server contains course content multimedia. which is downloaded and displayed on the student terminal's digital display. During the course. the server can receive and store upload data from the student terminal. which may include images or videos. This upload data is then made available to the instructor terminal. The instructor terminal has a multimedia editing user interface that displays the upload data and allows the instructor to add markup data at specific XY coordinates on the screen. The student terminal can display the upload data with the added markup. The system is designed to regenerate the augmented video in native format to allows the student terminal to download and display the augmented video without requiring additional plug-ins or software.

The application discloses an online course recommendation method and device based on class concentration detection, which first collects basic information data of students, vectorizes the basic data of the students, and generates an initial recommendation list by using a user-based collaborative filtering method; then, concentration detection is performed on video records of the students during class, the proportion of the concentration state is calculated, and the proportion is taken as a score of the students on the course; finally, the existing score is converted into a matrix form, a recommendation result is obtained by using a matrix decomposition-based collaborative filtering method, and the recommendation result is fused with the initial recommendation result to obtain a final course recommendation result. The application combines the concentration of the students during class, can better feedback the interest degree of the students on the course, and obtains a more accurate student-course score; and the application can improve the sparsity of the student-course score matrix, and has a better effect when the matrix decomposition-based collaborative filtering method is applied.

This invention discloses an intelligent online course recommendation method and system based on big data analysis, comprising the following steps: S1, collecting multi-source data and preprocessing it; S2, extracting learning state features, constructing course nodes and course connection edges, and building a course knowledge graph based on the association strength; S3, performing vector representation learning in the course knowledge graph, constructing a course transition probability matrix, and determining the initial recommendation set corresponding to the current learning state; S4, performing node propagation calculation in the course knowledge graph starting from course nodes, and screening the candidate course set; S5, performing candidate path search and sorting the candidate paths to form a recommendation priority sequence; S6, compiling the course recommendation list and collecting feedback data to incrementally update the course knowledge graph. This invention enables collaborative analysis in the online course recommendation process, improving the matching accuracy of course recommendations.

1. The name of the design product: graphical user interface of cloud service platform management and display of electronic equipment. 2. The use of the design product: an electronic device. 3. The design points of the design product: in the graphical user interface. 4. The picture or photo that best indicates the design points: front view. 5. The use of the graphical user interface: for providing corresponding cloud services for customers. 6. The human-computer interaction mode of the graphical user interface: the front view is the home page of the cloud service platform, and all functions are overviewed and displayed; clicking "online courses" at the top of the front view obtains an interface change state diagram, and multiple courses are provided for users to learn. "XX" in the design interface represents text and / or numbers and / or letters and / or symbols, and the gray block is a replaceable content picture.

The invention relates to an online curriculum learning path recommendation method. The method comprises the following steps: S100, constructing a tree-shaped graph directory of online learning curriculums; s200, acquiring learning habit data of the student, wherein the learning habit data comprises learning duration, learning frequency, preference resource type, knowledge point mastering degree, wrong question distribution and learning period preference; s300, based on the tree-shaped graph directory and the learning habit data, calculating recommendation priorities of all course nodes through a learning guide formula, and generating a personalized learning path; and S400, the generated personalized learning path and the tree-shaped graph directory are displayed in an associated manner, students are guided to sequentially complete learning of course nodes of corresponding levels according to recommendation priorities, the learning progress is tracked in real time, and the recommendation priorities are dynamically updated.

The present application relates to the technical field of online education, in particular to a learning progress advancing method based on online higher education courses, comprising: monitoring the learning progress of users in real time, capturing users with learning progress lag according to the learning progress of users in online courses; monitoring the learning activity of users in real time, determining the online course learning supervision group according to the learning activity of users in online courses and the capture result of users with learning progress lag, the present application sets the online course learning supervision group and supervision queue through monitoring the learning progress and activity of users, thereby determining the service target of the method, further adopting the mode of mutual binding of users with leading learning progress in online courses and the service target of the method to bring certain incentive effect to the service target of the method, and configuring the online course learning prompt period to continuously prompt users to carry out online course learning, effectively advancing the learning progress of each user in online courses.

The invention discloses an online course failure prediction method. The method comprises the following steps: S1, data preprocessing; s2, screening input and output variables; s3, constructing an educational heterogeneous information network model, wherein the educational heterogeneous information network comprises a bidirectional long short-term memory network, a feedforward attention mechanism, a multi-layer perceptron and a bilinear interaction structure; fusion modeling of multivariate time series and table type features is realized through an educational heterogeneous information network model, so that course failure risk prediction is realized. According to the method, the prediction efficiency is remarkably improved, the decisive influence of the later-stage learning input and accumulated scores of the students on the outcome can be revealed through an interpretable mechanism, and a quantitative basis is provided for hierarchical intervention and resource allocation of online courses.

