291 results about "Learning behavior" patented technology

The invention discloses a dynamic learning path planning method and system based on a learner portrait. The method comprises the steps of collecting learning behavior data, learning result data and background attribute data of a target scholar based on a plurality of data sources, and obtaining knowledge point mastery degree vector representation, learning style preference and cognitive ability level to construct a multi-dimensional dynamic portrait of the target scholar; generating an initial personalized learning path of the target scholar based on a preset learning target and the multi-dimensional dynamic portrait of the target scholar; learning process data of a target scholar for related learning resources is monitored in real time, a learning effect evaluation report is generated according to the learning process data, a multi-dimensional dynamic portrait is adjusted to trigger a path replanning mechanism to generate a brand new personalized learning path, and the brand new personalized learning path is pushed. The learning path and the current state of the learner are kept optimally matched all the time, and personalized static planning is upgraded to dynamic syndrome guidance.

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 intelligent education, in particular to a personalized learning path planning method fusing artificial intelligence and education big data, and the method comprises the steps: collecting multi-modal education data of learning behavior data, cognitive process data and emotional state data from a learning terminal; constructing a learner portrait feature vector based on the preprocessed multi-modal education data; establishing a knowledge graph, and dynamically updating a weight matrix based on group learning data; and taking the learner portrait as a state space, taking a learning path decision as an action space, taking a learning effect as a reward function, generating a personalized learning path by adopting near-end strategy optimization, optimizing a path planning model, and generating a recommendation result. Personalized learning path planning is realized by constructing a multi-modal data fusion framework, a dynamic knowledge graph and an intelligent planning method.

The invention discloses an intelligent personalized topic recommendation method based on a knowledge graph, and relates to the technical field of information retrieval, and the method comprises the steps: constructing a multi-dimensional subject knowledge graph which defines knowledge point entities and topic entities through the knowledge graph, and establishes structural relationships and capability dimension attributes between the entities; forming a structured knowledge basis for recommendation; collecting learning behavior data of the user, and generating a user knowledge state model for dynamically evaluating the knowledge state of the user in combination with the multi-dimensional subject knowledge graph; selecting a corresponding recommendation strategy according to an output result of the user knowledge state model, and performing multi-dimensional question matching based on capability dimension matching based on the multi-dimensional subject knowledge graph to generate a personalized question recommendation scheme; and dynamically optimizing the multi-dimensional subject knowledge graph, the recommendation strategy and the matching rule based on the feedback of the user to the recommendation scheme. The problems that a traditional recommendation system is shallow in knowledge association, rough in diagnosis and rigid in strategy are solved.

The invention relates to an AI-based teaching resource intelligent recommendation method and system. The method comprises the steps of obtaining and performing cleaning and standardization processing on a multi-source data set to obtain standardized data, and constructing a knowledge graph; based on the standardized data, learning behavior characteristics of the students are extracted, a clustering algorithm is adopted to generate dynamic tags associated with a preset scene, and student portraits are obtained; based on the knowledge graph, performing semantic annotation on preset teaching resources, and dividing resource difficulty levels in combination with a post competency model to form an annotated resource library; and based on the student portrait, identifying a student learning demand, and according to the student learning demand, matching the resources in the labeled resource library to generate a personalized resource recommendation list. According to the method, by integrating the multi-source data and constructing the knowledge graph, the learning requirements of students can be accurately identified, intelligent recommendation of personalized teaching resources is realized, and the utilization efficiency of the teaching resources is also improved.

The invention discloses a knowledge graph teaching path dynamic recommendation method and system based on reinforcement learning. The method comprises the following steps: constructing a cognitive adaptive dynamic knowledge graph; obtaining an individual multi-dimensional state vector of a student to be recommended; obtaining a trained student state perception model and a trained teaching path reinforcement learning model; inputting the individual multi-dimensional state vector of the to-be-recommended student into a trained student state perception model to obtain cognitive state information of the to-be-recommended student; and calling the trained teaching path reinforcement learning model by taking the cognitive adaptive dynamic knowledge graph as an environment and combining cognitive state information of the to-be-recommended student, so as to obtain a current personalized teaching path recommendation result of the student individual. The teaching content sequence can be dynamically adjusted according to the knowledge state and the learning behavior of the student, and the teaching integrating degree is improved; the teaching path is continuously optimized through learning, and different types of students can be adapted.

