375 results about "Learning resource" patented technology

The invention discloses a personalized learning recommendation method based on a personalized knowledge graph. The method comprises the steps that the knowledge graph is constructed, and a learner fills in learning style self-description and learning target expected content; the current learning condition of the learner is subjected to primary research evaluation through the enrollment test module; an initial learner portrait model is established, a learner enters a database for learning, and a learning evaluation module performs staged evaluation on the learning condition of the learner; updating a learner portrait model, and generating a learning path and learning resources adaptive to the learner by using a knowledge graph technology and a reinforcement learning DQN network; updating the learning path database; the new learning path is recommended to the learner; according to the method, the learning data of the students are analyzed to determine the personal basic conditions of the students, the learner portrait model is constructed, the knowledge graph technology and the learning recommendation algorithm are utilized to adapt the personalized learning features to the learning knowledge element sequence, and a personalized learning push path recommendation mode is constructed to assist the students in improving the learning effect.

The invention discloses a personalized learning recommendation method for a learner. The method comprises the steps that the learner completes learning style evaluation and learning target setting of the learner; the current learning condition of the learner is subjected to primary research evaluation through the enrollment test module; an initial learner portrait model is established, a learner enters a database for learning, and a learning evaluation module performs staged evaluation on the learning condition of the learner; updating a learner portrait model, and generating a learning path and learning resources adaptive to the learner by using a knowledge graph technology and a reinforcement learning DQN network; updating the learning path database; the new learning path is recommended to the learner; according to the method, the learning data of students in the platform learning process are analyzed to determine the personal basic condition of the students, the learner portrait model is constructed, the knowledge graph technology and the learning recommendation algorithm are utilized to adapt personalized learning features to the learning knowledge element sequence, a personalized learning push path recommendation mode is constructed, and the learning recommendation efficiency is improved. And the learning effect can be improved.

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 provides a student adaptive auxiliary learning method and system based on artificial intelligence, and the method comprises the steps: constructing a subject knowledge graph, and carrying out the correlation and structuralization of knowledge points; the knowledge points are associated with learning resources and test questions in the subject knowledge graph; collecting learning behavior data of students, and constructing student portraits; the student portrait comprises three dimensions of learning style, knowledge level and hobbies and interests; wherein the knowledge level is a mastering probability of each knowledge point acquired according to the subject knowledge graph; personalized learning paths, learning resources and learning strategies are recommended to the students according to the student portraits and the subject knowledge maps; and learning results of the students are automatically evaluated and fed back. According to the characteristics and requirements of each student, a personalized learning scheme is provided, the learning efficiency is improved, the limitation of time and space is broken through, and the students can obtain high-quality learning resources which are more personalized for themselves anytime and anywhere.

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 learning planning system based on a computer, and relates to the technical field of course learning planning, and the course learning planning system comprises a learning data acquisition module, a knowledge graph construction module, a learning index scoring module, a multi-index comprehensive evaluation module, a learning plan making module and a real-time progress feedback module. Wherein the modules are connected through electric signals; the learning data acquisition module is used for collecting learning data of students and determining evaluation indexes of the learning data. According to the course learning planning system based on the computer, by monitoring the learning progress and achievement of students, instant learning feedback is provided for the students, so that the students can quickly know the learning state of themselves, related learning resources and exercises are intelligently recommended according to the learning conditions of the students, the students are helped to consolidate knowledge and make up for deficiencies, and the learning efficiency is improved. Students can achieve a higher learning effect in a shorter time, and the learning efficiency is improved.

The invention discloses a digital intelligent teaching recording and broadcasting system and method, and relates to the technical field of data encryption, and the method comprises the steps: collecting teaching videos and audios of a teacher through a high-definition camera and an audio device, carrying out the preprocessing, obtaining the course content, recognizing the key information of the course content through a natural language processing NLP and computer vision technology, and carrying out the recording and broadcasting. The method comprises the following steps: segmenting a video into single knowledge point fragments, performing classified storage, forming a learning resource library, collecting basic information and learning habit data of students, constructing a user portrait, matching learning resources, extracting the learning habit data in the user portrait, performing feature extraction, and obtaining a comprehensive feature vector; constructing a learning state evaluation model by using a convolutional neural network to comprehensively analyze the comprehensive feature vector to obtain a learning state comprehensive score, formulating a playing strategy according to the learning state comprehensive score and matched learning resources, regularly testing knowledge points of students, evaluating the mastering degree of the students, and updating a user portrait according to a result.

