52 results about "Educational assessment" patented technology

The invention provides a full-cycle intelligent education evaluation system based on a knowledge graph, and relates to the technical field of data processing, and the system is used for carrying out the correlation analysis of a learning behavior data set and a knowledge point data set of a student, calculating the mastering degree of the student on each knowledge point, recognizing the relation between the student and each knowledge point, and carrying out the calculation of the mastering degree. The method comprises the following steps: acquiring knowledge points of students, mapping the knowledge points into a graph structure to obtain a staged knowledge graph, updating the staged knowledge graph, adjusting the relationship between the students and each knowledge point to obtain a dynamic knowledge graph, extracting the real-time mastery degree of each knowledge point according to the dynamic knowledge graph, and periodically analyzing the knowledge points in combination with time dimensions. And obtaining periodic evaluation data, analyzing the mastering degree, the progress trend and the weak knowledge points of the students at each knowledge point, and generating an individual evaluation data set. According to the invention, the knowledge graph can be constructed to periodically evaluate the learning condition of the student.

The invention, which relates to the technical field of artificial intelligence, discloses an artificial intelligence-based education evaluation and feedback system comprising a data acquisition module, a vector generation module, a prediction module, an error region positioning module and a feedback module. The system constructs a unified high-dimensional cognitive state vector by collecting answering behaviors, eye movement tracks, facial micro-expressions, voices and intonations and electroencephalogram signals of students; generating a learning evolution path map based on a dynamic Bayesian network and a causal reasoning mechanism, and predicting future learning bottleneck nodes; an error region is recognized through semantic deconstruction and graph matching, and context-associated personalized feedback content is generated in combination with a generative language model; according to the system, an evaluation feedback closed loop of cognitive state modeling, accurate identification of an erroneous region and intelligent feedback pushing is realized, and the accuracy of education evaluation and the effectiveness of intervention are improved.

The invention discloses a homework semantic evaluation method based on a multi-modal large model, and relates to the technical field of education intelligent evaluation, and the method comprises the steps: 1, normalizing multi-modal answers into a step sequence, a scoring key point map, an acceptable answer set and an initial scoring anchor point, and building a step and key point bidirectional index; 2, performing consultation according to teacher roles, generating candidate scores and key credits, and calculating a key point coverage degree, a consistency degree and a contradictory proportion; and step 3, reading the conditional reliability ledger, generating a comprehensive weight, aggregating the comprehensive weight to obtain a final score, and outputting an uncertainty index at the same time. 4, giving out hierarchical personalized explanation, determining a minimum correctable step, executing step-level echoing, and generating a training list; 5, carrying out anchor question online calibration and fusion parameter updating, and setting a trigger proportion upper limit and an early stop threshold according to a budget perception route; the link realizes unified measuring scale, steady decision, traceable learning and controllable cost.

The present invention discloses an educational evaluation method for dynamic vocational ability and job matching based on a knowledge graph. S1. Construct a vocational ability knowledge graph; S2. Construct a job requirement knowledge graph; S3. Design and train a double-layer generative adversarial network model; S4. Match the vocational ability enhancement plan and job requirement changes generated by the double-layer generative adversarial network model with the existing vocational ability knowledge graph and job requirement knowledge graph; S5. Based on the dynamic changes in market demand and the development of individual vocational abilities, update the vocational ability knowledge graph and job requirement knowledge graph in real time, and use the double-layer generative adversarial network model to generate new matching relationships to continuously optimize the matching results; S6. Generate an educational evaluation report and career planning suggestions according to the optimized matching results. The present invention significantly improves the dynamic adaptability and matching accuracy of vocational ability and job matching, and realizes the precise docking between job seekers and job requirements.

