134 results about "Cognitive diagnosis" patented technology

The invention provides a learning data accurate mining method based on cognitive calculation driving. The learning data accurate mining method comprises the following steps of S1, performing multi-modal learning behavior data acquisition and heterogeneous integration; s2, a dynamic feature weight optimization step based on calculus; s3, performing cognitive state differential equation modeling; s4, a cognitive diagnosis hybrid model based on statistics; s5, incremental construction of the dynamic knowledge graph is carried out; s6, constructing a federated learning framework for privacy protection; s7, a cognitive intervention strategy is generated; s8, constructing a multi-granularity effect evaluation system; s9, a step of constructing an interpretability enhancement module; s10, a step of carrying out adaptive iterative optimization; the learning data accurate mining method based on cognitive calculation driving has the following advantages that the data utilization rate breaks through the limitation of a traditional method through federated learning and heterogeneous graph fusion; the attention prediction error is reduced through differential equation modeling, and the method is superior to all existing ARIMA / LSTM baseline models.

The invention discloses a multi-source heterogeneous learning path planning method based on personalized constraints, which solves the problems of single path, data staticization and the like in the prior art, and comprises the following steps: acquiring multi-source behavior data of a learner, carrying out feature modeling, and constructing a high-dimensional behavior portrait; according to the high-dimensional behavior portrait, utilizing a neural collaborative filtering algorithm to predict the interest and mastering probability of a learner to any knowledge point, and generating a preliminary learning path; carrying out dominant and implicit evaluation on the knowledge mastering state of the learner by adopting a cognitive diagnosis model, and carrying out dynamic correction on the preliminary learning path to obtain a corrected learning path; performing dimension reduction on the multi-source behavior data by adopting a principal component analysis algorithm to extract a core factor, and forming an evaluation basis for path optimization; and constructing a multi-objective path function model, and obtaining an optimal dynamic learning path by adopting a multi-objective optimization path algorithm in combination with the corrected learning path and the core factor. The method has high practical value and popularization value in the technical field of learning path planning.

The invention discloses a multi-group time sequence student cognitive diagnosis method based on a joint probability model, which belongs to the field of education and comprises the following steps: acquiring student answer data, group classification labels and covariable data of at least two continuous time points; according to the student answer data, the group classification label and the covariable data, a joint probability model is constructed, and the joint probability model comprises a structure model and a measurement model; the structure model is used for describing a dynamic change process of a potential knowledge mastering state of the student, and the measurement model is used for establishing an association relationship between answer data of the student at each time point and the potential knowledge mastering state; and performing analysis based on the joint probability model to obtain the potential knowledge mastering state of the student. According to the method, the potential knowledge mastering state and response mode difference can be compared among multiple groups, and an important basis is provided for evaluating educational fairness and optimizing teaching strategies.

The invention provides a personalized learning path recommendation method and device based on enhanced cognitive diagnosis. The method comprises the following steps: constructing a learning behavior heterogeneous graph to describe richer learning behavior characteristics of students; according to a knowledge forgetting simulation algorithm based on reinforcement learning, a training agent learns a knowledge forgetting rule of a student in a learning process according to a reward mechanism; evaluating knowledge cognition levels of the students at different moments according to the learning behavior sequence fused with the forgetting rules of the students; building a knowledge sequential graph by using a finite-state automatic machine so as to build a hyponym and a first-to-second relationship between knowledge points; a learning path recommendation method based on Monte Carlo search enhancement is adopted, and personalized learning paths are recommended for students in an unobservable environment according to the knowledge cognition level and the knowledge sequential diagram. According to the method, the cognitive level of the student can be accurately evaluated, the knowledge sequential graph can be automatically generated, and the real demand of recommending a reasonable and effective personalized learning path to the student in real time in an unobservable environment is met.

The invention belongs to the field of intelligent man-machine interaction, and relates to an intelligent writing and correcting method and system.The method comprises the steps that a flexible pressure sensor matrix and a coordinate positioning system are used for sensing multi-dimensional data generated when an input pen writes, and a four-dimensional writing feature space is constructed; a three-level correction system is adopted, and logical reasoning verification is conducted on the answers through a deep semantic understanding model; the three-stage correction system sequentially comprises OCR font matching, semantic correction based on a large language model and intelligent correction based on a process reward model; a cognitive diagnosis model is introduced, and the answer hesitation degree is judged by analyzing answer indexes; fusing question answering information, utilizing a large model to identify knowledge points to which questions belong, and inferring knowledge mastering weak points according to question answering conditions; writing information is accurately collected through an intelligent writing capture technology based on multi-modal sensing fusion, an intelligent correction integrated method is built in combination with a cognitive diagnosis model, and efficient recognition and accurate correction of written content are achieved.

