32 results about "Personalized education" patented technology

The invention discloses a risk early warning monitoring and personalized AI education intervention recommendation system for student spoofing and campus violence, and relates to the technical field of campus safety prevention and control. According to the invention, early warning and dynamic tracking of student spoofing and campus violence risks are realized, and the timeliness and accuracy of risk identification are improved; a multi-level risk summarization and visual report generation function is adopted, so that an education manager can quickly master the overall campus safety situation and formulate personalized intervention measures according to different risk levels, thereby effectively reducing the occurrence rate of deception and violence events and creating a safer campus environment; through cooperative operation of AI education intervention strategy generation and a Web push module, real-time identification and accurate intervention of campus spoofing risks are realized, multi-dimensional data such as student individual risk scores, behavior history and environmental factors are integrated, and a highly personalized intervention plan is generated.

The invention discloses an active multi-mode education agent system and method, and belongs to the technical field of artificial intelligence education. The system adopts an end-cloud collaborative architecture and comprises two parts, namely end-side equipment and a cloud micro-service system. The end side device is responsible for collecting multi-modal data (emotion data, behavior data and content data) of a learner in real time and carrying out preprocessing and feature extraction. The cloud micro-service system comprises an API gateway service, a state reasoning service, an active response generation service, a multi-modal content generation service and a TTS service, and is used for processing and analyzing the multi-modal data and generating a personalized active response. According to the method, the confusion state of a learner can be actively perceived, the cognitive state is inferred based on a multi-modal data fusion algorithm, personalized education content is generated according to the cognitive state, a perfect service degradation strategy is provided, and the reliability and learning effect of the system are improved.

The invention discloses an educational guidance scheme generation method and device based on causal inference, relates to the technical field of data mining, can be applied to the educational field, and mainly aims to solve the problem that a personalized educational guidance scheme lacks pertinence and effectiveness. The method mainly comprises the steps of constructing a feature marker causal relationship matrix; obtaining a target feature matrix, a target learning state marking matrix and real-time learning feedback data of the target education object; performing high-dimensional embedding representation learning on the target feature matrix to obtain a high-dimensional embedding representation matrix, and fusing the high-dimensional embedding representation matrix with the target learning state marking matrix and the target feature matrix to obtain a feature marking mapping matrix; generating an initial education guidance scheme through a high-dimensional geometric variational generation network according to the high-dimensional embedded representation matrix and the feature mark mapping matrix; and optimizing the initial education guidance scheme according to the real-time learning feedback data to obtain a target education guidance scheme. The method is mainly used for generating education guidance schemes.

This invention discloses a method for supporting individual educational development based on innate biometric assessment. The method first collects fingerprint image data, which is considered lifelong and unchanging, from individuals. Second, it preprocesses the fingerprint images and extracts quantified feature vectors. Then, it inputs these feature vectors into a pre-trained biometric-educational trait association model to obtain quantified assessment scores for the individual across multiple preset educational trait dimensions. Next, combining the assessment scores, individual age, and developmental goals, a personalized initial educational development plan is generated by matching rules through an educational plan generation rule engine. This invention uses stable biometrics as an objective benchmark and, through a computable model and rule engine, achieves a complete technical process from innate trait assessment to the generation and optimization of long-term, dynamic, and personalized educational support plans, solving the technical problems of traditional methods that rely on unstable acquired assessments and lack a long-term reference system.

Provide a system. 【Solution means】 Means for collecting children's personality, interests, and past behavior data, Means for analyzing the collected data and selecting the most suitable activities for children, Means for proposing the selected activities to the user, Means for analyzing the feedback obtained after the implementation of the activities, Means for updating activity proposals based on the feedback, Means for proposing an individualized education plan, Means for further improving the proposed content based on the execution results of the plan, A system including the above.

The invention relates to the technical field of medical treatment and health information, in particular to a visual interaction-based self-injection success rate evaluation method for elderly diabetics, which comprises the following steps of: constructing an aging-suitable structured video teaching module, presenting an injection process in stages and embedding a standardized question node; a closed loop type Teach-back interaction feedback process is deployed; analyzing the multi-modal behavior data by using a computer vision and natural language processing technology, and extracting key operation parameters and cognitive integrity indexes; establishing a dynamic scoring model, and quantitatively evaluating the self-injection capacity in combination with the link weight and a standard threshold value; and personalized education intervention and post-discharge periodic follow-up visit management are implemented based on an evaluation result. Objective quantification and continuous education of the self-injection skill of the elderly patient are realized, the education effect is improved, and the operation related risk is reduced.