The invention relates to the technical field of physical education management, and particularly discloses a digital informatization physical education comprehensive management system, which comprises a user interaction module, a data acquisition module, a data processing module, a system optimization module and a distributed database, the data acquisition module acquires teaching content, motion completion degree and user physiological data through a camera, a motion capture sensor and wearable equipment; the data processing module cleans and converts the data, and quantitatively evaluates whether the learning results of the students are qualified or not by using a constructed education evaluation coefficient mathematical model; the system optimization module calculates a scene optimization coefficient by analyzing a user favorable comment trend and multimedia loading performance, and automatically identifies a teaching scene needing to be optimized; the distributed database adopts a master-slave replication and fragmentation technology to improve data access efficiency and fault tolerance, digital management of physical education is achieved, and therefore the quality and efficiency of physical education are improved.

The invention discloses a cognitive function digital evaluation system and method based on dynamic visual tracking, four attention function tests are integrated in one system for the first time, the attention function of an individual can be comprehensively evaluated, and a more comprehensive and more systematic cognitive function evaluation report is provided; compared with a traditional single-function test, the cognitive condition of an individual can be known more comprehensively, and a more valuable reference is provided for the fields of educational evaluation, clinical diagnosis, human resources and the like; for example, in education evaluation, a teacher can comprehensively understand attention characteristics of students through the system and make a more targeted teaching plan; in clinical diagnosis, a doctor can comprehensively evaluate the cognitive function of a patient to assist diagnosis and treatment.

The present invention discloses a cross-time point cognitive diagnosis method that considers student influencing factors, belonging to the field of educational assessment technology, comprising: obtaining student answer data at several time points, fitting the student answer data at each time point respectively; assigning each student to a potential knowledge mastery state based on the fitting results, and calculating the classification error probability; constructing a potential transfer cognitive diagnosis model, optimizing the potential transfer cognitive diagnosis model based on the classification error probability, obtaining an estimated initial state probability and an estimated transfer probability based on the optimized potential transfer cognitive diagnosis model; and performing cross-time point cognitive diagnosis on students based on the estimated initial state probability and the estimated transfer probability. The present invention can effectively analyze the impact of covariates on students' initial state, as well as the changing pattern of potential knowledge mastery state at different time points.

The invention relates to the technical field of education evaluation, in particular to an English reading understanding ability analysis system based on deep learning, which comprises the following steps: an answer text analysis module, a reading mode analysis module, an attention ability evaluation module, an efficiency score calculation module, an understanding obstacle recognition module and a reading ability evaluation module. According to the method, the keyword in the answer text of the student is extracted and compared with the preset answer, so that the understanding degree of the student on the English reading understanding text is effectively identified, and the reading behavior of the student is monitored in real time, so that the attention of the student can be identified in a concentrated and dispersed time period; the attention level and the reading efficiency of the student are effectively evaluated, and in combination with the detection of reading disorder and the analysis of the ability level of the student, an educator is helped to quickly and accurately grasp the learning condition of the student and adjust the teaching strategy and content, so that the individualized requirements of the student are met, and the learning efficiency and the education quality are effectively improved.

The invention provides a higher education textbook quality evaluation method and system based on a particle swarm big language model, and relates to the technical field of education evaluation. The method comprises the steps of collecting text data of higher education textbooks; preprocessing the text data by using a particle swarm optimization algorithm to obtain preprocessed data; constructing a large language model based on a particle swarm, and performing feature extraction on the preprocessed data through the large language model to obtain feature data; the feature data comprises semantic features, structural features and quality features; performing comprehensive evaluation on the feature data through the evaluation function to obtain a comprehensive evaluation score; and comparing the comprehensive evaluation score with a preset standard score to determine the quality grade of the textbook. By comprehensively evaluating the textbook features, flexibly optimizing feature selection and scientifically setting the quality standard, the accuracy and practicability of textbook quality evaluation are remarkably improved.

