204 results about "Learning resource" patented technology

The invention discloses an educational scene-oriented knowledge graph enhanced large model personalized learning recommendation method and system, and the core thought of the method is that through the coupling of a knowledge dominant structure and a large model semantic capability, personalized learning resource pushing of triple driving of structure + semantics + behavior is realized. And deep, multi-dimensional and dynamic feedback-driven learning resource personalized intelligent recommendation can be realized in combination with student individual differences, learning paths, cognitive levels and teaching resource semantic structures.

The invention discloses a personalized learning resource recommendation method and system based on multi-agent collaboration and a dynamic knowledge graph, and the method comprises the steps: constructing the dynamic knowledge graph, enabling nodes to be associated with teaching resources (videos, test questions, teaching plans, PPT and the like), and enabling edges to represent the logic relation between the resources; collaborative decision is made through four layers of agents: a target determination agent generates a learning target based on student historical learning data and a graph node state; the path planning agent plans a learning path in combination with the target and the learner model; the resource screening agent matches personalized resources from the path nodes; the user portrait intelligent agent updates the learner model in real time; and finally, generating a dynamic recommendation result and feeding back the optimized knowledge graph. Through multi-agent hierarchical collaboration and dynamic interaction with the knowledge graph, the problems of cold start, incomplete resource coverage and path stiffness of a traditional recommendation system are solved, and precise and adaptive learning resource recommendation is realized.

The invention discloses a course resource recommendation method and device based on an improved state space model, equipment and a medium, and relates to the technical field of learning resource recommendation, and the method comprises the steps: collecting user course resource data, obtaining and preprocessing an interaction sequence, mapping the user interaction sequence to a low-dimensional vector space, and inputting the low-dimensional vector space into the improved state space model; the target interest course is matched with the candidate course resource set, the recommendation score is calculated, and the recommendation list is generated, so that the calculation complexity is reduced, the user interest is accurately captured, and the efficiency and accuracy of course resource recommendation are improved.

The invention relates to the technical field of personalized learning. The method comprises the steps that a learning target and a cognitive state vector serve as joint input information to be sent into a large language model, and a knowledge point set and learning gap information corresponding to the learning target are generated; and aligning the knowledge point set with a pre-repair relationship and a dependency relationship in a preset educational knowledge graph to obtain an alignment result, and when a difference value between the updated cognitive state vector and the cognitive state vector exceeds a preset cognitive state change threshold value, determining a learning target based on the updated cognitive state vector and the learning target. And performing semantic analysis and reasoning of the large language model again to obtain a new knowledge point set, determining a new learning resource sequence according to the updated personalized learning path, and pushing the learning resource sequence to the user terminal. The method has the effect of realizing closed-loop optimization of the learning scheme.

The embodiment of the invention relates to the technical field of artificial intelligence, and provides a personalized learning resource recommendation method and system based on deep learning. The method comprises the following steps: acquiring a user learning scene and a real-time operation behavior to construct a heterogeneous interaction graph; in combination with a Transform meta-coding model of an MAML architecture, pre-training a meta-model based on a real-time operation behavior to adapt to fine tuning of scene parameters; embedding a domain knowledge graph entity to obtain a semantic association vector, and capturing an entity pre-repair relationship; performing multi-hop reasoning on the heterogeneous interaction map through a GAT map attention network, and iteratively generating a user preference vector and a learning resource feature vector; for interactive sparse users, real-time operation behaviors are input into the pre-training meta-model to generate exclusive recommendation parameters, new resource feature vectors are generated in combination with the knowledge graph, and user preference vectors are matched based on the exclusive parameters; and predicting the interaction probability according to the matching vector, and outputting the target recommendation information according to the interaction probability so as to improve the recommendation efficiency and accuracy of the learning resources and guarantee the adaptation degree of the learning resources and the user.

The invention relates to the technical field of vocational education, in particular to a vocational ability training system based on an artificial intelligence technology, which comprises a user portrait modeling module for generating a dynamic personal ability map based on historical data and an ability evaluation model of a user; the knowledge graph engine is used for integrating industry capability standards, post demand data and a real-time updated vocational skill knowledge base to form a structured knowledge network; the AI training recommendation module is used for dynamically generating a personalized training path by adopting a reinforcement learning algorithm in combination with a user portrait and a knowledge graph; the digital human application module constructs a simulation working scene through virtual reality and NLP technologies, and supports a user to complete training through natural dialogue and operation; and the real-time feedback and correction module is used for analyzing user performance by using multi-modal data and generating instant guidance suggestions. The system has the following beneficial effects of training personalized depth improvement, learning resource and scene expansion, learning effect evaluation and feedback optimization, and technology fusion and interactive innovation.

