71 results about "Educational data" patented technology

The invention provides a multi-modal education evaluation system and method based on super-automation and federal cognitive calculation, and the system integrates multi-source heterogeneous data collection, a federal cognitive calculation engine, an automatic evaluation workflow engine and a privacy enhanced data processing architecture. And comprehensive acquisition, efficient processing, accurate evaluation and safety protection of education data are realized. Through the LSTM-Bayesian hybrid model of dynamic parameter adjustment, intelligent feature engineering, interpretability analysis, reinforcement learning strategy optimization and other technologies, the system can generate personalized learning path planning and an adaptive teaching intervention scheme, and the teaching effect and learning efficiency are improved. And meanwhile, the security and privacy of the education data are ensured by adopting privacy protection technologies such as differential privacy and homomorphic encryption.

The invention discloses an intelligent question bank retrieval and recommendation system based on an artificial intelligence knowledge graph, and relates to the technical field of artificial intelligence education. Comprising a knowledge graph construction module which is used for processing original education data and constructing a neighborhood knowledge graph comprising a hard preposition relation and a soft incidence relation; the user knowledge state graph construction module is used for constructing a user personal knowledge state graph isomorphic to the domain knowledge graph, and dynamically calculating a mastery index of each knowledge node through a deep knowledge tracking model based on user historical answer data; according to the method, by constructing the domain knowledge graph containing the hard preposition relation and the soft incidence relation, discrete knowledge points are organized into the structured network conforming to the cognitive law, so that the system can understand and follow the internal logic between knowledge, and a learning path which is clear in organization, efficient and coherent is generated.

The invention belongs to the technical field of information management, and discloses an intelligent education activity management system and method based on a large model. The system comprises four layers of data acquisition and access, processing and storage, intelligent analysis and decision, and user interaction and application. Education data are analyzed through fusion of transfer learning and a large model, an activity process is optimized based on a semantic network and the large model, and personalized learning services are provided by utilizing emotion interaction and the large model. The method is implemented through the steps of data acquisition, processing, model training, application and the like. Experiments show that the method can significantly improve student scores, improve educational activity organization efficiency and student satisfaction, provide powerful support for intelligent management of educational activities, and have good application prospects.

The invention discloses an educational data distributed storage method based on a block chain, and the method comprises the following steps: S1, collecting and preprocessing educational data, and generating a standardized data unit; s2, performing hierarchical label labeling and encryption on the data unit to generate an encrypted data block; s3, performing hash operation on the encrypted data block, extracting an abstract, a tag and a timestamp, constructing an index and writing the index into a block chain; s4, generating a unique path based on the index entry, and mapping the encrypted data block to a plurality of distributed nodes for storage; s5, constructing a bidirectional mapping relation in combination with the index entry and the binding path, and adding access control parameters; s6, the access identity is recognized, the authority is verified, an authorization identifier and a decryption token are generated, and control parameters are updated; and S7, the access party obtains the encrypted data block according to the authorization identifier, decrypts and uses the encrypted data block, and writes an access behavior into the block chain. According to the invention, encrypted storage, intelligent authorization and traceable sharing of education data are realized, and security, reliability and management efficiency are improved.

In a first aspect, an apparatus for delivering adaptive educational content using an automated tutoring model includes a processor and a memory communicatively connected to the processor is presented. The memory contains instructions configuring the processor to receive a prompt from a user. The processor, extract linguistic data from the prompt through an automated tutoring model. The processor is configured to determine, through the automated tutoring model, relevant educational data for the user based on the linguistic data of the prompt. The automated tutoring model is configured to classify the user to a learner group based on the prompt. The automated tutoring model is in communication with and selects the relevant educational data from an educational database based at least on the learner group. The processor is configured to present the selected relevant educational data to the user through a display device in communication with the processor.