The invention relates to a personalized learning resource recommendation method based on a subject knowledge graph and a graph neural network, and belongs to the field of education content recommendation. The method comprises the following steps: extracting key entities in a textbook and a relationship between the key entities, and constructing a subject knowledge graph; establishing a preliminary learning record table according to the behavior data of the student in the learning process; through knowledge tracking model integration, predicting mastery degree distribution of students for each knowledge point in the learning record sheet, and constructing student portraits; embedding the knowledge points in the subject knowledge graph and the association relationship thereof into the student portrait, and updating the knowledge embedding representation in the student portrait through a graph neural network; and constructing a personalized recommendation model, and pushing learning resources conforming to the current learning state of the student by adopting a mixed recommendation method of collaborative filtering and content recommendation. According to the method, personalized demands of students can be accurately captured in multiple dimensions, and the accuracy, generalization and interpretability of recommended contents are effectively improved.

The invention discloses an artificial intelligence self-adaptive education system based on big data, and relates to the field of intelligent education, the artificial intelligence self-adaptive education system comprises an education resource management module, a student data management module, a self-adaptive learning recommendation module, an auxiliary teaching decision module and a security reinforcement module, and a cloud data management library is used for classifying and integrating education resources. The method comprises the steps of establishing a knowledge graph, performing knowledge point classification on resources by constructing the knowledge graph, performing accurate matching on personal portraits of students and the knowledge graph through an incremental self-adaptive matching model, dynamically adjusting recommended learning resources in real time, analyzing collective learning problems through a big data intelligent analysis model, and generating collective teaching suggestions. A three-layer security firewall is adopted to ensure the security of the system; the problem that an existing self-adaptive education system neglects the learning progress and weak links of classes or groups is solved, and intelligent management, personalized learning recommendation and collective problem analysis of education resources can be achieved.

The invention discloses an adaptive learning path recommendation method based on a hypergraph neural network and knowledge tracking, and relates to the field of learning path recommendation, and the method comprises the steps: determining an incidence relation between learning resources, and enabling the incidence relation to serve as an edge of a learning resource undirected graph; the features of the learning resources serve as embedded feature vectors of all nodes of the learning resource undirected graph; updating the embedded feature vector by using a graph neural network to obtain a resource embedded vector, and taking the resource embedded vector as a node feature of a learner hypergraph structure; performing iterative aggregation on the learner hypergraph structure by using a hypergraph neural network to obtain a dynamic resource embedding and learner behavior sequence, generating an initial recommendation list, further generating a candidate learning path set, and generating a Pareto frontier solution set by using a non-dominated sorting genetic algorithm II; and calculating a comprehensive score of each path in the solution set based on a dynamic weight distribution strategy and a comprehensive utility function, and determining an optimal learning path. According to the invention, the accuracy and effectiveness of learning path recommendation are improved.

The invention discloses a teaching information processing system and method based on electrical automation control, and relates to the technical field of intelligent teaching, and the method comprises the steps: collecting multi-modal teaching information, and carrying out the preprocessing; extracting and fusing features by using the preprocessed multi-modal teaching information to generate learning state features; constructing and dynamically adjusting a knowledge graph by using the learning state features to obtain a dynamic knowledge graph; generating a personalized learning path based on the dynamic knowledge graph, recommending learning resources, and recording the utilization rate of the learning resources; generating a comprehensive learning state of the student based on the learning state feature and the learning resource utilization rate; and generating teaching feedback according to the comprehensive learning state. According to the invention, by constructing the dynamic knowledge graph and combining the learning state characteristics and the learning resource utilization rate of the students, the knowledge point mastering condition of the students is dynamically evaluated, the personalized learning path is generated, and the adaptive learning resources are pushed.

The invention relates to the technical field of computers, and provides a multi-modal learning resource intelligent recommendation method and system based on an AI large model, and the method comprises the steps: carrying out the intention analysis of interaction behavior data and user information based on the AI large model, and obtaining a user feature vector; obtaining multi-modal resource data based on the user feature vector, and integrating the multi-modal resource data to obtain a resource representation vector; mining implicit association between resources and knowledge points and a mapping relation between user requirements and target skills based on a knowledge graph in combination with user feature vectors and resource representation vectors to obtain a knowledge network; generating candidate recommended learning resources based on the context awareness information and the knowledge network; and performing sorting optimization on the candidate recommendation learning resources based on the user feature vector and the resource representation vector in combination with the current stage index of the multi-modal resource data, and generating an initial resource recommendation list. According to the embodiment of the invention, the content features of the learning resources are comprehensively captured, and the resource recommendation accuracy is improved.

The invention belongs to the technical field of graph neural networks, and discloses a personalized education resource recommendation method and system based on a graph neural network, and the method comprises the steps: extracting first coding information of a learner and second coding information of a learning resource from a coding information base; inputting the first coding information and the second coding information into a multi-interest contrast graph attention network to obtain a first feature vector corresponding to the learner and a second feature vector corresponding to the learning resource; multiplying the first feature vector by the second feature vector to obtain the preference degree of the learner and the learning resources for educational resource recommendation; the multi-interest contrast graph attention network is obtained by training sample data of historical learning records and learning resources of the learner as a training set, and the multi-interest contrast graph attention network is used for capturing and understanding interest preferences of the learner by fusing various interest information of the learner and attribute information of the learning resources. The educational resource recommendation accuracy and interpretation can be improved.