1. The name of the design product: graphical user interface of online courses for electronic devices. 2. The use of the design product: for electronic devices. 3. The design points of the design product: the graphical user interface displayed on the screen. 4. The picture or photo that best indicates the design points: front view. 5. The use of the graphical user interface: for users to browse, watch online courses, or register for training activities, or pay for online professional title declaration, etc. 6. The human-computer interaction mode of the graphical user interface: the front view, interface change state diagram 1 is the main interface for users to browse courses and training activities, including live open courses, professional title evaluation services, offline special training, continuing education courses, and online high-quality courses; scrolling down the mouse in the front view enters interface change state diagram 1; clicking the 【view more】 button of offline special training and continuing education courses in interface change state diagram 1 enters interface change state diagram 2, and scrolling down the mouse in interface change state diagram 2 enters interface change state diagram 3; interface change state diagram 2 and interface change state diagram 3 are training activity list pages.

The invention discloses an automobile technology online learning platform based on online and offline fusion, which relates to the technical field of online education and comprises an identity management module, a path generation module, a task scheduling module, a feedback correction module and a data aggregation module, and performing centralized configuration on account permissions in the multi-role portal and the user center, generating a corresponding identity list according to use scenes of students, teachers and administrators, and outputting matched course entrances and practical training entrances according to the identity list. According to the invention, through multi-role identity management and dynamic learning path generation, continuous connection of online courses and offline practical training is realized, and unification and coherence of the teaching process are guaranteed. Through process data aggregation and feedback closed-loop operation, dynamic association of learning states, practical training results and teaching feedback is established, targeted improvement guidance is generated, a learning path is updated in real time, and teaching monitoring precision and learning improvement efficiency are improved.

The application relates to the technical field of network class monitoring, in particular to a network class learning behavior monitoring system based on AI, which comprises a data collection module, a behavior monitoring module, a recommendation analysis module, a range determination module and a range optimization module. The recommendation analysis module is used for determining a network class recommendation state according to the number of abnormal behavior users and the course difference degree of the abnormal behavior users, and determining a recommendation mode according to the network class recommendation state to determine a recommendation item. The range determination module is used for determining a selection priority coefficient of each network class to be selected according to a graph correlation degree and a group mapping correlation degree, and determining a network class recommendation range based on the network class recommendation quantity corresponding to the recommendation item and the selection priority coefficient. The range optimization module is used for determining whether to perform range optimization according to the recommendation range freshness of the network class recommendation range and a negative feedback coefficient. The network class recommendation module is used for performing network class recommendation on each recommendation item based on the network class recommendation range. The application can guarantee the accuracy of behavior monitoring and the intelligent level of system recommendation in a complex network class environment.

The application discloses an online multi-modal resource mixed recommendation method fusing a classroom behavior sequence, and comprises the following steps: step 1, frame images are collected from a video, and classroom behaviors in the frame images are labeled; step 2, multi-modal knowledge ontology modeling is carried out; step 3, a student classroom behavior is recognized and represented based on a YOLO-V5 network model; step 4, a reconstructed behavior feature sequence is trained through a Baum-welch algorithm to obtain a multi-state GMM-HMM classification model based on a classroom behavior; and step 5, a mixed recommendation model is established by introducing three types of data, namely, classroom behaviors, cognitive levels and learning styles, and a resource recommendation list TOP-N is given in combination with a collaborative filtering algorithm. The application introduces classroom behavior data to enrich learner representation information, so that more accurate recommendation of online course resources is realized.

The invention discloses an online course quality evaluation platform, which relates to the technical field of online education and comprises a multi-source data acquisition and fusion module, a quality evaluation index modeling module, an analysis and comprehensive evaluation calculation module, an evaluation result visualization module and an application interface module. Through the synergistic effect of multi-source data fusion and a dynamic weighting algorithm, an evaluation result has scene adaptability, a multi-source data acquisition module integrates data such as learning behaviors, course content and technical logs, dimension features are extracted through a feature engineering unit, various data information is deeply mined, the actual condition of a course is comprehensively reflected, and the evaluation result is more accurate. According to the dynamic weighting algorithm, index weights are automatically adjusted according to course types, dynamic adjustment of the weights is achieved through comprehensive analysis of factors such as the course types, audience groups and education objectives, evaluation results can be accurately matched with different education scenes through combination of the dynamic weighting algorithm and the dynamic weighting algorithm, and dynamic alignment of evaluation standards and course attributes is achieved.

The invention relates to the technical field of online education, and discloses an online course intelligent auxiliary platform based on data analysis, and the platform comprises a user data collection unit which is used for obtaining login data, content interaction data and evaluation exercise data of a user; the user state analysis unit is used for judging the learning state of the user according to the login data and the content interaction data; the learning content analysis unit is used for judging the learning completion degree of the user according to the learning state of the user and the content interaction data; and the learning assisting module is used for pushing supplementary learning content according to the learning completion degree of the user and the evaluation exercise data. The login data and the content interaction data of the user are obtained to judge the learning state and the learning completion degree of the user, the learning result of the user can be judged more accurately and comprehensively, meanwhile, the defects existing in the learning process of the user can be overcome in the mode of pushing and supplementing the learning content, and the learning efficiency of the user is improved. And the learning effect of the user is improved on the whole.