The invention provides an AI intelligent education method and system based on a knowledge graph, and the method comprises the steps: extracting a knowledge entity and teaching semantic relation from multi-source education data, and constructing a multi-dimensional education knowledge graph containing a dynamic weight feature vector; learning behavior data of students are collected, knowledge point mastery degree is calculated based on a reaction theory, dynamic cognitive state vectors including cognitive loads and forgetting laws are generated in combination with time sequence data, and a personalized cognitive state model of the students is constructed according to the dynamic cognitive state vectors; a self-adaptive learning path is generated based on the map and a cognitive model, intervention is triggered when learning deviation is monitored, and a compensation learning sub-path is generated; and executing the compensation path, and carrying out synchronous iteration updating on the entity weight, the relationship and the cognitive model parameters in the knowledge graph based on a result. According to the system, precise personalized teaching and dynamic path adjustment are realized, and the teaching suitability and the learning effect are continuously improved through closed-loop optimization.

The invention, which relates to the technical field of education recommendation, discloses a student portrait-driven education agent recommendation system based on a cognitive diagnosis map, comprising a time-frequency decomposition processing module, a weight rhythm matching module, a time sequence coupling damping module, a dynamic difference readjustment module and a time-frequency domain self-balancing control module. And the time-frequency decomposition processing module is used for establishing a time-frequency decomposition processing layer based on drifting characteristics of student portrait parameters in a time dimension, and performing energy distribution analysis on multi-source dynamic data streams from learning behaviors, test performance and emotion feedback according to time slices. Through time-frequency decomposition and non-resonance rhythm matching, dynamic coordination of weight adjustment and student portrait drifting is realized, characteristic fluctuation amplification is prevented, and the stability of a learning path is guaranteed; and a dynamic closed loop is formed through time sequence coupling damping and time-frequency domain self-balancing control, stable convergence of cognitive features is realized, and the accuracy and continuity of educational agent recommendation are improved.

The invention discloses an education robot teaching strategy generation system based on learning behavior big data analysis, and particularly relates to the technical field of intelligent education. The method comprises the following steps: acquiring learning behavior data of students in an education robot teaching process; identifying the active adaptation behavior of the student, and generating an active adaptation behavior identification result; based on the active adaptation behavior recognition result, analyzing a response relationship between the existing teaching strategy and the student learning behavior, and outputting a teaching strategy adaptability analysis index; according to the teaching strategy adaptability analysis index, the deviation degree of the existing teaching strategy deviating from the real learning requirement is evaluated, and a teaching strategy deviation evaluation result is obtained; an adaptive feedback suppression constraint and a strategy depolarization weight are constructed to generate a teaching strategy candidate set, screening and confirmation are carried out according to a consistency verification rule and a robustness threshold, an education robot teaching strategy is updated, and an education robot teaching execution instruction is formed. And the scientificity and individuation level of the teaching strategy are improved.

The invention discloses a personalized learning scheme recommendation method and system based on artificial intelligence, and relates to the technical field of intelligent recommendation, and the method comprises the steps: collecting a learning behavior track and a knowledge mastering progress, carrying out the normalization processing, and generating structural feature input; performing multi-dimensional feature mapping and weight training on the structured feature input by using a reinforcement learning model to generate a learning preference vector; matching the learning preference vector with the weighted feature interaction matrix, calculating an adaptive score, and generating a candidate resource set; and performing iterative fusion on the candidate resource set and the real-time interaction feedback vector to generate a feedback fusion adaptive score matrix, updating adaptive scores by adopting a dynamic adjustment method, forming an optimized resource sequence, converting the optimized resource sequence into a task execution path, and generating personalized learning scheme recommendation. According to the invention, the accuracy of learning resource matching and the timeliness of personalized recommendation are improved.

