464 results about "Personalized learning" patented technology

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 embodiment of the invention provides an intelligent learning path recommendation method and system based on a dynamic state. The method is applied to the technical field of intelligent learning recommendation, and comprises the following steps: calculating a skill adaptation weight, a load weight and an emergency degree weight in real time based on a dynamic state of a target employee, including a current skill improvement condition, a workload and a task emergency degree; combining the skill matching degree, the learning strength and the task association degree of each learning unit in the candidate learning path, and performing comprehensive evaluation by using a multi-weight scoring function; and sorting the paths according to the comprehensive score, generating a personalized recommended learning path set and sending the personalized recommended learning path set to an employee terminal, thereby realizing intelligent learning path recommendation with dynamic adaptation and accurate matching. According to the scheme, personalized learning path pushing aiming at actual post requirements and working states of the employees can be realized, correlation, urgency and acceptability of learning contents are improved, learning efficiency and task adaptability are remarkably enhanced, and the employees are helped to quickly compete with post targets.

The invention discloses a writing brush calligraphy practice correction system based on real-time handwriting analysis. The system comprises a sensing layer used for collecting handwriting tracks, physiological signals, environmental parameters and ink mark characteristics; the edge calculation layer is used for executing noise filtering, coordinate system normalization, multi-modal data alignment and feature primary extraction through an Apache Kafka data pipeline; and the algorithm analysis layer is used for carrying out super-long calligraphy stroke sequence modeling by adopting an S4 architecture time sequence model, introducing a Neural ODE module for modeling, capturing dynamic characteristics in a continuous pen wielding process, constructing a multi-physics-field coupled PINN framework, constraining neural network prediction through a physical loss function, deploying a dual-stage characteristic extractor to extract high-order characteristics, and extracting the high-order characteristics. Book style features are extracted, and the current practicing book of the user is classified in real time; the intelligent correction layer is used for generating a dynamic correction suggestion, constructing a personalized learning path, realizing self-adaptive scoring and providing aesthetic dimension feedback at the same time; and the user interaction layer is used for providing an AR correction interface.

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 intelligent education, and discloses a teaching programming system based on large language model assistance and a teaching recommendation method. According to the system, a multi-dimensional ability graph and a knowledge point topology network are constructed, and a large language model is utilized to carry out multiple rounds of intention analysis and semantic reasoning to generate a personalized learning path sequence. The system continuously tracks a learning track, realizes continuous optimization of a teaching strategy by dynamically calibrating strategy parameters, and actively configures special reinforcement resources based on track prediction. According to the system, a breakthrough from static recommendation to dynamic adaptation is realized, the accuracy of learning path planning and the timeliness of teaching intervention are improved through deep semantic understanding and a closed-loop optimization mechanism, so that the programming teaching system can really understand learning requirements and adapt to changes of learning states in real time, and the teaching efficiency is improved. And the knowledge mastering firmness and the learning efficiency are improved.

The invention relates to the technical field of personalized learning, and discloses a personalized learning path recommendation system based on a knowledge graph. The system comprises a user portrait module, a knowledge graph construction module, a path generation module and an effect evaluation module. The user portrait module obtains user learning parameters of the learner; the knowledge graph construction module receives learning domain information, constructs a domain knowledge graph based on user learning parameters, sets knowledge node granularity, and performs association analysis on knowledge units to obtain association weight values of the knowledge nodes; the path generation module dynamically plans a learning path according to the association weight value and adjusts the learning path in real time in learning; and the effect evaluation module monitors the knowledge point mastering degree, the learning progress deviation and the path completion rate of the learner, and performs abnormal early warning. The system can provide learning paths fitting individual differences for learners, and improves the pertinence and flexibility of learning.