An object of a system according to an embodiment is to provide individualized education adapted to a learner and to realize efficient learning support.SOLUTION: A system according to an embodiment includes a AI tutor, a question answering unit, a teaching material providing unit, a teaching material development supporting unit, and a visualization providing unit. The AI tutor is loaded with the generated AI. The question answering part performs question answering adapted to the learner. A teaching material providing part provides a teaching material adapted to the learner. A teaching material development support part supports teaching material development. The visualization providing unit provides the visualized teaching material.SELECTED DRAWING: Figure 1

The invention discloses an education content personalized recommendation system based on deep learning, and relates to the field of education technology and deep learning, and the system comprises a multi-modal data processing module which collects and processes four types of data, and forms a standardized vector; the target disassembling and mapping module is used for disassembling a macroscopic target into microscopic sub-targets and generating a list and a mapping table; the ability and interest modeling module evaluates ability and constructs an interest model; the resource recombination recommendation module screens and pushes combined resources; the feedback iterative optimization module collects feedback, updates data, optimizes parameters and reversely transmits the parameters; according to the method, a personalized learning path is constructed through a multi-modal data processing and target dynamic disassembly technology, and the requirements of learners are accurately evaluated in combination with multi-dimensional capability quantification and interest modeling; and meanwhile, a resource intelligent recombination recommendation mechanism is adopted, a comprehensive and high-timeliness resource list is generated based on an adaptation degree algorithm, and a feedback closed loop is formed to continuously optimize system parameters, so that the learning experience and effect are remarkably improved.

The invention discloses a new problem scene-oriented multi-modal large model knowledge tracking method, which is characterized in that modal level fusion is realized by adopting a perception coordination module, vision, text and context input are aligned, and rich problem characterization is constructed; problem-level fusion is realized through a cognitive integration module, multi-modal clues and learner interaction history are dynamically integrated, and a potential knowledge state is deduced under the condition of not depending on a question ID; and through an adaptive generalization module, representation is dynamically generated for unseen problems based on semantic similarity, and zero sample migration is realized. Compared with the prior art, the method has the advantages that zero sample knowledge without problems is tracked, so that the MKT-LLM can truly realize zero sample generalization to open and real world education content, and the limitation that an existing knowledge tracking method cannot be generalized to a new problem is effectively solved; the method can be widely applied to an intelligent education system, a self-adaptive learning platform and an interactive tutoring system, and provides technical support for personalized education in an open learning environment.

The invention relates to a question generation method and device, equipment and a storage medium. According to the main technical scheme, the method comprises the following steps of: obtaining a question text and a question solving path of a seed question, determining a knowledge dimension score, a cognitive dimension score and an operation dimension score of the seed question according to the question text and the question solving path of the seed question, and obtaining a knowledge dimension score, a cognitive dimension score and an operation dimension score of the seed question according to the knowledge dimension score, the cognitive dimension score and the operation dimension score of the seed question; and obtaining a comprehensive difficulty score of the seed question, generating a latest question according to the seed question, the comprehensive difficulty score of the seed question, the knowledge point information of the seed question and the target difficulty of the seed question, and generating a question matched with the target difficulty of the seed question according to the latest question and the target difficulty value of the seed question. According to the invention, the difficulty of the questions can be objectively evaluated, and the questions meeting specific difficulty requirements are generated, so that real personalized education is realized.

The invention relates to the related technical field of machine learning and education data mining, and provides a personalized education resource recommendation method and system, and the method comprises the steps: obtaining an interaction record, carrying out the preprocessing, carrying out the semantic feature extraction for the course description text of each learning resource, and carrying out the frequency domain-spatial domain parallel feature enhancement processing, weighting is carried out based on the self-adaptive gating coefficient weight; and finally, through the trained depth prediction model based on the neural factorization machine, predicting to obtain a recommendation score of the user on the learning resources, and further obtaining a recommendation result of the learning resources. According to the method, a neural factorization machine architecture is combined with innovative designs such as a parallel frequency-space domain fusion module, a self-adaptive gating mechanism and a global-local feature enhancement mechanism, deep modeling of student historical interaction behaviors and course content semantics is realized, and high-precision personalized resource recommendation is provided.