The invention relates to the technical field of data processing, in particular to a personalized learning path planning method and system. Comprising the following steps: acquiring a behavior data sequence arranged according to a time sequence, and extracting a repetitive mode in a learning behavior and a difference point deviating from a standard learning process to obtain a behavior difference index; dynamically adjusting an association structure between nodes in the knowledge graph, identifying key knowledge nodes, determining a missing concept set, and inserting missing concepts into a current learning path to generate a supplementary path draft; by simulating a plurality of alternative learning paths and calculating coherence scores of the alternative learning paths, screening and sorting to obtain a plurality of personalized learning path options; and integrating the behavior data newly generated by the learner through a feedback module, and outputting a final dynamic learning path. According to the method, the problems of insufficient dynamic adaptability and difficulty in effectively utilizing the behavior data of the learner in the existing learning path planning are solved, and dynamic and personalized learning path planning based on the behavior difference of the learner is realized.

The invention relates to the technical field of intelligent education planning, and discloses a dynamic course planning method based on a knowledge graph. The method comprises the following steps: acquiring real-time learning data including knowledge point mastering degree, learning behavior and progress; and performing association analysis on the data by using the subject knowledge graph to generate a learning state representation vector reflecting knowledge point association strength and path dependency degree. And inputting the vector into a course planning model for calculation to obtain an adjustment scheme containing the knowledge points to be strengthened, the path rearrangement sequence and the resource index. According to the scheme, the node weight of the corresponding learner in the knowledge graph is updated, and a dynamically evolved personalized knowledge graph is formed. And generating a next-stage learning task instruction based on the graph, wherein a logic mapping relationship between the to-be-learned knowledge point sequence and a preset learning target is clearly revealed. According to the method, structured traceability and diagnosis of learning weak links are realized, and an interpretable personalized learning path with a clear logic basis can be provided.

The invention discloses a personalized safety training content generation method, system and device and a medium. The personalized safety training content generation method comprises the steps that answer data, historical performance data and future operation tasks of operators in safety training are collected; safety knowledge weak points and learning behavior modes of the operating personnel are identified, and related risk association points are determined; based on the safety knowledge weak point, the learning behavior mode and the risk association point, constructing a structured user portrait, based on a deep neural network model fused with an attention mechanism, screening basic training content matched with the user portrait from a dynamic training material library, and adjusting the basic training content to obtain a basic training result; according to the method, the personalized target safety training content is generated, and the safety training is performed on the operator according to the target safety training content, so that the generation efficiency of the personalized safety training content can be improved.

The invention discloses an intelligent self-adaptive learning method and system based on a course knowledge graph, and aims to construct a comprehensive knowledge graph through deep analysis of course contents, and the comprehensive knowledge graph comprises knowledge points, dependency relationships among the knowledge points, difficulty levels and learning sequences. By collecting and analyzing the learning behavior data of the students, the system can evaluate the mastering condition of the students on each knowledge point in real time, and further dynamically adjust learning paths and recommendation resources according to the individual requirements of the students. The system adopts an adaptive algorithm, optimizes a learning path and provides personalized learning resources according to the real-time learning progress and feedback of the students, so as to help the students to efficiently master knowledge in the shortest time. Meanwhile, the system further integrates a learning effect evaluation and feedback mechanism, and the accuracy and the intelligent degree of the learning scheme are gradually improved by continuously monitoring the learning process of the students.

The invention relates to the technical field of learning style recognition, and provides a learning style recognition method and system based on deep learning, a medium and equipment, and the method comprises the steps: obtaining learning behavior features of a learner, obtaining embedded features through preprocessing, gradually decomposing the embedded features into non-negative matrix factorization factor features through multi-level non-negative matrix factorization operation, and obtaining a non-negative matrix factorization factor features; the embedded features and the non-negative matrix factorization factor features are fused through an adaptive attention fusion mechanism to obtain fused features; wherein the multi-layer non-negative matrix factorization operation adopts a multi-layer factorization structure, and potential factors are gradually increased layer by layer; and on the basis of the fusion features, learning styles are predicted through a deep classification network. And the learning style identification accuracy is improved.