The invention belongs to the technical field of intelligent voice processing, and particularly relates to a spoken language pronunciation training and correcting system based on intelligent equipment, which acquires a user rhythm feature set including pitch change rate, accent intensity, pause duration and intonation contour by acquiring spoken language audio data sent by a user for a target text, and corrects the spoken language pronunciation training and correcting system. The method comprises the following steps: acquiring spoken language audio data, converting the spoken language audio data into a phoneme sequence aligned with target text time, generating a rhythm deviation degree report according to comparison evaluation of a user rhythm feature set and the phoneme sequence, an execution intonation mode, accent distribution, speech stream sound change and speech speed rhythm, and determining the rhythm deviation degree according to a rhythm problem type in the report in combination with user historical learning data. And forming and outputting a correction scheme including a text prompt, a targeted minimum contrast training unit and a listen-and-read simulation task, solving the problem of weak capability of correcting hyper-phoneme in a second language spoken language of an adult, and improving the authentic and fluency of the spoken language, thereby realizing efficient personalized learning.

The invention provides a recommendation method and system fusing big language model reasoning and multi-source trajectory information, and the method comprises the steps: firstly obtaining user historical learning behaviors and static attribute information after receiving a user recommendation request, and constructing a static interest vector; in combination with the initial feature vector of the learned knowledge point and the map enhancement vector of the first-order neighbor node of the knowledge map, generating explicit and map extension interest vectors, and fusing to obtain a user interest vector; screening N unlearned knowledge points to form a candidate set through similarity analysis of user interest vectors and unlearned knowledge point vectors and / or reasoning of a large language model on user association information; and screening the target knowledge points through mastery degree verification of the pre-modified knowledge points, and outputting a recommendation result after sorting. According to the method, recommendation accuracy and suitability are improved, and personalized learning requirements are met.

The invention provides a personalized learning path planning system and method, and belongs to the technical field of emerging software and emerging technical services, and the system comprises a data acquisition module which is used for collecting learning behavior data of a terminal user, obtaining a learner portrait package based on the learning behavior data, and sending the learner portrait package to a server; the path planning module is used for carrying out node matching on the learning ability feature vector and a pre-constructed knowledge and skill map, outputting a to-be-learned content node set, dividing learning advanced levels and generating learning path description containing to-be-learned nodes and the advanced levels, and the path generation module is used for generating a primary path planning scheme, the scheme feasibility verification module is used for carrying out scheme feasibility verification in combination with the learning advanced level and the path adaptation parameters, and then outputting a final executable path scheme, and the scheme execution module is used for executing the final executable path scheme and controlling learning content pushing and progress adjustment. The problems that in the prior art, personalized learning path planning is not high in precision, not high in adaptability, lack of dynamic optimization and the like are solved.

Processing circuitry of a learning platform may be configured to maintain a graph database describing student learners. Processing circuitry may obtain new student learner data and load the data into the graph database. Processing circuitry may receive an engagement or interaction from the new student learner and responsively extract new learnings about the new student learner which are loaded into the graph database. Processing circuitry may receive an inquiry from the new student learner and in response, extract the new student learner data and the new learnings from the graph database and contextualize, using a large language model, a learning unit from the educational content provided by the learning platform as a response to the inquiry using the new student learner data and the new learnings. Processing circuitry may further return the learning unit contextualized by the large language model to the new student learner.

The invention provides an anesthesia virtual simulation training system fusing knowledge, skills and a thinking closed loop. The anesthesia virtual simulation training system comprises a medical knowledge base module, a clinical thinking module, a skill training module, an examination question brushing module, a knowledge graph module and an intelligent platform bottom layer framework. The intelligent platform underlying architecture comprises a data middle platform, an AI engine and a 3D engine, collects student behavior data of each module, constructs a dynamic student ability portrait through a gradient boosting tree algorithm and a collaborative filtering recommendation model, analyzes knowledge blind areas and skill shortages, plans a personalized learning path and pushes targeted training content, and provides a personalized learning result. A closed-loop process of evaluation, learning, practice and re-evaluation is formed; and deep fusion of theoretical knowledge, clinical thinking and skill operation is realized through a cross-module collaboration mechanism. The problems that traditional anesthesia teaching is high in practical operation risk, scattered in resource and insufficient in individuation are solved, and the clinical comprehensive ability and teaching quality of anesthetists are effectively improved.