The invention provides a mathematical question and answer process teaching system, which comprises a user interaction module, a question analysis and standardization module, a cognitive diagnosis engine, a generative teaching module, a mathematical knowledge graph database and a user portrait database, teaching content and guiding questions are presented to the user in a multi-mode form; the question analysis and standardization module is used for performing semantic understanding and structured analysis on mathematical questions and generating standardized question description and at least one standard question solving path; according to the method, the structured reasoning of the knowledge graph is combined with the natural language generation ability of the generative AI, so that a closed loop from'problem solving 'to'diagnosis' to'heuristic teaching 'is realized, thinking blockage points of students can be accurately positioned and personalized tutoring can be provided like human teachers, and the learning efficiency and the autonomous thinking ability are remarkably improved.

The invention relates to the technical field of disease diagnosis, and comprises a disease diagnosis method and system based on neural network cognitive diagnosis, and the method comprises the following steps: obtaining medical image data, calculating the gray level change rate, gradient direction distribution and edge continuity of a lesion region, counting the lesion tissue damage area, and analyzing the pathological tissue damage degree. And obtaining a lesion distribution consistency index. According to the invention, through comprehensive calculation of the medical image data and the pathological tissue slice data, fine-grained description of a focus area is realized, association among different lesion features is realized, and calculation of a lesion propagation path matching rate is realized, so that identification of lesion diffusion conditions is more accurate, and development trends of diseases in different tissue structures can be effectively predicted; the calculation of the cross-modal feature error is combined with the adjustment of the distribution weight of the lesion region, misdiagnosis caused by modal difference and extraction of abnormal signals in a lesion diffusion range are reduced, so that screening of potential diseases is more targeted, and the accuracy of disease recognition is improved.

The invention discloses a teaching implementation method based on combination of motion perception and AI driving, and relates to the technical field of education and teaching. The implementation of the teaching method comprises the steps of multi-modal data expansion and acquisition, cross-dimensional dynamic evaluation and cognitive diagnosis, emotion adaptive feedback and intervention, cross-scene learning map construction and capability migration, and whole-process data closed loop and systematic evolution. And the multi-modal data expansion acquisition comprises the steps of dynamic data acquisition, physiological data acquisition, environmental behavior data acquisition and data preprocessing. The method has the advantages that a multi-modal data acquisition scheme of a common mobile phone camera and a built-in sensor is adopted; the system replaces the traditional dance teaching which depends on special three-dimensional modeling equipment and sensor gloves for boxing teaching, reduces the hardware cost, can realize full scene coverage of families, classrooms, outdoors and the like without professional site deployment, and solves the core problems of high hardware cost and limited scenes in the prior art.

The invention provides a learning process-oriented ternary second-order coupling dynamic cognitive diagnosis method, which comprises the following steps of: dividing learning units in a learning process into'knowledge, time sequence and behavior 'triples; knowledge-time sequence and behavior-time sequence first-order coupling analysis knowledge state and behavior state changes are obtained through a neural network model and a behavior feature extraction technology, meanwhile, a guess gate, a proficiency gate and a learning gate are set by adopting a GLU gating technology, complex modeling of cognitive state changes of learners in the learning process is achieved, and the learning efficiency is improved. Obtaining a potential ability state of the learner; finally, through multi-task prediction of behavior characteristics and reaction results, diagnosis of the cognitive state of the learner and prediction of future performance of the learner are achieved. According to the invention, the learning condition of the learner can be scientifically and comprehensively predicted, and the purpose of assisting the teacher in precise teaching is achieved.

The invention provides a hierarchical attention cognition diagnosis method based on knowledge association and abnormal behavior mixing, which comprises the following steps of: aggregating heterogeneous test question attributes such as test question texts, concepts and difficulty by adopting a graph neural network technology, and updating neighbor nodes to obtain knowledge association; a short-term attention mechanism based on working memory capacity is adopted to aggregate preorder knowledge information associated in a time sequence, and first-order coupling of'knowledge-time sequence 'is achieved; key features reflecting student guessing and carelessness are extracted from click stream features, guessing and carelessness functions are set to calculate guessing indexes and carelessness indexes, and first-order coupling of'behavior-time sequence 'is achieved; based on the knowledge state and the abnormal behavior index, setting a guess gate and a carelessness gate, correcting the cognitive state of the student, obtaining the comprehensive ability state of the student, and realizing second-order coupling of'knowledge-time sequence-behavior '; and finally, adopting a multi-task learning method, and introducing an abnormal index prediction task so as to correct the answer performance prediction of the learner in the future.

The invention, which relates to the technical field of education recommendation, discloses a student portrait-driven education agent recommendation system based on a cognitive diagnosis map, comprising a time-frequency decomposition processing module, a weight rhythm matching module, a time sequence coupling damping module, a dynamic difference readjustment module and a time-frequency domain self-balancing control module. And the time-frequency decomposition processing module is used for establishing a time-frequency decomposition processing layer based on drifting characteristics of student portrait parameters in a time dimension, and performing energy distribution analysis on multi-source dynamic data streams from learning behaviors, test performance and emotion feedback according to time slices. Through time-frequency decomposition and non-resonance rhythm matching, dynamic coordination of weight adjustment and student portrait drifting is realized, characteristic fluctuation amplification is prevented, and the stability of a learning path is guaranteed; and a dynamic closed loop is formed through time sequence coupling damping and time-frequency domain self-balancing control, stable convergence of cognitive features is realized, and the accuracy and continuity of educational agent recommendation are improved.