The invention discloses a network school student personalized learning path recommendation system, and relates to the technical field of education, and the recommendation system comprises a data collection module which obtains the micro-expression and limb details of a student during learning based on a camera, collects the learning behavior data of the student, and judges the knowledge ability data of the student through image analysis and limb analysis; the student portrait construction module obtains the knowledge weakness direction of the current student based on the knowledge ability data of the student, and constructs a multi-dimensional student portrait; and the knowledge graph construction module is used for constructing an association graph of the subject knowledge system, and the association graph comprises a preposed dependency relationship and a difficulty level between knowledge points. According to the invention, learning is reinforced and learning rhythm is adjusted in real time through a recommendation algorithm layer, proper challenges are introduced into an anti-fragility mechanism to enhance knowledge mastering toughness, path output is coupled with real-time states of students, course design and teaching can be fed back, learning efficiency and achievement sense of students are improved, and technical barriers of network schools are enhanced.

The invention relates to the technical field of intelligent education, in particular to a personalized learning path generation method and system driven by a dynamic knowledge graph. The method comprises the steps of collecting and processing multi-modal learning behavior data of a learner to construct and update a dynamic knowledge graph reflecting a knowledge mastering state and knowledge point association in real time; a double-engine diagnosis mechanism combining large model deep reasoning and knowledge graph real-time verification is adopted, and cognitive weak points and knowledge structure defects of learners are accurately recognized; and on the basis of a diagnosis result, a personalized learning path adaptively matched with the cognitive state of the learner is generated through multi-agent collaborative decision, and closed-loop optimization is performed on a knowledge graph and a path planning strategy according to real-time feedback of a path execution effect. According to the invention, the defects of the traditional adaptive learning system in the aspects of diagnosis accuracy, individuation degree and dynamic adaptability are effectively overcome, and accurate, efficient and continuously optimized individualized learning experience can be provided for students. According to the method, through deep fusion of double-engine diagnosis and the dynamic knowledge graph, the accuracy and reliability of cognitive state diagnosis are remarkably improved, and the illusion problem of a large model in the STEM field is solved; through a multi-agent collaborative decision-making mechanism, high personalization and dynamic adaptability of a learning path are realized; finally, a teaching closed loop with a self-optimization capability is formed, and the intelligent level and the teaching efficiency of the self-adaptive learning system are essentially improved.

The invention relates to the technical field of intelligent education, in particular to a learning resource recommendation method, system and device, a medium and a product. According to the method, basic information and historical learning behavior data of a target object are obtained, an initial interest map is constructed in combination with a graph neural network algorithm, emotion calculation is performed according to multi-modal emotion data of the target object, a comprehensive emotion vector is generated, and therefore the target object is obtained according to a first interest vector of the initial interest map and the comprehensive emotion vector. And determining a learning resource recommendation result so as to improve the accuracy and adaptability of learning resource recommendation and further improve the learning efficiency of target objects such as children.

The invention discloses a smart education guidance evaluation system and method based on big data, and belongs to the technical field of smart education and big data analysis. The system comprises a multi-source heterogeneous education data acquisition module, a data preprocessing and feature fusion module, a multi-dimensional capability evaluation module, a learning behavior sequence analysis module, a personalized guidance recommendation module and a multi-role visual display module. A subjective and objective fusion weighting algorithm and a double-flow attention knowledge tracking algorithm are adopted to realize five-dimensional capability comprehensive evaluation, a key behavior node identification algorithm is utilized to carry out learning behavior time sequence modeling, and a cognitive load perception path optimization algorithm is utilized to generate a personalized learning path. The problems that in an existing education evaluation system, the evaluation dimension is single, data collection fragmentation is achieved, and personalized guidance lacks data support are solved.