The invention discloses an education evaluation method and system based on visual identification data analysis, and relates to the technical field of education analysis, and the method comprises the steps: presetting the understanding difficulty for each mapping node, and taking the understanding difficulty as a teaching path reference; performing semantic analysis on the explanation scheme, extracting knowledge points and secondary and supplementary key phrases, and associating the knowledge points and the secondary and supplementary key phrases to corresponding mapping nodes to realize content structuring; acquiring real-time classroom voice information, extracting and analyzing characters to determine real-time keywords, positioning corresponding nodes in the mind map, sequentially connecting the nodes to form a real-time map node sequence line, and capturing an actual explanation path; calculating student initial state evaluation, combining node difficulty correction to obtain final evaluation, and accumulating all student evaluation as a teaching effect; taking a preset explanation order line teaching effect corresponding to the real-time order line as a standard, determining the relative positioning of actual evaluation, and achieving deviation quantification; according to the invention, through multi-modal fusion and a dynamic order line mechanism, the real-time performance and accuracy of evaluation are improved, and teaching improvement guidance is provided.

The invention relates to the technical field of educational informatization and block chain technology fusion, and particularly discloses a digital archive management system based on a multi-modal knowledge graph and a block chain, which comprises four core layers: a self-adaptive block chain evidence storage layer ensures credible sharing of cross-mechanism data; the multi-modal knowledge graph intelligent construction layer realizes unified understanding and correlation analysis of multi-modal data; an evaluation engine driven by deep learning completes intelligent evaluation and personalized recommendation; and the intelligent education evaluation support layer provides decision support and risk early warning. According to the method, Transform, a graph neural network, a time sequence graph neural network and the like are fused, the problems that in the prior art, achievement verification is difficult, multi-modal processing is weak, graph construction is immature, evaluation is rigid and the like are solved, data credibility, system adaptability, decision making scientificity and user experience are remarkably improved, and advanced technical support is provided for educational informatization.

This invention relates to the field of computer technology and discloses a method for evaluating the effectiveness of vocational education based on learning behavior data mining. The method includes: collecting learners' full-domain learning behavior data sequences; generating cognitive style feature vectors through a self-attention sequence encoder; dynamically weighting the behavioral features using style gating units to form a style-weighted effective behavioral feature matrix; combining a pre-constructed vocational skills knowledge graph and aggregating the behavioral features layer by layer through a graph neural network to calculate the mastery score of each skill node; and finally generating an evaluation report containing multi-dimensional evaluation results and personalized suggestions. This invention achieves accurate, objective, and personalized skills assessment for learners with different cognitive styles, improving the fairness and diagnostic value of vocational education evaluation.

We provide the system. [Solution] Equipped with an information processing device for automating educational evaluation, A means for selecting multiple educational issues based on one or more conditions and automatically generating an educational assessment test, A means of performing automatic scoring by comparing the received answer data with the model answer, A system including means for generating and presenting scoring results and feedback.

The application discloses a likelihood-based cognitive diagnosis Q matrix estimation method, belongs to the field of cognitive diagnosis evaluation, and comprises the following steps: obtaining response data of students to questions and a partially defined Q matrix, fitting a G-DINA model to obtain a posterior distribution of a student attribute vector, and calculating a marginal likelihood based on the posterior distribution. Furthermore, the fitting degrees of different q vectors are evaluated through AIC or BIC values, the optimal q vector is selected to gradually fill unknown parts of the Q matrix, and the process is continued until convergence. The method reduces the dependence on experts, improves the accuracy and efficiency of Q matrix estimation, and is suitable for large-scale education evaluation scenarios.

The invention provides an examination proposition person behavior model construction method and system based on deep learning, and relates to the field of artificial intelligence and educational evaluation, and the method comprises the steps: obtaining proposition records, constructing a knowledge application graph, analyzing proposition preferences based on a time window, training a proposition behavior recognition network, and optimizing a migration path and difficulty distribution. And anomaly detection of proposition behaviors is realized. According to the method, the knowledge preference and the behavior mode of the proposition person can be accurately described, the proposition quality monitoring efficiency is improved, the proposition risk is reduced, and the examination fairness is guaranteed.