The invention relates to the technical field of intelligent education, in particular to an AI-based nursing teaching scene construction and evaluation system. According to the system, a dynamic case script is generated through historical case data, and a virtual patient physiological model is constructed; creating three teaching scenes of community follow-up visit, emergency rescue and traditional Chinese medicine nursing fusion by using a mixed reality technology; a personalized learning portrait of the student is constructed through a pre-test, and accurate matching between the ability of the student and the teaching content is formed; a double-track system skill training module is built, and AI tutor guidance and teacher guidance are integrated; and an ability evaluation system based on operation data is established, and intelligent pushing of adaptive training content is realized. The problems that in traditional nursing teaching, scene authenticity is insufficient, personalized training is insufficient, and clinical thinking is difficult to cultivate are solved, and the clinical operation skill and decision judgment ability of students 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 discloses an AI-based computer basic course personalized learning path recommendation method and system, and belongs to the technical field of AI-based data processing. According to the system, behavior data, cognitive data and course interaction data of a learner are acquired through a multi-dimensional data acquisition module, a computer basic course knowledge point association network is established in combination with a dynamic knowledge graph construction module, and a personalized learning path is generated by using an improved deep reinforcement learning algorithm. And the path is dynamically adjusted through the real-time feedback module. The core of the method is that a learner portrait is fused with space-time correlation features of a knowledge graph, a cognitive evaluation model is updated in real time through a Bayesian network, the problems that in a traditional recommendation method, paths are solidified, and the dynamic learning state of an individual is ignored are solved, more accurate personalized learning guidance is achieved, and the learning efficiency and effect of a computer basic course are improved.

The invention provides a test paper correction and personalized learning method based on OCR and a large language model. The method comprises the steps of S1, test paper scanning and preprocessing; s2, performing OCR (optical character recognition) and data structuring; s3, intelligently correcting the large model; s4, performing multi-dimensional statistical analysis; and S5, generating and pushing variable questions. According to the method, technologies such as OCR (Optical Character Recognition), a large language model and a knowledge graph are fused, so that the whole process intelligence from test paper correction to personalized learning guidance is realized, the efficiency and accuracy of education evaluation are improved, and technical support is provided for personalized teaching.

The invention relates to the technical field of artificial intelligence education, and discloses a personalized online education system and method based on artificial intelligence, and the system comprises a data collection and perception module which is used for collecting the dynamic behavior data and multi-mode perception data of a learner; the knowledge processing and constructing module is used for processing teaching contents to construct a structured knowledge graph; the user modeling module is used for constructing a dynamic learner portrait comprising a knowledge state model and a cognitive-emotional state model for the learner based on the collected data and the constructed knowledge graph; and the intelligent decision and intervention module is used for generating and executing personalized learning intervention based on the dynamic learner portrait. According to the method, by fusing multi-mode cognitive state perception, reinforcement learning intervention decision and a knowledge graph dynamic evolution mechanism, accurate and timely personalized intervention for learners and intelligent guidance of optimal learning strategies are realized.

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 education knowledge graph quality evaluation method based on confidence evaluation, and relates to the technical field of natural language processing, and the method comprises the steps: S1, obtaining multi-source education data, and achieving the data standardization through a subject term unified dictionary and an education field exclusive relationship type mapping table; s2, constructing a dimension weight adaptive model, and dynamically adjusting weights of accuracy, integrity and consistency in combination with subject types, education stages and application scenes; s3, based on the standardized data and the real-time weight, respectively evaluating the confidence of each dimension through double mechanisms, three sub-dimensions and double detection; s4, fusing to obtain a comprehensive confidence coefficient, and if the comprehensive confidence coefficient does not reach the standard, adjusting parameters according to priorities and returning to re-evaluation; and S5, updating a model coefficient according to teacher feedback and student learning effect data to realize iteration. According to the method, the evaluation suitability and accuracy are improved, and reliable quality guarantee is provided for educational knowledge graph supporting teaching and personalized learning.