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 dynamic optimization, and discloses an intelligent learning dynamic optimization system introducing a time sequence. The system comprises a multi-source data preprocessing module, an intelligent learning knowledge graph establishing module, a learning path optimizing module and a learning resource matching module. Firstly, a data detection model is constructed based on a neural network to perform invalid data filtering and abnormal data detection; secondly, knowledge node association strength is calculated based on a time sequence, and an intelligent learning knowledge graph is established; marking a learning path in the intelligent learning knowledge graph, and performing dynamic path planning by using an improved Harris eagle optimization algorithm to find an optimal learning path; and finally, generating learning resource configuration according to the optimal learning path, and establishing a learning resource feature library for resource matching to obtain an intelligent learning optimization strategy. According to the method, the learning behavior data are analyzed and processed by introducing the time sequence, and the purpose of intelligent learning dynamic optimization is achieved.

The invention provides an educational resource sequence recommendation method and a related device, and relates to the technical field of resource recommendation. After student learning behavior data, learning resource attribute data and knowledge graph structure information are obtained, a regularization matrix decomposition technology is adopted to carry out potential factorization modeling on a student-resource interaction scoring matrix, and basic preference embedding features of students and resources are extracted. Aiming at diversity and complexity of student learning behaviors, a multi-view feature coding module is designed, learning sequence and short-term interest features are extracted from a behavior sequence view, static attribute features of students and courses are extracted from an attribute information view, and knowledge point pre-correction dependence and association relationship features between courses are extracted from a knowledge structure view. In order to improve the consistency and discrimination of feature representation, on the basis of multi-view fusion features, a comparative learning mechanism is introduced, and the discrimination and migration modeling ability of the model to student interests and preferences is enhanced by constructing a positive and negative sample comparison loss function.

The invention discloses a multi-modal learning resource intelligent recommendation method based on an AI large model, and relates to the technical field of educational information, and the method comprises the steps: collecting multi-modal learning resources, carrying out the semantic feature extraction and vectorization representation, and obtaining a structured resource set; constructing a knowledge mastering state vector based on the resource set, and determining a knowledge hole set; virtual nodes are generated according to the knowledge holes, and an enhanced knowledge graph is constructed; calculating semantic path correlation and a path comprehensive score in the enhanced knowledge graph, and screening a candidate resource set; calculating a personalized score based on the candidate resource set, and generating a final recommendation list; and acquiring feedback data of a learner to obtain a learning income index, performing node and edge level attribution analysis, and performing enhanced updating and weight normalization processing on the knowledge graph according to an increment contribution weight to form a new knowledge graph. According to the invention, through knowledge mastering state modeling and map enhancement, automatic identification and dynamic completion of knowledge holes of learners are realized.

The invention relates to the technical field of computer processing, in particular to a learning strategy generation method, electronic equipment and a storage medium, and the method comprises the steps: obtaining an evaluation result after a target user completes individual ability evaluation; performing multi-modal interpretation processing on the evaluation result, and determining an interpretation video of the evaluation result; the interpretation content of the interpretation video comprises knowledge point mastering condition information of the target user, capability condition information of each evaluation dimension, subject potential prediction information and occupational prediction information; and matching the ability condition information of the target user under each evaluation dimension with the content label of the learning content in the learning resource library to generate a learning strategy. According to the method, the interpretation video with the multi-dimensional interpretation content is provided, the understandability of the user to the evaluation result can be improved, meanwhile, the personalized learning strategy is given, the evaluation result is converted into the action scheme, and the quality of learning development guidance can be improved.