The invention provides a personalized education content dynamic generation method and system based on generative AI, and the method comprises the steps: collecting the learning behavior data of a student, and coding the learning behavior data into a behavior vector; on the basis of the behavior vector, constructing a cognitive state vector containing knowledge point mastery degree and error type cognitive semantics; inputting the cognitive state vector into a pre-constructed multi-target content generation intention function, and generating a structured teaching content generation instruction; and inputting the teaching content generation instruction into a generative AI model, and controlling the generative AI model to output personalized teaching content matched with the instruction. According to the invention, the controllability, responsiveness and teaching guide value of personalized teaching of the generative AI system in an education scene are improved.

We provide the system. [Solution] A means of creating personalized educational programs using generative artificial intelligence, A means of providing learning materials while adapting to multiple languages ​​in real time, A means of presenting supplementary information to deepen understanding through natural language interaction with the user, A means of recording educational progress and providing individual feedback, A means of providing a community feature that allows users to share knowledge with each other, A system that includes this.

The invention discloses a personalized AI education intervention platform oriented to children's psychological adaptation, and relates to the crossing field of education and artificial intelligence. The platform is composed of a family end, a school end, a hospital end and a central server, the central server receives multi-source data, and after preprocessing and feature fusion, the psychological risk level is predicted and a disposal scheme is generated in combination with the age stage of children. Multi-source data is divided into structured data, unstructured data and time series data, the structured data, the unstructured data and the time series data are respectively processed to generate vectors, and weighted fusion is carried out according to age stages. And constructing an age stage exclusive prediction model group to perform risk prediction, making a disposal scheme for different age stages and risk levels, and disassembling the disposal scheme into executable tasks of each client. According to the method, multi-dimensional data integration assessment and customized intervention according to age groups are realized, data sharing authority is managed through a guardian, cross-platform collaboration and privacy protection are balanced, and the children psychological risk assessment and intervention efficiency is improved.

The application discloses a diabetes education and training platform combining a virtual scene and an AI video two-dimensional code, and belongs to the technical field of digital health. The platform comprises the following modules: a user data acquisition module, which acquires multi-source data of users; a user portrait construction module, which constructs a dynamic user portrait based on data; an AI video generation module, which automatically generates a personalized education video according to the portrait and a knowledge graph; a two-dimensional code generation and distribution module, which generates a traceable dynamic two-dimensional code for the video; a mobile terminal, which is used for playing the video and providing virtual scene interaction; a virtual scene interaction module, which constructs a simulated real scene for user practice; a learning feedback and optimization module, which collects user behavior data and physiological improvement data, and continuously optimizes the user portrait and the video generation model. Through personalized content generation, scene-based practice and closed-loop optimization, the application solves the problems of traditional education content homogeneity, low participation and separation of knowledge and practice, and significantly improves the education effect and user compliance.

The invention relates to the technical field of attention detection, in particular to an fNIRS-based student classroom attention assessment system, which comprises a multi-channel fNIRS signal acquisition headband, a signal preprocessing module, a feature extraction module, an attention state assessment model, a data storage and management module and a visualization and feedback module, the attention state evaluation model is a sub-age evaluation model library formed by a plurality of sub-models of different age groups; through adaptive adjustment of filter parameters, a motion artifact correction strategy and an evaluation model, brain development characteristics, physiological noise characteristics and behavior mode differences of different age groups are fully considered, the accuracy and applicability of attention evaluation of various student groups are remarkably improved, and real personalized education monitoring is realized.

The system of the embodiment aims to cater to children's individual learning needs and share information with parents and educators in real time. [Solution] The system according to the embodiment includes a collection unit, a generation unit, a dialogue unit, and a sharing unit. The collection unit collects children's behavioral data or learning history. The generation unit generates an individual educational plan based on the data collected by the collection unit. The dialogue unit advances learning by interacting with the children based on the educational plan generated by the generation unit. The sharing unit shares the learning progress and growth recorded by the dialogue unit with parents or educators in real time.

