44 results about "Educational data" patented technology

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.

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 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.

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 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.

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.

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.

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.

The invention discloses a blood relationship tracking and operation log analysis method and system for educational data, relates to the field of educational data analysis, and realizes whole-process closed-loop management of educational data safety management by constructing a double mapping model. The method comprises the following steps: firstly, based on an SQL structure optimization model trained by unauthorized historical data, the query performance can be remarkably improved on the premise of ensuring semantic accuracy; secondly, establishing a risk identification mechanism between a database permission level and an SQL operation structure through an unauthorized access detection model constructed by permission related data; a dynamic weight decision of immediate responsiveness and security is introduced, and system efficiency and data security are intelligently balanced when an unauthorized risk is detected; performance optimization, authority control, risk assessment and blood relationship tracking are organically fused, the problem that safety and performance are difficult to consider in traditional data management is solved, and reliable guarantee is provided for safe and efficient operation of an educational data system.

The application discloses a multi-source heterogeneous education data driven student intelligent evaluation method, and relates to the technical field of artificial intelligence. The method aims to solve the problems of data semantic fragmentation, uninterpretable model and application deviation from the essence of education in the prior art. The method constructs a three-layer architecture: first, based on the educational ontology, the multi-source heterogeneous data is subjected to semantic unified modeling to form a student knowledge graph; second, an interpretable multi-modal model integrating educational prior knowledge is designed to realize academic risk early warning and ability portrait, and to synchronously generate attribution explanation; finally, through a man-machine collaborative mechanism, an operable developmental intervention suggestion is output, and a privacy protection and algorithm fairness review mechanism is embedded. The application realizes the organic unification of data deep fusion, decision transparency and credibility, and education value regression.

A system is provided.SOLUTION: The system includes a means for analyzing uploaded educational data and automatically generating test questions using generative artificial intelligence, a means for a user to review and edit the generated test questions through a terminal, and a means for inputting and uploading student performance data.SELECTED DRAWING: Figure 1

An object of the system according to the embodiment is to eliminate a shortage of teachers and a disparity in education and to provide high-quality education equally to children.SOLUTION: A system according to an embodiment includes an educational site support AI, an educational data analysis AI, and a generation AI. The teaching site assistance AI supports the teacher's work. The educational information analyzing AI manages and analyzes the child's educational information. The generation AI generates a teaching material.SELECTED DRAWING: Figure 1

We provide the system. [Solution] A device for collecting learner education data, A device that analyzes collected educational data and identifies areas of weakness, A device that generates an individualized educational plan based on identified areas of weakness, A device for monitoring the progress of the generated educational plan, A device that provides educational support using robots, A system that includes this.

The application provides a multi-scale semantic feature fusion problem chain extraction method and related equipment, belonging to the technical field of educational data analysis. The method comprises: dividing dialogue text into multiple subtexts according to semantics; obtaining global semantic features of each sentence in the subtext by using a long text large model; extracting multi-scale local semantic features from the global semantic features by using a residual multi-scale gating module of a multi-scale classroom teacher-student dialogue problem chain text division model; fusing all multi-scale local semantic features to obtain fusion features; and dividing problem chains in the subtext according to the fusion features by using a discourse tile superposition perception classification head of the multi-scale classroom teacher-student dialogue problem chain text division model. The application realizes high-precision division of classroom dialogue text and accurate construction of classroom problem chains by using a multi-scale classroom teacher-student dialogue problem chain text division model to extract and fuse multi-scale features of sentences.

We provide the system. [Solution] Means for collecting and accumulating educational data, A means of analyzing collected data and automatically generating educational plans, A means for optimizing educational materials and assessment tests based on the generated educational plan, A means of displaying an interface for managing educational progress and providing individualized instruction, A means of updating educational data in real time, A means for controlling an automated device equipped with an educational support device that includes means for providing customized educational materials based on the progress of the students, A system that includes this.