The invention discloses an AI-driven personalized foreign language learning content generation platform, and belongs to the technical field of artificial intelligence. Comprising the following modules: a data center and user portrait module used for comprehensively presenting personalized learning features and demands; the language ability evaluation module predicts the learning ability in combination with historical learning data and generates a standardized ability evaluation report; the intelligent content service module preferentially recommends the matched existing content, automatically generates personalized learning resources and dynamically adjusts the content difficulty; the learning path planning module continuously optimizes a path planning algorithm through a feedback closed loop to realize long-term formulation and dynamic adjustment of a learning strategy; the real-time monitoring and diagnosis module monitors and analyzes learning behaviors in real time, provides instant feedback and accurate error diagnosis, and continuously optimizes the learning effect based on data driving; and the multi-modal interaction module is used for providing scene-based adaptive interface display and ensuring interaction experience consistency and fluency among the functional modules.

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 discloses a teaching management method and system based on electronic technology courses, and relates to the technical field of teaching management, and the method comprises the steps: collecting and preprocessing multi-source data, constructing an interactive network diagram of students in courses, and carrying out the classification of student groups; constructing a knowledge graph in combination with the course data and the learning behavior data, and initializing a personalized learning path based on the knowledge graph and student group features; and learning resources of different paths are recommended through a multi-view attention mechanism. The multi-level learning path constructed through the knowledge graph ensures that the students grasp knowledge step by step, can adapt to individual differences of the students, performs resource recommendation by using a multi-view attention mechanism, and can realize high-matching-degree resource pushing on the basis of capturing individual features of the students, thereby effectively improving learning efficiency and resource utilization rate, and improving learning efficiency. The dynamic optimization of the learning path is realized, and the adaptability and pertinence of teaching management are remarkably improved.

The invention belongs to the technical field of artificial intelligence education, and particularly relates to a student learning behavior prediction method based on artificial intelligence, and the method comprises the steps: obtaining student learning data and teacher teaching task data; constructing a student agent according to the student learning data, and constructing a teacher agent according to the teacher teaching task data; a dynamic teaching strategy generated by the teacher agent is input into the student agent, and the student agent predicts possible learning behavior change of the student based on a learned behavior mode in combination with a strategy-behavior causal model; the prediction result is linked with a teaching management mechanism, a dynamic learning file is generated, the teacher intelligent agent matches personalized learning resources through an intelligent recommendation algorithm according to the dynamic learning file, and the student intelligent agent receives feedback of students on the learning resources and performs targeted intervention actions in combination with feedback information. Therefore, the problems of insufficient causal reasoning ability, weak calibration ability, insufficient data fusion ability and the like in the prior art are solved.

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.

According to the artificial intelligence knowledge and skill education system and method provided by the invention, through a vivid virtual learning environment constructed by the virtual reality module, a learner can throughout the mind of practice operation of artificial intelligence knowledge and skill, just like being in a real working scene or experimental environment, and the knowledge and skill education efficiency is improved. The interestingness, the participation degree and the concentration degree of learning are greatly improved, knowledge and skills can be better understood and mastered, knowledge and skill information sharing of different learning resource subjects is achieved, the learning effect is improved, perceptual mastering of practical skills can be rapidly achieved at low cost, and the interestingness, the participation degree and the concentration degree of learning are improved. And reference is provided for connection of school education and social practice education. According to the method, individuation and self-adaptive learning deepening are realized, individual differences of learners in a virtual reality environment are fully considered, abundant learner features are analyzed by using a neural network, an individualized learning path which is more practical is generated, and a virtual scene and a learning task are dynamically optimized according to a learning condition.

Embodiments of a content recommendation or selection system are described. The system uses a pipeline of machine learning models to select content for a user. In embodiments, a first user model generates a first score of content categories for the user based on short-term user data. A second user model generates a second score of the categories for the user based on long-term user data. The two scores are combined to select the categories to include on the content user interface. In embodiments, new categories are added to the recommendations based on an exploration-exploitation algorithm. In embodiments, content categories are organized on the user interface in a manner to promote neighborhood diversity. Advantageously, the machine learning pipeline enables independent configurability of various objectives of the content recommendation or selection system and reduces the amount of machine learning resources needed to implement the system.