The invention relates to the technical field of artificial intelligence and education, in particular to an education evaluation and feedback system based on artificial intelligence. Comprising a multi-modal data acquisition module, a data preprocessing and fusion module, a dynamic knowledge graph construction module, a multi-dimensional evaluation analysis module, a personalized feedback generation module, a teaching intervention push module and a system iteration optimization module, all the modules are linked in sequence, and the system iteration optimization module and the data preprocessing and fusion module form a closed loop. According to the method, the traditional score only theory is broken through, a knowledge-ability-thinking-emotion four-dimensional evaluation system is constructed, three-dimensional cognition of students is realized in combination with multi-modal data, the evaluation result better meets the essential requirements of education, the learning process of the students is tracked in real time through the dynamic knowledge graph and the improved Bayesian network model, and the learning efficiency of the students is improved. The evaluation result is adjusted according to the knowledge mastering condition and ability growth, and the defect of staticization of traditional evaluation is overcome.

This invention relates to the field of artificial intelligence-based educational assessment, and discloses a cognitive diagnostic model and method based on knowledge text. The model includes a text feature extraction module for extracting and generating knowledge point association vectors representing the degree of association between questions and preset knowledge points; a knowledge proficiency embedding module for mapping knowledge proficiency vectors; a question attribute embedding module for mapping question knowledge difficulty vectors and question discrimination scalars; a knowledge association attention module for adjusting attention weights and generating weighted knowledge representations; and an adaptive nonlinear prediction module containing a multi-layer KANLinear structure composed of B-spline basis functions and outputting the prediction probability of correct student answers. This invention solves the problem of fragmented internal module functions in existing models and features high interpretability and independence from manual annotation.

The invention discloses a labor education practice self-adaptive evaluation system and device based on multi-source perception, and relates to the technical field of intelligent management, and the system comprises a perception terminal module which is used for collecting student labor data and environment change data in real time; the data processing module is used for carrying out deep integration analysis on the multi-source sensing data and establishing a labor behavior portrait of the student; the self-adaptive evaluation module is used for carrying out self-adaptive labor evaluation according to the labor behavior portrait of each student; and the feedback interaction module is used for performing periodic assessment summarization and multi-party information interaction according to the personalized labor assessment report. The technical problems that existing labor education evaluation depends on subjective judgment, interference factors are many, real-time feedback cannot be achieved, and the reliability of the evaluation result is poor are solved, comprehensive and real-time student labor performance evaluation and personalized feedback are achieved through a self-adaptive evaluation mechanism relying on multi-source perception and robustness artificial intelligence, and the evaluation efficiency is improved. And the reliability of the evaluation result is improved.

This invention provides a method and system for constructing a deep learning-based model of exam question setter behavior, relating to the fields of artificial intelligence and educational assessment. The method includes acquiring question-setting records to construct a knowledge application graph, analyzing question-setting preferences based on time windows, training a question-setting behavior recognition network to optimize transfer paths and difficulty distribution, and achieving anomaly detection in question-setting behavior. This invention can accurately characterize the knowledge preferences and behavioral patterns of question setters, improve the efficiency of question-setting quality monitoring, reduce question-setting risks, and ensure exam fairness.

The invention discloses a multivariate intelligent evaluation and career planning consultation system based on multi-modal data fusion, and belongs to the crossing field of artificial intelligence and education evaluation. The system comprises a multi-modal data acquisition module, a preprocessing module, a multivariate intelligent evaluation module, a cross combination analysis module, a dynamic updating module and a personalized recommendation module. The acquisition module integrates structured data (questionnaires, academic data and genes) and unstructured data (behaviors, voices and images); the preprocessing module cleans, converts and fuses data; the evaluation module constructs an'intelligence-capability 'secondary model, and quantifies 8 items of intelligence and 40 items of core capability; the cross analysis module generates a potential label; the dynamic updating module iterates the result in real time; and the recommendation module outputs interest, academic, professional and career planning suggestions. The system solves the problems of single traditional evaluation data, rough evaluation, poor dynamics and the like, and improves the planning precision and timeliness.