The invention provides a family-school interaction classroom note analysis system based on OCR and a large language model. The family-school interaction classroom note analysis system comprises a note scanning module, an OCR recognition module, a note analysis module and a family-school pushing module. Through linkage of all the modules, automatic analysis of classroom notes, efficient cooperation of families and schools and personalized learning closed loop are achieved, and transformation of education from unified teaching to personalized cultivation is promoted.

The invention belongs to the technical field of education data mining and personalized learning, discloses a learner knowledge cognition level diagnosis method and system based on cross-scale learning performance dynamic modeling, and has higher accuracy in the aspects of learner cognition state prediction and knowledge point difficulty assessment. Through a selective state space modeling mechanism and cross-scale historical learning income feature engineering, cognitive change tracks of students in various learning scenes can be accurately captured; and the robustness, convergence efficiency and long sequence processing capability of the model in learner performance prediction are improved. The method can be widely applied to a personalized education platform, a self-adaptive learning system and an intelligent teaching auxiliary tool, provides accurate student learning state analysis for teachers, optimizes learning path design, and improves the teaching effect.

The invention provides an artistic course personalized learning path method and system based on deep learning. Firstly, an artistic course learning basic information set of a learner and a preset artistic course module library are acquired; calling a pre-trained deep learning course dynamic association model to generate association strength description of the learning basis of the learner and each course module; screening and sorting based on association strength description to form an initial learning module sequence; acquiring a real-time learning behavior data set of the first course module learned by the learner, and generating a module adjustment signal; and adjusting the initial learning module sequence according to the module adjustment signal to obtain a personalized learning path adaptive to the real-time learning state of the learner, and generating a personalized learning path document comprising a course module learning sequence and module connection guidance, thereby realizing accurate customization and dynamic optimization of the learning path, and improving the learning efficiency. And the learning effect of artistic courses is improved.

The invention belongs to the technical field of education course recommendation, and discloses a personalized learning path recommendation method based on a knowledge graph, which comprises the following steps: acquiring a knowledge point set according to a learning target, and constructing a basic knowledge graph; matching the first similar group, and obtaining learning records of the first similar group to form a first reference record set; marking difficulty values for the knowledge points according to the mastery degree in the first reference record set, and generating a knowledge point learning sequence; taking the first reference record set as a target reference set, and matching an optimal learning track for each knowledge point to obtain a comprehensive learning path; after learning records of the learner are obtained, matching a second similar group, and obtaining a second reference record set; and optimizing the comprehensive learning path according to the second reference record set, and adding review nodes for the knowledge points with low mastery degree. According to the method, knowledge point difficulty values are marked based on actual learning data, a learning sequence of cognitive load balance is generated, a dynamic recommendation process from general to individuation is realized, and learning efficiency and experience are improved.

The invention discloses a teaching evaluation method and system based on artificial intelligence, and relates to the technical field of teaching evaluation, and the method comprises the steps: constructing a student learning analysis time series data set based on an LMS learning management system integration platform; constructing a dynamic student learning behavior evaluation model by using an unsupervised learning algorithm, and generating a student personalized learning portrait; based on the personalized learning portrait of the student, combining the historical score data of the student and the learning progress of the student, utilizing a deep neural network optimization model to realize dynamic real-time prediction and self-adaptive adjustment of the learning state of the student, and generating a continuously updated student learning state evaluation map; and based on the continuously updated student learning state evaluation map, analyzing the relationship between the student learning progress and the teacher teaching effect, dynamically adjusting the student learning strategy and the teaching plan, and generating an artificial intelligence teaching evaluation scheme. The method has the beneficial effects that personalized and scientific teaching evaluation and optimization are realized, and the teaching effect and the learning achievement of students are improved.