The invention relates to the technical field of artificial intelligence and education technology cross fusion, and particularly discloses a new media course intelligent learning resource generation system based on generative AI, and the system obtains the multi-dimensional state data of a learner through the means of eye movement tracking, electroencephalogram collection, interaction behavior analysis and the like, and constructs a high-dimensional state space through fusion; differential induction logic programming and a depth feature extraction network are cooperatively compressed into a low-dimensional semantic meta-state space, and the model interpretability and decision-making efficiency are improved; dynamic matching of personalized resource types and difficulty levels is realized based on a graph neural network and reinforcement learning; calling a multi-modal generation model to generate texts, videos or interactive contents as required, and introducing a cross-modal consistency verification mechanism to guarantee the output quality; the system also has an online adaptive optimization capability, combines a symbol rule recombination mechanism and a differential privacy protection strategy, and takes account of user privacy security while ensuring long-term stable operation of the system.

The invention provides an intelligent course matching and promotion path recommendation method and device based on post portraits. The method comprises the steps of obtaining internal multi-source heterogeneous data of an enterprise and performing preprocessing to obtain a standardized multi-dimensional feature data set; constructing a dynamic post capability portrait by adopting a semi-definite programming method of an asymmetric random block model; analyzing a causal relationship between capability dimensions by applying a linear time primitive algorithm to obtain an employee capability assessment report; a constraint solution algorithm meeting excessive dissipation is adopted to calculate the capability gap matrix to obtain a personalized capability improvement requirement; screening and combining the learning resources through a weighted matching algorithm to obtain a personalized learning recommendation plan; and adopting a promotion path planning algorithm to construct multi-dimensional promotion possibility evaluation to obtain personalized promotion path suggestions. According to the invention, a closed-loop feedback mechanism of learning, evaluation and promotion is realized, and a systematic solution is provided for enterprise talent development.

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 provides a learning resource recommendation method based on knowledge tracking and retrieval enhancement generation, and belongs to the technical field of education. The method comprises the steps of obtaining data of a user learning platform and / or learning content uploaded by a user, and constructing a background knowledge base related to a current learning task of the user; acquiring learning interaction behavior data of the user, constructing a knowledge state tracking model, and forming a current knowledge mastering state of the user; wherein the learning interaction behavior data of the user comprises knowledge points, exercises, videos and texts; and outputting personalized learning resource recommendation and question and answer response by utilizing a retrieval enhancement generation model in combination with the background knowledge base and the knowledge mastering state. Therefore, dynamic, personalized and knowledge-accurate learning content recommendation can be realized.

The present application belongs to the technical field of resource allocation management, and provides a kind of intelligent campus teaching resource management method and system based on artificial intelligence, comprising: obtaining the learning resource search record in the present stage learning process of student, and the next stage teaching resource allocation situation of school, screening out main learning resource, according to the search record to carry out search effective frequency analysis, obtain search effective value, carry out search conversion analysis of main learning resource, obtain search conversion value, and determine search dominant platform and non-search dominant platform in combination with the reserve proportion of main learning resource in each resource platform, integrate to obtain search conversion platform group, carry out search jam risk analysis to search channel, judge whether conversion channel allocation is needed, if needed, according to search conversion record and conversion nearby analysis, in combination with search conversion value, conversion channel is distributed, which is beneficial to the rational allocation of teaching resources and improves resource utilization.

The invention provides a self-adaptive personalized learning path generation method and system, and the method comprises the steps: extracting a target capability list from an existing training model or post competency requirements, and constructing a capability map; based on a user historical course test result and a practical operation task completion condition, generating a user capability state vector by adopting a weighted scoring mechanism; performing priority scoring on the capability nodes, screening the capability nodes according to the priority score to form a learning path, distributing learning resources in combination with user preference, and generating a task scheduling plan; issuing a task according to the task scheduling plan, and collecting behavior data; calculating a behavior effectiveness score, identifying an abnormal task of which the behavior effectiveness score is lower than a threshold value, and adding a behavior deviation label; and based on the behavior deviation label matching correction strategy of the abnormal task, adjusting a path and a scheduling plan, and outputting a corrected learning path and a scheduling table.

The invention discloses a method for generating and optimizing a personalized learning scheme for college entrance examination based on deep learning, and aims to solve the problems of large learning difference of students, difficulty in personalized tutoring and low utilization rate of learning resources. Comprising the following steps: S1, collecting multi-source learning data of students through a multi-platform data interface and an embedded tracking technology; s2, preprocessing the multi-source learning data set to obtain a standardized learning data set; s3, constructing a deep learning model to extract student learning feature parameter vectors; s4, generating a student personalized learning scheme through a personalized recommendation algorithm; s5, collecting student learning process data in real time; s6, dynamically adjusting and optimizing the personalized learning scheme in combination with a feedback optimization mechanism; and S7, performing quantitative evaluation and generating a learning effect report. According to the method, intelligent and adaptive personalized learning recommendation and dynamic optimization can be realized, and the scientificity and learning efficiency of college entrance examination preparation are remarkably improved.