The invention discloses a label-based education data dynamic analysis method and system, and belongs to the technical field of big data analysis and education business system optimization, and the method comprises the steps: S1, constructing a multi-dimensional label system, and creating an extensible tree-shaped label group structure; the tree-shaped label group comprises administrative level labels and business labels, and the administrative level labels at least comprise a provincial level, a municipal level, a district and county level and a school level; s2, performing data fusion and dynamic calculation, and configuring a dynamic label actuator driven by a threshold value; s3, performing intelligent statistical display, and performing dynamic labeling on the original data through an assembly line processing engine; through the analysis model, generating a multi-dimensional cross analysis result based on the label hierarchical relationship; and S4, dynamic feedback, including real-time early warning and developability evaluation report. According to the method, the statistical requirements of various levels of organizations under multiple dimensions are supported, and the flexibility of data query and statistics is remarkably improved.

A smart training student data integrated management system of the present invention belongs to the technical field of smart training and education data analysis, and comprises a learning track data acquisition module used for acquiring student interaction data of students in a training process; the dynamic cognition map construction module is used for constructing a dynamic cognition map representing the ability of the trainee by performing quantitative analysis on the dynamic association strength between skills in response to the trainee interaction data; the emergence capability identification and prediction module is used for calculating the emergence index of the skill cluster based on the dynamic cognitive map and identifying a capability emergence cluster; the self-adaptive evaluation engine module is used for dynamically updating the weight of the evaluation dimension in response to the emergence index, and robustness and universality of similarity calculation are guaranteed.

An education information system includes a data reading unit having a housing with a flat top surface and a plurality of wireless data reading units in the housing. The data reading unit is configured to wirelessly read educational data from at least one card that contains the education data, and to further transmit the educational data to a computer screen for display. Each educational data-containing card includes an embossed icon on a top card surface, the embossed icon being representative of the particular educational data stored thereon.

A mastery level determining method to assess user mastery of educational skills by centralizing and processing data from diverse educational platforms is disclosed. The method involves collecting user educational data from various sources such as online learning platforms, external assessments, and internal quizzes. This data is then normalized to adapt to the educational standards and ingested into a structured mastery framework. The recent and reliable data is evaluated to determine skill mastery states, prioritizing newer and more reliable information. Subsequently, reliability and recency scores are calculated and assigned to each educational data. The method further categorizes user mastery skills into distinct states, providing an overview of the user's knowledge. The simplified mastery states and associated tags are accessible to the user, facilitating personalized learning experiences and assessments within the online learning platforms.

The invention discloses an intelligent student evaluation method driven by multi-source heterogeneous education data, relates to the technical field of artificial intelligence, and aims to solve the problems that in the prior art, data semantics are split, a model cannot be explained, and application deviates from the essence of a child. According to the method, a three-layer architecture is constructed: firstly, semantic unified modeling is performed on multi-source heterogeneous data based on an education ontology to form a student knowledge graph; secondly, designing an interpretable multi-modal model fused with educational priori knowledge, realizing academic risk early warning and ability portraits, and synchronously generating attribution interpretation; and finally, an operable developmental intervention suggestion is output through a man-machine cooperation mechanism, and a privacy protection and algorithm fairness review mechanism is embedded. According to the method, organic unification of deep data fusion, transparent and credible decision and educational value regression is realized.

The invention relates to the technical field of block chain technology and education data management, and discloses an education data management system and method based on a block chain, and the method comprises the steps: constructing an education business field specific language, and training an education business intention understanding model; applying a constraint solving algorithm to solve the smart contract parameters meeting various constraint conditions, and generating a smart contract code meeting multi-objective optimization; performing multi-level formalized verification on the generated intelligent contract code; constructing an educational policy and regulation knowledge base and calculating a contract policy compliance score; implementing a hierarchical transition strategy; the system is constructed based on a domestic Feiteng processor and a Kylin operating system, and stable operation and data security in an extreme environment are ensured; the technical problems of difficulty in intelligent contract development, difficulty in policy compliance evaluation and complexity in contract evolution upgrading in education data management are solved, and reliable storage, safe sharing and effective management of education data are realized.

The invention discloses a student score prediction method and system based on deep learning, and relates to the technical field of education, and the system comprises a data collection module, a data preprocessing module, a model training and optimization module, a prediction and analysis module, a user interface (UI) module, and a feedback and iteration improvement module. And a plurality of deep learning models are used to learn a complex relationship between features and scores, and accurate prediction of future academic scores of students is realized. According to the student score prediction method and system based on deep learning, a set of student score prediction system which is high in precision, explainable and dynamically evolved is constructed by fusing multi-source education data and a deep learning technology, so that the problems of low prediction precision and insufficient data utilization of a traditional method are solved; in addition, timeliness and accuracy of academic prediction are effectively improved, personalized education and educational resource optimization configuration are assisted, and data driving and scientization of educational decision are promoted.