A system according to an embodiment aims to provide personalized education based on the student's learning style and comprehension.SOLUTION: A system includes an understanding unit, a grasping unit, a generation unit, and a reporting unit. The understanding unit understands the textbook or lesson content. The grasping part grasps the learning style and the degree of understanding of the student based on the information understood by the understanding part. The generation unit generates a customized homework or test based on the information grasped by the grasping unit. The reporting unit reports the learning progress to the parent based on the homework assignment and the test result generated by the generation unit.SELECTED DRAWING: Figure 1

The invention provides a college student growth track intelligent tracking method and system, and belongs to the field of student management, and the system comprises a terminal module and a computer module which are in communication connection. Real-time and multi-dimensional collection of campus scene data is realized through the smart phone terminal, and deep fusion with multi-source heterogeneous data such as subjective evaluation, academic performance and practical experience of teachers is realized, so that the depth and interpretability of growth analysis are remarkably improved; a user is supported to drill and analyze layer by layer through a visual interface, and comprehensive interpretation from a macroscopic trajectory to a microcosmic cause is realized; meanwhile, safe sharing and role operation of data are achieved through the authority management module, pertinence and timeliness of teacher evaluation are enhanced, intelligent and systematic support is provided for self-cognition, academic early warning and personalized education of students, and the limitation that data are scattered, static analysis is conducted and causal association is difficult in a traditional method is effectively overcome.

The present application relates to the technical field of educational big data analysis and personalized education, and discloses a deep learning effect correlation mining method based on children's animation watching behavior data, which specifically comprises the following method steps: step one, constructing a three-dimensional data model, the three dimensions including behavior dimension, content dimension and effect dimension; step two, through a group difference analysis model, an effective animation watching behavior path is identified for children groups of different ages. The present application can comprehensively and accurately evaluate the learning process and effectiveness of children by constructing a three-dimensional data model (behavior dimension, content dimension and effect dimension). Experimental data shows that after using the method, the vocabulary standard rate of the small child group (0-6 years old) is improved to 68%, and the long-time immersion mode conversion rate of the big child group (7-12 years old) is improved by 75%. This data-driven optimization mechanism ensures that the learning path is highly matched with the cognitive development law of children, thereby significantly improving the English enlightenment effect.

The invention discloses a personalized question practice scheme generation method for intelligent integration of wrong questions, and relates to the technical field of personalized education, and the method comprises the steps: receiving a user wrong question record set, analyzing question stems, options and answers, and mapping knowledge points to a multi-level knowledge system tree; the occurrence frequency of the knowledge points is calculated, and statistics is carried out on error answer option distribution modes of the associated wrong questions; determining a weak knowledge node set and a typical error understanding mode corresponding to the weak knowledge node set; on the basis, matched correction exercise questions are retrieved in the question bank, difficulty adaptation adjustment is carried out, and a preliminary personalized exercise question sequence is generated; according to the method, a deep cognitive misunderstanding area is recognized by analyzing a wrong answer distribution mode, and structural weaknesses are positioned by utilizing a knowledge system tree, so that a personalized exercise scheme capable of accurately correcting wrong understanding and consolidating a knowledge system is generated.

This invention discloses a safety education content delivery system and method based on driving behavior analysis, relating to the field of traffic safety technology. The proposed solution includes a driving data collection module, a driving behavior analysis module, a safety education resource library module, a personalized delivery decision module, a content delivery execution module, and an effect feedback optimization module. This invention not only accurately extracts driving behavior characteristics and assesses risks through multi-dimensional data collection and advanced models, identifying driver behavioral shortcomings and potential hazards, but also dynamically matches driver risk levels and learning styles based on a tagged resource library and priority model to deliver personalized educational content, solving the problem of homogenization. Furthermore, it constructs a closed-loop feedback optimization mechanism, continuously optimizing models, materials, and strategies based on feedback data to improve educational effectiveness. It adapts to various driving scenarios and usage habits, exhibiting strong compatibility and practicality, effectively correcting non-standard driving behaviors, and reducing the accident rate.