The invention provides a parent terminal applet system for recommending and reporting student learning conditions based on parent portraits. The system comprises a data acquisition module for acquiring a student multi-source learning data set from students after a parent terminal applet logs in; meanwhile, parent portrait information is recommended and determined for parents of the students; the fusion processing module performs data processing on the multi-source learning data set of the student by adopting a data fusion method; the behavior analysis module performs learning behavior analysis according to the student multi-source learning processing data to obtain student learning analysis data; the report generation module is used for generating a learning condition report text based on student learning analysis data in combination with parent portrait information through a natural language processing model to obtain learning condition report text information; and the condition feedback module feeds back the learning condition of the student in the parent-side applet according to the learning condition report text information. According to the invention, learning conditions of students can be reflected more deeply, and parents can be ensured to accurately understand learning condition feedback information of the students.

The invention discloses a personalized learning portrait construction method fusing multi-source data and state updating, and belongs to the field of student portrait construction. The method comprises the steps of firstly collecting learning behavior data of a student, calculating a mastering probability value of the student for each knowledge point in a knowledge graph based on a preset evaluation model, and mapping the mastering probability value into an explicit mastering state; active interaction behaviors of the students are monitored in real time, the self-evaluation mastering state is deduced, and whether correction of the explicit mastering state of the specific knowledge points is triggered or not is judged according to the state difference degree; and taking the corrected mastering state of the knowledge point as an updating trigger point, calculating an expected influence quantity of the mastering state change of the corrected knowledge point on the associated knowledge point based on the topological structure of the knowledge graph, and updating the mastering state of the associated knowledge point according to the expected influence quantity, thereby generating an updated personalized learning portrait. According to the method, the mastering state of the associated knowledge points is dynamically and intelligently updated, and a solid technical support is provided for self-adaptive learning path recommendation.

The invention discloses a learning progress updating system, method and device and electronic equipment, and relates to the technical field of education, and the system comprises a learning behavior collection module, a behavior change analysis module, a learning progress mapping module and a learning progress adjustment module. The learning behavior acquisition module is used for acquiring learning behavior data of a target student and a first learning progress of at least one virtual user; the behavior change analysis module is used for determining behavior change data of the target student; the learning progress mapping module is used for performing learning progress mapping on the behavior change data to obtain a second learning progress of at least one virtual user; and the learning progress adjustment module is used for generating progress adjustment information corresponding to the at least one virtual user, so that the at least one virtual user is adjusted to a second learning progress. The learning progress of the virtual user is updated based on the behavior change data, the matching degree between the virtual user and the student is improved, and the accompanying learning requirements of the student in different learning stages are met.

The invention discloses a learning recommendation method and device based on education meta universe and knowledge tracking, and relates to the technical field of meta universe learning application. The method comprises the following steps: data collection and arrangement: continuously collecting learning behavior data of students on a platform by using various sensors and interactive interfaces in the education universe platform, collecting basic information of the students at the same time, and carrying out classified storage on the collected data according to a time sequence and a data type. Through comprehensive collection of student learning behavior data, basic information and intelligent interaction feedback, in combination with a knowledge tracking model and a comprehensive evaluation method, student knowledge states can be accurately evaluated, recommendation contents are screened and individually sorted according to evaluation results, students can obtain learning resources most suitable for themselves, and learning efficiency is improved. And the recommended contents are screened and individually sorted according to the evaluation result, so that the students can obtain learning resources most suitable for themselves, and the learning efficiency is remarkably improved.

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 invention discloses an intelligent identification and analysis system for school breeze evaluation based on artificial intelligence. The system comprises a school breeze data acquisition module, a data preprocessing module, a school breeze feature analysis module, a school breeze evaluation engine module, a personalized improvement suggestion module, a user interaction module, a system management module and a data security module. According to the method, the full-scene learning behavior data of the students are acquired in a multi-source manner, the characteristics of the scholarship breeze are deeply mined by using an artificial intelligence algorithm, a scientific scholarship breeze evaluation system is constructed, and the transformation from traditional manual evaluation to intelligent and accurate evaluation is realized.