The present application relates to the technical field of data processing, and more particularly to an educational data processing method based on coexistence of standard reference and norm reference, comprising: obtaining real-time access times, accuracy and interaction frequency; determining a first temporary target; determining a second temporary target; determining a processing target; determining a target evaluation index and an analysis evaluation index; and determining an evaluation grade after adjusting the reference threshold according to the index deviation. The present application can accurately capture the performance of students in different learning scenarios, real-time evaluate their learning state and dynamically adjust by fusing the dual evaluation mechanism of standard reference and norm reference. Through the gradual adjustment of the standard reference threshold and the deep application of the clustering analysis model, the limitations of single evaluation method can be eliminated, the evaluation result is more comprehensive, accurate and personalized, and the problem of low recognition accuracy of the processing device caused by poor data objectivity due to single evaluation standard is effectively solved.

The invention relates to the technical field of big data information processing, in particular to an application method and system based on education experience data, and the method comprises the following steps: data resource carding, education experience data model core library table design, data connection, data aggregation, data governance, data modeling and service development. The method has the beneficial effects that the information data of each commissioning bureau in the government is fully utilized, the'information isolated island 'of the data among the commissioning bureau of the government is broken, and the sharing and fusion of the data are realized; comprehensive and systematic sorting of education experience data is realized, a set of data model and data service is provided, and data support is provided for application of education data; the method is high in practicability, wide in application range and easy to popularize, widens thinking for internal personnel of the government to deeply apply education data, and promotes development of government affair big data environment and industry.

The utility model discloses an educational data display device, which relates to the technical field of data display and comprises a base, an adjusting component is fixedly mounted on the upper surface of the base, a movable disc is fixedly mounted on the upper surface of the adjusting component, a data display component is fixedly mounted on the upper surface of the movable disc, and the adjusting component comprises a fixed cylinder. The lower surface of the fixed cylinder and the upper surface of the base are fixedly installed, and a movable column is fixedly installed on the lower surface of the movable disc. Data are placed in a layered mode through the showing stands, the showing plates and the fixing clamps, layered showing of educational data is facilitated, the showing stands are driven to rotate through the motor and the rotating shaft, multi-directional rotating showing is achieved while the data showing area is increased, the phenomenon of waste of used space is avoided, the LED lamp strip is used for illumination, and the display effect is good. The device can illuminate the display data under the condition that the external environment is dark, so that the educational data can be clearly displayed, and the display effect of the educational data is improved.

An educational information system and method includes a data reading unit having a housing with a top surface; and a plurality of wireless data reading units in the housing, wherein the data reading unit reads educational data, via the wireless data reading units, from at least one card containing the educational data and transmits the educational data to a computer screen for display, each card of the at least one card comprises an icon on a top card surface, the icon representative of the educational data stored on the associated card of the at least one card, and the at least one card is opaque.

The invention relates to an educational data management system and a data management method, and the method comprises the steps: obtaining educational data from different educational related platforms, the educational data comprising geographical location information, classifying the educational data based on business attributes, and determining an involved object and an involved range of the educational data; grading the education data according to the data category, the related object and the related range to obtain core data, important data and general data; important data are encrypted and transmitted through an SM4 encryption algorithm and two-way transmission verification, a geographic label code is generated according to geographic position information of core data, the geographic label code is embedded into a secret key of the SM4 encryption algorithm, and the core data are encrypted and transmitted based on the secret key and the two-way transmission verification. According to the method and the device, the problem of safety risk in educational data transmission is solved, and the safety of educational data transmission is improved by performing encryption transmission on data of different grades in different modes through data grading.