A system according to an embodiment aims to provide personalized teaching by analyzing sensor data and feeding it back to an agent or xR.SOLUTION: A system includes a collection unit, an analysis unit, and a feedback unit. The collection unit collects sensor data. The analysis unit analyzes the data collected by the collection unit. The feedback unit feeds back to the agent or xR based on the analysis result obtained by the analysis unit.SELECTED DRAWING: Figure 1

A system is provided.SOLUTION: A system comprising: means for providing educational support functions; means for automatically scoring student submissions; means for automatically generating educational materials; means for collecting student learning data; and means for analyzing the collected data and providing personalized educational plans.SELECTED DRAWING: Figure 1

This invention belongs to the field of internet education application technology and relates to a time-aware, personalized, dynamic recommendation method for educational resources. It includes defining a user-resource interaction graph, constructing an educational domain knowledge graph, designing a dynamic encoding mechanism based on relative time intervals, designing a time-aware attention weighting calculation mechanism, initiating fine-tuning at a target time point, designing a learnable gating transition mechanism, designing a sliding window-based embedding interpolation update strategy to estimate the probability of user u interacting with item i, and employing an optimization objective based on Bayesian Personalized Ranking (BPR) loss during the pre-training and fine-tuning stages. This invention constructs a time-enhanced dynamic graph recommendation framework, introducing a time encoding mechanism and a graph structure fine-tuning mechanism based on the educational knowledge graph, to achieve accurate modeling of the learner's knowledge state evolution process, thereby improving the accuracy, real-time performance, and personalization of educational resource recommendations.

The system according to this embodiment aims to provide customized educational content tailored to the needs of individual learners. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, a provision unit, and a tracking unit. The reception unit receives user input. The generation unit generates customized educational content based on the information received by the reception unit. The provision unit provides the educational content generated by the generation unit. The tracking unit tracks learning progress based on the educational content provided by the provision unit and adjusts the learning plan as necessary.

The invention provides a user service recommendation method and device, equipment and a medium, and the method comprises the steps: analyzing original data from a plurality of heterogeneous education data sources, and generating a standardized intermediate data stream; performing semantic alignment on entities in the intermediate data stream and entities in a pre-constructed education knowledge graph to generate an aligned knowledge graph; finely adjusting the pre-training language model by using the educational domain corpus to generate a domain enhancement model; wherein the aligned knowledge graph is used as a constraint in the fine tuning process; and in response to a service request of a current user, generating a personalized education intelligent service for the current user based on the service request, the aligned knowledge graph and the domain enhancement model.

An object of the system according to the embodiment is to automate creation of an individual education program and a learning print for a student with a disability.SOLUTION: A system according to an embodiment includes an education program generation unit and a learning print generation unit. The educational program generation unit automatically generates an educational program based on the degree of student's disability. The learning print generation unit automatically creates a learning print based on the educational program generated by the educational program generation unit.SELECTED DRAWING: Figure 1

The application relates to the technical field of personalized education, and discloses a personalized learning monitoring system based on user behavior data, which comprises a data collection module, a learning target setting module, a self-evaluation and feedback module and an emotion monitoring module.The data collection module is used for collecting learning behavior data and physiological data of a target user and integrating interactive log data in a learning platform.The learning target setting module sets a personalized learning target system comprising short-term and long-term targets.The self-evaluation and feedback module guides the target user to regularly perform self-evaluation and helps the target user to understand current learning progress.The emotion monitoring module identifies current emotional fluctuations of the target user.A dynamic task adjustment module dynamically adjusts the difficulty, content and rhythm of learning tasks based on the personalized learning target system, current learning progress and current emotional fluctuations, and generates real-time adaptive learning task flows.The application automatically identifies state changes, pushes matching tasks, and greatly reduces cognitive burden and management cost.

The invention discloses a personalized education recommendation method, system, medium and device, the system comprises a data acquisition unit, a knowledge graph construction unit, a propagation unit, a course scoring unit and a personalized education recommendation scheme generation unit, the data acquisition unit is in communication connection with the knowledge graph construction unit; the knowledge graph construction unit is in communication connection with the propagation unit, the propagation unit is in communication connection with the course scoring unit, and the course scoring unit is in communication connection with the personalized education recommendation scheme generation unit. According to the method and the system, knowledge graph information such as first modification and second modification among courses is fused, and the influence of social relations of students on course selection is introduced, so that the relevance between recommendation results and course targets is clearer, and meanwhile, the recommendation results are more personalized and better fit the characteristics and requirements of the students.