The invention discloses a knowledge point state determination method and device based on an AI agent, and relates to the technical field of education, and the method comprises the steps: obtaining the learning behavior data of a student for learning knowledge points in a knowledge point learning grid, and the mastery degree identification of the knowledge points in the knowledge point learning grid; determining first mastery degree data of the students on knowledge points in the knowledge point learning grids based on the mastery degree identifiers, and evaluating first knowledge point state data according to the first mastery degree data; determining second mastering degree data of the students on knowledge points in the knowledge point learning grid based on the learning behavior data, and evaluating second knowledge point state data according to the second mastering degree data; and according to the first knowledge point state data and the second knowledge point state data, determining state abnormal knowledge points from the knowledge point learning grid, and according to the state abnormal knowledge points, updating a knowledge graph corresponding to the knowledge point learning grid. The learning effect can be improved.

The invention discloses a verifiable evidence storage and authentication method and system for a learning process and a storage medium, and relates to the technical field of computers and education, and the method comprises the specific steps: capturing the learning behavior data of a learner in real time, and generating a structured learning event record; performing multi-dimensional verification processing on the learning event; carrying out batch aggregation on the verified learning events according to a preset rule, generating an aggregation proof by using a cryptographic accumulator, and anchoring the aggregation proof to a tamper-resistant distributed account book; generating a dynamic learning resume through an event aggregation algorithm based on the stored learning event, and storing the dynamic learning resume in a distributed storage system; automatically evaluating the dynamic learning resume according to a preset multi-dimensional authentication standard to generate an authentication result; and generating a digital certificate meeting the verifiable certificate standard through an intelligent contract or a credible signing and issuing service based on the authentication result. According to the invention, a reliable learning process and result proof are provided for the learner for lifetime, and the education and occupational development opportunities are improved.

Provide a system. 【Solution means】 Means for inputting the user's interests and existing knowledge, Means for selecting 3D models and supplementary materials based on learning themes, Means for transmitting the selected content to the user terminal and preparing a virtual reality environment, Means for analyzing the context and generating relevant information based on voice commands and gestures from the user, Means for presenting the analysis results and generated information to the user in real time, Means for recording the user's learning behavior and analyzing the progress, Means for providing feedback based on the progress, Means for analyzing the user's tendencies and proposing future career paths, A system including the above.

The application relates to the technical field of data management control, and particularly discloses a data management control method and system based on a maternal and infant one-stop learning cloud platform, which comprises the following steps: based on real-time collected maternal and infant physiological data, maternal and infant historical health records and user learning behavior data, a multi-modal maternal and infant user portrait containing dynamic health state labels and dynamic learning ability labels is constructed; based on multi-modal maternal and infant user portraits of all maternal and infant users, multi-dimensional health horizontal comparison factors of pregnant women and fetuses or infants in each maternal and infant user under each division attribute and each division level are determined, and maternal and infant health deviations of each maternal and infant user are analyzed; based on the multi-dimensional health horizontal comparison factors of the pregnant women and the fetuses or the infants in each maternal and infant user under each division attribute and each division level and the maternal and infant health deviations, intelligent adaptation of multiple services is driven; and a closed loop from health deviation to education content, health plan and deviation improvement is formed.

This invention belongs to the field of educational technology, specifically an adaptive learning time management method based on learning behavior. It compares the current and future cognitive load equivalents of a student's current learning session with the expected load range derived from parental control rules. This identifies immediate or predicted load conflicts, and based on the conflict type and magnitude, triggers and executes corresponding control instructions that integrate the basic duration of a single rest or the selection range of low-cognitive-load knowledge points to adjust the learning process. Finally, the triggering logic of the control instructions is optimized through the feedback data package generated after the execution of the control instructions. This approach quantifies parents' personalized management intentions into identifiable and operable control criteria, and ensures that the triggering of intervention instructions such as rest interruptions or path adjustments matches the student's real-time cognitive resource consumption status, thereby improving the effectiveness and adaptability of the control strategy.