The invention discloses a collaborative education platform based on a modern information technology. The collaborative education platform comprises a data layer for collecting student-related education data; the model layer is used for constructing an appreciation evaluation model and carrying out hierarchical progress evaluation and cooperative group identification, the appreciation evaluation model calculates a teacher appreciation contribution index based on student baseline scores and progress rates, the hierarchical progress evaluation is grouped according to student bases, the progress rates in the groups are calculated respectively, and the cooperative group identification is carried out; the collaborative group recognition module is used for detecting team formation behaviors of students through an interactive teaching module, automatically creating temporary collaborative portraits and calculating semantic similarity of resources and group targets based on BERT codes; the application layer is used for carrying out teaching value calculation, collaborative resource recommendation and collaborative achievement management by adopting a micro-service architecture and an API (Application Program Interface) gateway; the interactive service layer is used for providing a teacher-side teaching value visual instrument panel and a cooperative guiding capability index monitoring interface; and the decision support layer is used for performing assessment value calculation and decision optimization based on a big data analysis platform and a machine learning model technology.

A computing system includes a medical application for patient follow-up and education after a healthcare visit. The computing system includes a database with educational materials relating to diagnosis, medications, procedures, etc. A computing system obtains a selection from a healthcare provider, using a provider application or web portal, of the educational materials relating to a patient diagnosis or treatment plan. The computing system provides access to the selected educational materials to the patient using a patient application or web portal. The computing system generates a comprehension test relating to the educational materials and a symptom questionnaire relating to the patient diagnosis or treatment plan. The computing device may also generate a compliance questionnaire relating to patient compliance with the treatment plan. The computing device receives a patient response to the comprehension test, the symptom questionnaire and the compliance questionnaire and provides the patient response to the healthcare provider.

The invention relates to the technical field of big data management and retrieval enhancement generation, and particularly discloses a college data management large model platform based on a vector database and retrieval enhancement, comprising a multi-modal vectorization engine integrating a multi-modal large language model to realize deep semantic coding and representation learning of education data; the intelligent vector retrieval system realizes high-precision rapid retrieval based on neural information retrieval and learnable indexes; the advanced RAG inference engine improves the generation quality through an adaptive retrieval strategy, a multi-hop inference mechanism and knowledge fusion; the intelligent educational knowledge engine provides domain knowledge support and intelligent analysis based on dynamic knowledge graph and educational ontology modeling. By integrating the latest artificial intelligence technology, an advanced college data management platform is constructed, powerful technical support is provided for education informatization, and important theoretical significance and practical value are achieved.

The present invention discloses a method for cross-domain query of educational system data based on a knowledge graph. S1. Construct a multi-level ontology in the education field; S2. Record the association relationships between partitions through a semantic connection strategy; S3. Use the semantic connection strategy to synchronously update the association information between partitions; S4. The structured query statement includes the target partition information to be retrieved and the query semantics; S5. Generate a cross-partition query path; S6. Based on a distributed computing framework, perform parallel query operations on the located knowledge graph partitions, and integrate the query results of each partition according to the cross-partition query path to form a preliminary query result set; S7. Use the semantic reasoning ability of the multi-level ontology to optimize the semantics of the preliminary query result set, and integrate the optimized query results into the final output result. The present invention significantly improves the unity of educational data and the query association efficiency.

An exam question generating method, based on artificial intelligence (AI), includes receiving at least one reference material input by a user; receiving at least one exam parameter input by the user; and generating a plurality of exam question rules through an AI model, and generating at least one exam question that complies with the plurality of exam question rules based on the at least one reference material and the at least one exam parameter; wherein the AI model has undergone a pre-training process, and the pre-training process includes setting at least one educational material as a training dataset; and analyzing the training dataset to induce the plurality of exam question rules.

The invention provides a student data desensitization method, device and system and a storage medium, and belongs to the technical field of data desensitization, and the method comprises the steps: importing an original student information image and a student name list; performing correction analysis on the original student information image to obtain a corrected student information image; segmenting the corrected student information image to obtain a text feature image and a table structure image; performing coordinate analysis on the character feature image according to the student name list to obtain a target coordinate vector set; according to the method, the desensitization efficiency and accuracy are greatly improved, digital service infrastructures of different scenes can be compatible, leakage of irrelevant sensitive information is avoided, the problems of low efficiency, high error rate, privacy leakage risk and the like in traditional manual processing are effectively solved, and reliable technical guarantee is provided for safe sharing of education data.