The application belongs to the technical field of personalized learning, and discloses a multi-level intelligent cognitive tracking method, system, storable medium and terminal, the method comprising: introducing Bloom cognitive domain education target classification, constructing test question knowledge cognitive tensor TKC, collecting learning resources and answer data of learners, and generating a sequence of learner time sequence answer pairs; introducing a multi-attribute cognitive diagnosis method, combining a deep neural network, and constructing a cognitive level mining model; sorting and encoding the cognitive level mining results of the learners to obtain deep representation features, combining a self-attention mechanism, constructing a multi-level intelligent cognitive tracking model, and further predicting the answer performance of the learners on the test questions. The application is beneficial to accurately and finely modeling the overall knowledge structure and specific level of the learners, thereby promoting personalized learning of the learners and providing a new idea for mining and tracking the cognitive state and level of the learners in an online learning platform.

The invention discloses a training content planning and checking method and device and a medium, and the method comprises the steps: carrying out the data collection of a student according to a preset data type, so as to obtain multi-modal data corresponding to the data type, carrying out the feature extraction of the multi-modal data, so as to obtain a data feature, and determining a student portrait according to the data feature; determining a learning target according to the trainee portrait, determining knowledge graph navigation according to a predetermined knowledge dependency relationship and the learning target, and calling a resource library according to the knowledge graph navigation to obtain learning resources; and determining knowledge points according to the knowledge graph navigation, determining an assessment standard according to the knowledge points and the trainee portrait, assessing the trainee according to the assessment standard, and obtaining an assessment result. From data acquisition to assessment result acquisition, the system can accurately adapt to trainees, scientifically plan learning and reasonably assess and evaluate, and effectively improves the training effect and the learning efficiency of the trainees.

The invention discloses a self-adaptive education resource intelligent recommendation platform, and relates to the technical field of intelligent education, and the platform comprises a data obtaining module which is used for obtaining the knowledge mastering probability distribution of students; a weak knowledge point identification module identifies weak knowledge points through the distribution, and searches prerequisite knowledge points in the subject knowledge concept lattice to form a candidate root cause set; the root cause knowledge point screening module sorts the candidate sets according to the weakness scores, selects knowledge points and judges whether the weakness scores exceed a preset threshold value or not, and if yes, Bayesian network anti-fact intervention is executed; the effective root cause determination module calculates the mastering probability change after intervention, and if the change exceeds a second preset threshold value, the knowledge points are determined as effective root cause knowledge points; the resource recommendation module recommends corresponding learning resources according to the effective root causes; according to the method, weak knowledge points are intelligently analyzed, so that prerequisite knowledge points which are not mastered by students can be positioned, and more accurate learning resource recommendation is provided.

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 dynamic learning resource recommendation. The invention relates to a dynamic learning resource recommendation system driven by an intelligent behavior analysis teaching aid. The system comprises a resource library establishment unit, an initial resource updating unit, an examination prediction unit, a resource selection unit and a resource stage management unit. The resource library establishing unit is used for establishing a resource library in the teaching assistant machine, and collecting grade information of each student, teaching progress of a teacher and examination data of the student at the same time; by calculating the knowledge point matching degree of the wrong questions and the learning resources, it is ensured that knowledge points corresponding to the wrong questions can be matched with related content in a resource library, accurate mapping is formed, low learning efficiency caused by generalization recommendation is avoided, by integrating multi-dimensional data portrait support, the recommendation resources are made to accurately match individual demands of students, and the learning efficiency is improved. Resource difficulty is prevented from being disjointed with student level.