The invention relates to the technical field of education science and technology recommendation systems oriented to personalized learning path optimization, and particularly discloses an education science and technology recommendation system oriented to personalized learning path optimization. The method aims at solving the problems that an existing recommendation system is difficult to dynamically perceive a cognitive state, knowledge structure semantics and dependency relationships are ignored, and the recommendation precision is low due to data sparsity. The system comprises a multi-source data acquisition and fusion module, a dynamic cognitive state evaluation module, a knowledge graph construction and semantic enhancement module, a path generation and optimization decision module and a self-adaptive execution and feedback adjustment module. Through multi-source data fusion, real-time cognitive state quantification, knowledge graph semantic modeling, multi-target optimization path generation and closed-loop feedback adjustment, accurate recommendation of personalized learning paths is realized, and the continuity, rationality and educational effectiveness of the paths are effectively improved.

The invention discloses an enterprise training evaluation system and method fusing RAG and an intelligent agent, and belongs to the technical field of training evaluation of artificial intelligence. The system comprises a computing node, a storage unit, a network interaction unit, a document vectorization processing module, a dynamic question setting module, a real-time semantic scoring module, a training effect visualization module and a learning recommendation module. The method comprises the steps of document preprocessing, dynamic question setting, real-time scoring, effect visualization and learning recommendation. Through deep fusion of RAG retrieval and agent decision, personalized question setting, multi-dimensional instant scoring, long-term knowledge state tracking and early warning and personalized learning recommendation based on student knowledge states are realized, and an evaluation-feedback-optimization closed loop is formed. According to the method, the question setting pertinence, the scoring accuracy (up to 92.3%) and the training efficiency (compressing the training time by 40%) are remarkably improved, and the method is suitable for various enterprise training scenes.

A method for guiding and constraining an Artificial Intelligence (AI) engine to deliver personalized learning recommendations based on a user's performance and behavior across online learning platforms. The method includes integrating a framework to enable communication between platforms and a learning system, collecting assessment and session data such as scores, time spent, answer choices, and navigation behavior. A data collection module parses this information to identify learning patterns, difficulties, and unproductive behaviors. Based on the analysis, a prompt is generated to guide the AI engine in producing personalized, actionable recommendations. These recommendations are presented to the user in real time via a popup window within the learning platform, providing adaptive, context-aware support during learning session.

The invention provides a course dynamic optimization method and system, an electronic device and a storage medium, and relates to the technical field of online education, and the method achieves the quantitative association of a course unit-knowledge fragment through a soft mapping matrix, enables the perplexity to be bidirectionally transmitted between the course unit and the knowledge fragment, and achieves the quantitative traceability. The comprehensive confusion degree is obtained by fusing the confusion degrees of the course unit layer and the knowledge fragment layer, so that the high-confusion course unit can be positioned more accurately and explainably, and fine optimization of the course can be realized; through a course optimization decision with cost constraint, automatic generation of a course optimization scheme with controllable cost is realized; besides, personalized learning path optimization of a course structure layer is realized, and a course optimization strategy continuously acts on subsequent learners, so that dynamic evolution of course contents and personalized learning paths is realized.

The invention relates to the technical field of intelligent teaching, and provides a self-adaptive personalized teaching system based on AI and a knowledge graph, and the system comprises a knowledge graph construction module, a student portrait module, a self-adaptive recommendation module, a teaching interaction module, and a management module. The knowledge graph construction module comprises a data processing unit, a knowledge extraction unit and a graph storage unit; the data processing unit is used for carrying out preprocessing such as word segmentation and stop word removal on the teaching text; and the knowledge extraction unit is used for extracting knowledge point entities and relationships between the entities from the preprocessed text through a natural language processing technology. The knowledge points are subjected to fine-grained modeling through the knowledge graph, the AI algorithm is combined to analyze student learning behaviors and evaluation data, knowledge vulnerabilities and learning characteristics of students can be accurately captured, the error rate is lower than that of a traditional method, the self-adaptive recommendation module generates personalized learning paths based on the reinforcement learning algorithm, and the learning efficiency is improved. And repeated learning of students on invalid knowledge points is avoided.