A cloud-based special education management platform and centralized educational data hub are disclosed. The platform comprises a processor, memory, and user interface for accessing a database of a child's complete educational records and data. The user interface presents information, appointments, reminders, and a calendar for managing the child's individualized education programs (IEPs) and educational requirements. The platform provides access to relevant laws, regulations, and resources, including professional contacts, facilities, and programs. The user interface may include a dashboard displaying an overview of the child's current plans, a forum for discussions, a document management system, a task management system, and a team management feature. The centralized hub aggregates data from disparate sources and provides unified access to key information, events, and resources. The invention streamlines the process for managing a special needs child's education.

We provide the system. [Solution] A means for receiving and integrating learning performance information, aptitude test information, and survey data, A means for analyzing an individual's interests, abilities, and personality traits based on the aforementioned integrated information, A means of providing personalized career information based on the aforementioned analysis results, A means of proposing appropriate career paths and learning plans within the region based on citizens' educational data, A system that includes this.

The object of the present invention is to provide a knowledge graph for teaching and learning assistance and its construction method, which relates to the technical field of educational data processing. The method includes the steps of: constructing a teaching and learning assistance evaluation matrix according to the learning behavior data, teaching and learning assistance resource data, and learning stage data of users; decomposing the teaching and learning assistance evaluation matrix into an educational knowledge mastery degree matrix and an educational knowledge correlation degree matrix through a non-negative matrix factorization algorithm; and constructing a knowledge graph according to the teaching and learning assistance evaluation matrix, the educational knowledge mastery degree matrix, and the educational knowledge correlation degree matrix. The construction method of the knowledge graph in this application integrates the relationships among educational knowledge nodes, learning stage nodes, and teaching and learning assistance resource nodes, and can, by analyzing the knowledge mastery and resource usage situations, as well as identifying the influence degrees of different teaching and learning assistance resources on educational knowledge points at different learning stages of users, help to efficiently screen teaching and learning assistance resources, help users discover weak links in the learning process, and optimize learning plans and learning methods.

This invention discloses an intelligent decision support system for higher education teaching based on AI and multi-source data fusion, relating to the fields of artificial intelligence and educational data processing technology. The system includes a multimodal teaching event aggregation engine, comprising: a temporal alignment and atomic feature extraction module, a candidate event frame generation module, a cross-modal semantic consistency verification module, and an event solidification module. Through this engine, discrete data points can be transformed into structured, contextualized teaching events suitable for in-depth analysis, providing a reliable data foundation for intelligent decision-making in higher education teaching.

We provide the system. [Solution] A terminal device for inputting educational data, A server means for receiving the educational data, A generation model means that operates on the server means and automatically generates lesson plans, A means for transmitting the lesson plan generated by the generation model means to a terminal means, An analytical method for analyzing student data and creating student personality profiles, A proposal means that suggests an educational policy optimized for the student based on the personality profile, A system that includes this.

We provide the system. [Solution] Means for collecting educational information and evaluating it based on reliability and accuracy, A means for receiving user learning requests and generating an optimal learning plan based on them, A means of calculating rewards based on the frequency and conditions of use of educational data selected from a large amount of information, A means for presenting an optimal educational plan in response to user input in an information processing device and adjusting the plan via an artificial intelligence agent, A system that includes this.

The application relates to the technical field of educational data processing, and particularly discloses a wrong question mining and accurate printing management system for learning situation big data. The system comprises a learning situation data acquisition module, a wrong question value evaluation module, a student state perception module, a dynamic priority scheduling module and an intelligent printing execution module. The wrong question value evaluation module performs multi-dimensional quantitative analysis on wrong questions, the student state perception module performs real-time analysis on student learning states, the dynamic priority scheduling module calculates the dynamic comprehensive priority of a task based on a composite model and reconstructs a printing queue, and the intelligent printing execution module sequentially executes printing. The application quantifies the value of wrong questions and student states, realizes dynamic intelligent scheduling of a printing task, ensures that high-value wrong questions are preferentially printed in a high-concentration learning window period, effectively reduces teaching feedback delay, and improves the accuracy of resource investment and teaching effect.