The invention discloses an educational AI assistant construction method based on a semantic similarity model, which comprises the following steps: collecting and preprocessing multi-source educational question and answer data, and training the semantic similarity model by using an LCQMC data set; finely adjusting the model by using education data so as to improve the semantic matching precision of the model in the education field; combining the fine-tuned semantic similarity model with a KMP algorithm and a QWEN large model to construct an educational AI assistant; and finally, accurate and professional answering of education questions is realized through an education AI assistant. According to the method, through a multi-stage training and retrieval enhancement mechanism, the question and answer accuracy and specialty of an educational AI assistant in the educational field are effectively improved.

The invention discloses an intelligent education cloud data management method and system based on modular design, and the method comprises the following steps: S1, building a standardized metadata model based on the multi-source heterogeneity of education data, and dynamically adapting to an acquisition module; s2, deploying independent functional unit modules on the cloud platform, constructing a reconfigurable assembly line through a standardized interface between the modules, automatically matching a processing path according to a data type, and outputting an incremental data set with a uniform format; s3, based on the access permission tree associated with the role, creating a logic storage partition bound with the teaching role; s4, the analysis demand is disassembled into micro-service computing nodes, and a multi-dimensional education analysis result is generated; s5, deploying an independent security monitoring module, and generating a periodic compliance audit report; according to the method, a module cascade feedback and security audit double-circulation mechanism is constructed, the problem of system module splitting is solved, a whole is formed under an interface framework, and the privacy protection strength, the scheduling efficiency and the behavior tracing reliability are improved.

A system is provided.SOLUTION: A system comprising: means for storing registration information received from a user in a database; means for receiving an educational material request from a user; means for generating an educational material based on the user's request using artificial intelligence; means for storing the generated educational material in the database and notifying the user; means for delivering the educational material when the user makes a download request; and means for collecting user feedback and storing it in the database.SELECTED DRAWING: Figure 1

Provide a system. 【Solution means】 Means for collecting the user's financial data and safely storing it in a data storage device, Means for analyzing the collected financial data and performing a comparative analysis of different financial products, Means for generating a notification of asset management activities based on the user's schedule information, Means for providing customized educational materials based on the user's learning level, Means for displaying a future asset plan in 3D visualization based on the user's investment objectives, Means for anonymizing the collected data and ensuring information security, Means for presenting real-time investment analysis results and assisting in optimizing transactions, Means for enabling the user to virtually experience a lifestyle using the visualized asset plan, A system including the above.

The application relates to the technical field of artificial intelligence and discloses an education data report content interaction method and system based on retrieval enhancement generation, which comprises the following steps: using a large language model to determine whether a natural language question is an education field question, and if so, performing semantic analysis to generate a structured query instruction. When the question involves cross-document correlation analysis, a structured semantic index library is retrieved to generate a retrieval result set; if policy correlation analysis is involved, a policy knowledge graph is matched by combining semantic similarity calculation and entity linking technology, and cross-modal fusion processing is performed to obtain the retrieval result set; and the retrieval result set is input into a retrieval enhancement generation model, and an education field language model is called to generate an analysis report. The application can solve the problems of inaccurate intent recognition, low cross-document analysis efficiency and the inability to dynamically combine the latest policies in the traditional interaction mode, significantly improve the efficiency and quality of education field data report interaction, and optimize the user interaction experience.

The system according to this embodiment aims to analyze a child's educational data and provide the child with the most suitable educational materials. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects children's educational data. The analysis unit analyzes the data collected by the collection unit. The generation unit generates educational materials based on the analysis results obtained by the analysis unit. The provision unit provides the educational materials generated by the generation unit.

Provide a system. 【Solution means】 Means for aggregating and integrating educational data from educational institutions, Means for analyzing the learning characteristics of each learner based on the said data, Means for generating an optimal educational plan by utilizing a generative AI model, Means for distributing learning support to an information processing device based on the said plan, Means for tracking the progress of learning and collecting feedback, Means for updating and optimizing the learning plan based on the said feedback, Means for creating and providing a progress visualization tool for educators, Means for providing interactive educational support through an information processing device that recognizes the voices and actions of educational learners and provides real-time feedback in response to questions, A system including the above.