The application belongs to the technical field of education evaluation, and discloses a cognitive analysis system focusing on ability change and question characteristics, which comprises an interactive coding network module, an ability acquisition module, an ability prediction module, an ability rationality module, a question prediction module, a knowledge point prediction module and a multi-task fusion module. The ability prediction module, the question prediction module and the knowledge point prediction module are used to respectively predict the probability of answering the next exercise correctly. Finally, the multi-task fusion module balances the three types of prediction probabilities through a fusion training mode to obtain the final probability of answering the next exercise correctly, and the final probability is used for cognitive analysis of the subject. The application can track the ability change of the subject in real time, provide dynamic ability value evaluation, and better reflect the ability development in the learning process.

The invention discloses a large model generation text detection method based on multi-agent cooperation. The method comprises the following steps: 1) input text processing: carrying out differential disturbance processing on an input text through three heterogeneous agents; 2) semantic similarity evaluation: performing similarity scoring on an original text and a modified text generated by each agent by using a scoring method BARTScore based on a sequence-to-sequence pre-training model, and normalizing to an interval of [0, 1]; 3) threshold value judgment: determining the optimal threshold value of each agent through the Youden's J statistical magnitude; and 4) a voting decision mechanism: performing final classification judgment by adopting a majority voting mechanism according to judgment results of the three agents. The method does not need to depend on annotated data and field fine tuning, through multi-dimensional feature decoupling and a dynamic adaptive strategy, the detection accuracy and cross-field robustness in a zero sample scene are remarkably improved, and the method is suitable for practical scenes such as educational evaluation and content auditing.

This system provides an educational assessment method that efficiently evaluates children's intellectual abilities and developmental status based on their interaction history with AI, and then provides feedback on the results to parents and educators. [Solution] The system comprises a storage means for saving dialogue history, a summary generation means for generating a summary of the dialogue history after a predetermined period of time has elapsed, an analysis means for integrating and analyzing new dialogue history with the saved summaries, and an evaluation generation means for generating an educational evaluation based on the analysis results. The analysis means performs the analysis based on rubrics related to language patterns, knowledge, and interests. Rubrics can be set by parents or teachers. This makes it possible to efficiently conduct long-term learning evaluations and realize multifaceted educational evaluations.

The invention discloses an education evaluation method and system based on onsite behavior performance data analysis, and relates to the technical field of education evaluation models, students are randomly selected for teaching verification recording, teaching is carried out based on a preset teaching template, stage detection is carried out, a student behavior performance image sequence is shot in each stage period, and the student behavior performance data are analyzed. Associating stage detection results to form a behavior performance data set; secondly, analyzing behavioral performance characteristics in the data set, judging the correlation between the behavioral performance characteristics and a stage detection result, reserving behavioral performance characteristic sections which have significant differences and need to be concerned, and constructing a sequence of the behavioral performance characteristic sections which need to be concerned according to time nodes of the behavioral performance characteristics; performing accuracy analysis on the sequences, and removing the sequences which do not meet preset requirements; and constructing an education evaluation model by taking the reserved sequence and the detection result of the corresponding stage as a data source, and performing automatic evaluation on the real-time education condition by using the model. According to the invention, by objectively quantifying the on-site behaviors of the students, the real-time performance and accuracy of evaluation are improved, and personalized teaching optimization is supported.

The invention relates to the technical field of computers, and discloses a vocational education effect evaluation method based on learning behavior data mining. The method comprises the following steps: collecting a global learning behavior data sequence of a learner; generating a cognitive style feature vector through a self-attention sequence encoder; performing dynamic weighting on the behavior characteristics by utilizing a style gating unit to form an effective behavior characteristic matrix of style weighting; combining a pre-constructed vocational skill knowledge graph, aggregating behavior characteristics layer by layer through a graph neural network, and calculating a mastery degree score of each skill node; and finally, generating an evaluation report containing a multi-dimensional evaluation result and personalized suggestions. According to the method, accurate, objective and personalized skill assessment of learners with different cognitive styles is realized, and the fairness and diagnostic value of vocational education assessment are improved.