The invention provides a learning training method and system based on an AI large model, and relates to the technical field of intelligent education. A learner portrait and a knowledge point graph are constructed, a learning path planning module is adopted to plan and optimize a learning path, a target learning path is determined, and a learning material recommendation engine is utilized to perform analysis to obtain customized learning resources. The method comprises the steps of obtaining an AI analysis large model, displaying a target learning path and customized learning resources to a target user for analysis learning, obtaining learning feedback data through a real-time feedback and evaluation module, carrying out dynamic analysis on the feedback data based on a backtracking mechanism adjustment module to obtain a learning optimization strategy, and carrying out training backtracking adjustment. The problem of low learning efficiency caused by insufficient learning resource precision and poor learning feedback and individuation degree is solved, and the effects of improving the learning efficiency and effect and meeting different learning individuation requirements are achieved.

Systems, methods, apparatuses, and computer program products for a switching machine learning functionality based on resource availability. A method may include executing a machine learning functionality, feature, or model at a first network element. The method may also include monitoring a machine learning resource at the first network element that impacts performance of the machine learning functionality, feature, or model. The method may further include indicating, to a second network element based on the monitoring, a machine learning-related resource availability impacting performance of the machine learning functionality, feature, or model. In addition, the method may include receiving, from a third network element, a configuration for an action to be executed by the first network element based on the machine learning-related resource availability impacting performance of the machine learning functionality, feature, or model. Further, the method may include executing the action.

The invention discloses a dynamic course planning method based on an adaptive learning algorithm. The method comprises the following steps: S1, constructing a multi-dimensional data model of a learner knowledge space; s2, forming a knowledge graph represented by fractal characteristics; s3, identifying weak areas and potential knowledge blind spots in the knowledge space of the learner; s4, recursively decomposing the knowledge graph by using a fractal recursive algorithm according to a real-time knowledge state analysis result of the learner in combination with a learning target; s5, optimizing recommendation content and performing dynamic adjustment according to knowledge mastering conditions, weak knowledge points and learning behavior preferences of the learners; and S6, according to the learning resources recommended in the step S5, by monitoring the performance data of the learner in the learning process in real time and combining a performance data analysis result, dynamically adjusting a learning path and resource recommendation content, optimizing learning suggestions and updating the knowledge state of the learner in real time until course planning is finished. According to the method, the personalized learning requirement can be better met, and the learning effect and the cross-scene adaptability of the system are improved.

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 education. The invention discloses a smart vocational education method and a control system thereof, and the system comprises a data collection module which is used for collecting learning data of students, a data processing and analysis module which carries out the comprehensive scoring of the learning data of the students through a scoring formula, and a personalized recommendation module which is used for initializing the learning paths of the students, collecting the learning feedback of the students, and carrying out the comprehensive scoring of the learning data of the students. The system comprises a data acquisition module used for acquiring data, a data processing and analysis module used for adjusting a learning path until a preset learning target is reached, a teaching evaluation module used for comprehensively evaluating various indexes of students, and a system integration module used for integrating the data acquisition module, the data processing and analysis module, the personalized recommendation module and the teaching evaluation module. The intelligent vocational education control system provides highly personalized learning experience for students through student feature recognition, adaptive learning path adjustment and comprehensive teaching effect evaluation. The system can dynamically adjust the learning resources and activities according to the learning characteristics and demands of each student, thereby improving the learning efficiency and motivation.

The invention relates to an automatic education resource generation and dynamic adjustment system and method based on artificial intelligence, electronic equipment and a computer readable storage medium, and the system comprises a resource site management module which is used for integrating an open learning platform, a third-party professional site and college learning resources; the data acquisition module is used for acquiring multi-modal resources related to knowledge points; the digital person making module is used for generating a digital person model and a sound model of the teacher; the resource generation module is used for automatically constructing a PPT and generating a teaching video according to the course large model output; the dynamic adjustment module is used for performing real-time adjustment and intervention on the generated teaching resources according to teaching requirements; and the user interface module is used for interaction operation between students and teachers and the system. According to the method, the generation efficiency of the educational resources is remarkably improved, the individuation and interactivity of the resources are enhanced, the dynamic adjustment and optimization of the resources are realized, and powerful technical support is provided for online education.

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 a junior high school history auxiliary learning method and system based on a knowledge graph, and belongs to the technical field of learning resource management and recommendation, and the method comprises the steps: obtaining junior high school history teaching data; the sources of the junior high school historical teaching data comprise a junior high school historical textbook, a test question book, an educational resource platform and a knowledge base; constructing a junior high school historical knowledge graph mode layer based on the junior high school historical teaching data; preprocessing the junior high school historical teaching data; constructing a junior high school historical knowledge graph data layer based on the preprocessed junior high school historical teaching data and the junior high school historical knowledge graph mode layer; and assisting students in learning based on the junior high school historical knowledge graph data layer. According to the invention, the informatization and intelligence level of history teaching in junior high schools can be improved.