The invention belongs to the technical field of data processing, and provides a learning ability evaluation model construction method for personalized learning of an online learning platform, which comprises the following steps: firstly, collecting behavior data and learning resource metadata of a user in a learning process; an individual behavior heterogeneous graph, a knowledge system hypergraph and a knowledge point sequential graph are constructed; then, a learning scene attention matrix is preset, and a context sensing mechanism is determined in combination with the knowledge point sequential diagram; and finally, according to a context sensing mechanism, the individual behavior heterogeneous graph and the knowledge system hypergraph, constructing a learning ability evaluation model. According to the method, by constructing the individual behavior heterogeneous graph, the knowledge system hypergraph and the knowledge point sequential graph, behavior data generated by the user in the learning process is deeply fused with metadata such as the type and difficulty of learning resources and associated knowledge points, accurate modeling of a complex interaction relation between the user-resource-knowledge points is achieved, and the user-resource-knowledge point interaction relation is established. Systematicness and dynamics of a knowledge system are comprehensively shown, and the accuracy of learning ability evaluation is effectively improved.

The invention discloses an intelligent management system based on data analysis, and belongs to the field of learning management systems, and the system comprises a weak subject analysis module which is used for extracting each subject score of a target student from a historical examination score database; the assistance matching module is in communication connection with the weak subject analysis module and is used for establishing a voluntary application one-to-one assistance relationship for the students with the weak subjects; the wrong question path generation module is used for analyzing the examination wrong questions of the target student in recent three months, counting knowledge point weights and constructing a dynamic learning path; the risk early warning module is used for detecting students whose scores decline in a cliff mode and starting three-level risk early warning through behavior and state analysis of campus monitoring; and the notification execution engine is used for triggering teacher appointment conversation, parent communication, seat adjustment and learning resource pushing operation according to the analysis result. According to the invention, quantitative evaluation can be carried out on dynamic change of subject strength, and then intervention necessity is automatically judged.

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 virtual power plant resource aggregation method and system based on a multi-relation space-time diagram neural network. The method comprises the steps of collecting operation characteristic data of adjustable resources and constructing a space-time data set; constructing a plurality of adjacent matrixes according to the geographical distance, the electrical distance and the historical correlation among the resources; inputting the spatio-temporal data set and the adjacent matrix into a multi-relation spatio-temporal diagram neural network, extracting spatio-temporal features through spatio-temporal convolution and diagram convolution, and performing multi-relation spatial dependence modeling; performing adaptive weighted fusion on the features under different relationships by using an attention mechanism to obtain a resource aggregation feature; based on the training model, learning a resource spatio-temporal evolution law to predict an aggregation power curve, and performing risk assessment in combination with confidence interval analysis; and finally outputting a power prediction result and a confidence interval thereof to provide decision support for market bidding. According to the method, the multivariate relationship is deeply fused, the uncertainty can be accurately quantified, and the precision of resource aggregation description and the decision reliability are remarkably improved.

The invention discloses an intelligent learning companion system, which comprises a data acquisition module, a data analysis module, an atlas analysis module, an intelligent question answering module, a dynamic question setting module and a resource recommendation module, the graph analysis module analyzes and reasones knowledge mastering conditions according to the knowledge graph, the intelligent question-answering module interacts with the students through a Scotch bottom heuristic question-answering method, and the dynamic question-setting module performs question-setting testing on the students according to analysis results of the data analysis module and the graph analysis module. And the resource recommendation module recommends learning resources required. According to the invention, corresponding knowledge point reinforcement and learning are carried out according to the knowledge point mastering condition of the student, and the learning income of the student is improved by providing personalized help.

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 invention discloses an ICT (information and communication technology) learning resource dynamic intention recognition semantic and time enhancement recommendation method, which comprises the following steps of: proposing learning requirements by a user, finding out a semantic-related resource list from ICTKB by using a semantic model, analyzing user query by using a large model, judging learning intentions (such as entry, depth, practice and the like) of the user, and recommending the learning intentions (such as entry, depth, practice and the like) of the user. And judging whether the query has timeliness sensitive words or not, calculating a timeliness score, calculating a comprehensive score in combination with semantic similarity, the identified intention (and the corresponding weight), the resource characteristics (popularity, credibility and difficulty) and the timeliness score, and reordering the recall list. And presenting the resource list which is subjected to intelligent reordering and most meets the intention and timeliness demand of the user to the user. According to the method, the dynamic learning intention can be deeply understood, high-quality learning resources advancing with times can be accurately matched, and the method can adapt to intelligent recommendation of rapid iteration characteristics of domain knowledge.