The invention relates to the technical field of education evaluation, in particular to an education quality evaluation method based on multi-source data fusion and statistical hypothesis testing. The method comprises the following steps: collecting multi-source learning data of students for each course; calculating an instant learning performance index and a classroom participation performance index of each student for each course based on the collected multi-source learning data; in combination with the instant learning performance index and the classroom participation performance index, determining an education quality measurement factor of each course; constructing a whole semester education quality measurement factor sequence according to the class time sequence, analyzing the dynamic change trend of the education quality measurement factor sequence to obtain a dynamic change trend characteristic value, and correcting the dynamic change trend characteristic value in combination with the end-of-period score data to obtain a course education quality evaluation characteristic value; and according to the course education quality evaluation characteristic value, comprehensively evaluating the whole semester education quality of the course. According to the invention, the accuracy of education quality evaluation is improved.

This invention discloses a smart desk integrating STEM assessment and teaching for preschoolers and its control and management method. The desk includes a sub-router and a switch housed within a wiring enclosure. A high-definition camera and a sound acquisition sensor are connected to the switch. The switch is connected to the sub-router, which in turn connects to an edge computing device and a main router. The main router is connected to an edge server. Through distributed dynamic networking algorithms, dynamic computing power scheduling algorithms, multimodal data fusion educational assessment algorithms, and server-only proximity collaboration algorithms, the smart desk achieves flexible networking, dynamic allocation of computing power, scientific teaching assessment, and server collaborative operation. This invention can seamlessly collect preschoolers' learning data, generate quantitative assessment reports, provide teachers with personalized teaching suggestions, and adapt to different teaching scenarios and class sizes, effectively improving the quality of preschool STEM teaching and resource utilization, and has broad application prospects.

The invention belongs to the technical field of artificial intelligence and education data processing, and provides a college education data fusion and intelligent evaluation method and system based on a large model, and the method comprises the steps: constructing a college education evaluation database, carrying out the structural management and fusion processing of multi-source data related to the college education evaluation, and carrying out the intelligent evaluation of the college education data. Evaluation task adaptation is carried out on the large model, so that the large model has semantic comprehension and processing capability oriented to college education evaluation; on the basis, intention analysis is performed on an education evaluation natural language problem input by a user, a structured evaluation request is generated, screening, fusion and processing of evaluation data are completed under data and authority constraint conditions, and a corresponding college education intelligent evaluation result is generated; and meanwhile, the evaluation result data or model is updated in combination with user feedback information. According to the invention, large-model intelligent evaluation and generation output based on college education data fusion constraints are realized, and the accuracy, consistency and interpretability of college education evaluation results are improved.

The invention discloses a mathematical core attainment evaluation method based on a multi-feature fuzzy rank and a vector, and belongs to the field of education evaluation and education data mining. The method comprises the following steps: constructing a high school mathematics core accomplishment index system which comprises a plurality of first-level indexes and second-level indexes; multiple examination data of the students are collected, the score rate of each student on each secondary index is calculated, multi-feature data are extracted, and the multi-feature data comprise the minimum value, the mean value and the maximum value of the score rates; performing normalization processing on the multi-feature data to obtain a normalized feature quantity; on the basis of the normalized feature quantity, calculating a fuzzy rank vector of each student under each feature quantity, the fuzzy rank vectors representing membership degrees of the students on different sorting results; and calculating a single-feature fuzzy rank sum vector and a multi-feature fuzzy rank sum vector. According to the method, by constructing the multi-feature data model and introducing the fuzzy rank vector sorting method, the scientificity and accuracy of high school mathematics core attainment evaluation are effectively improved.

DATA PROCESSING DEVICE FOR HIGHER EDUCATION ASSESSMENT