The invention discloses an application method of a serum Raman spectrum as a biomarker in cognitive impairment diagnosis, a cognitive impairment diagnosis system is used for diagnosis, the cognitive impairment diagnosis system comprises a Raman spectrum detection and preprocessing module, a database module and a cognitive diagnosis module, and the application method comprises the following steps: step 1, collecting a sample; step 2, pre-treating a sample; 3, collecting and preprocessing a serum sample by using a Raman spectrum detection and preprocessing module; 4, performing model construction and optimization by utilizing a database module; step 5, performing efficiency evaluation; and 6, performing cognitive grading diagnosis on the new sample by using a cognitive diagnosis module. The method has remarkable technical progress, no cognitive impairment diagnosis blood test method suitable for the whole crowd exists clinically at present, cognitive impairment can be diagnosed through the Raman spectrum of serum, detailed cognitive function grading can be carried out, and the cost of a single sample is less than 100 yuan.

The invention discloses a personalized tutoring method integrating collaborative cognitive diagnosis and adaptive teaching. The method comprises the following steps: firstly, constructing a college entrance examination data set, constructing an initial knowledge hierarchy tree of each college entrance examination subject according to the college entrance examination data set, and sequentially performing cognitive diagnosis processing on knowledge points corresponding to unmarked nodes on the knowledge hierarchy tree according to the initial knowledge hierarchy tree in an inverse sequence traversal sequence to obtain an initial student cognitive state tree; the teacher performs tutoring processing on all the sub-question-solving steps of each college entrance examination question according to the initial student cognitive state tree until tutoring processing on all the sub-question-solving steps is completed; and all college entrance examination questions are tutored in a repeated traversal manner until all college entrance examination questions are tutored. According to the method, through two-stage personalized tutoring of cognitive diagnosis and adaptive teaching, accurate evaluation of student individual cognitive states and adaptive adjustment of teaching strategies are realized, and the performance of the method is superior to that of an existing method.

The invention discloses a grassland desertification intelligent closed-loop management method and system based on multi-source remote sensing, and belongs to the technical field of ecological restoration and intelligent decision making. According to the method, technical breakthrough is realized by constructing an intelligent closed loop of perception, cognition, decision and optimization: firstly, multi-source data are fused to construct a dynamic ecological knowledge graph, and a desertification cause diagnosis report for quantifying the contribution rate of each driving factor is generated based on graph reasoning; calling a governance measure knowledge base to generate a scheme according to a diagnosis result, and optimizing a decision through digital twinborn rehearsal; finally, based on treatment effect feedback, the knowledge base is dynamically optimized through reinforcement learning; the corresponding system comprises a multi-source perception fusion module, an ecological cognitive diagnosis module, a decision rehearsal execution module and a self-learning optimization module. According to the method, the bottleneck of lack of causal diagnosis and system stiffness of a traditional method is overcome, the crossing from passive monitoring to active cognition and from static decision to dynamic self-learning is realized, and an innovative solution is provided for treatment of a degraded ecological system.

The invention discloses a subject cognition teaching knowledge construction and intelligent navigation system based on a knowledge graph, and relates to the field of education technology and intelligent teaching. The knowledge graph construction module is used for extracting knowledge points and association relationships thereof from subject teaching resources and constructing a structured knowledge graph; the personalized cognitive state vector generation module analyzes the mastering degree of knowledge points by means of a cognitive diagnosis model, and generates a state vector reflecting the cognitive level of an individual; the navigation strategy generation module adopts a hierarchical reinforcement learning method, and dynamically generates a navigation strategy containing knowledge point priorities and an optimal learning path by taking a learning target as guidance; and after the learning effect adjustment module pushes the strategy to the learner, acquiring knowledge point mastering state deviation and learning strategy consistency deviation in real time, and synchronously updating the edge weight of the knowledge graph and cognitive diagnosis model parameters. According to the system, through dynamic evolution of the knowledge graph and self-adaptive updating of the cognitive model, accurate optimization of a teaching strategy is realized, and the personalized learning navigation efficiency is improved.

The invention relates to the technical field of cognitive diagnosis, and discloses a differential chemical condition modeling method based on long-term and short-term interaction data, and the training process of a cognitive model comprises the steps: arranging question samples composed of interaction data and answering results of students on each question into learning records of the students; constructing a recent learning record and a long-term learning record based on the learning record; the recent learning records are sent to a static knowledge tracking module for learning modeling, and recent expressions of the students are obtained; the long-term learning record of the student is sent to a dynamic knowledge tracking module, and the historical cognitive state of the student is captured; obtaining the current cognitive state of the student through a fusion gating network, and predicting the probability that the student correctly answers the question; and taking the cross entropy between the probability and the actual answer result as a loss function for training. According to the method, the cognitive level of the students can be reflected, the future learning achievements of the students can be predicted, and a scientific basis is provided for educational decision making.

The invention discloses an online education cognition diagnosis method based on heterogeneous conceptual graph construction and modeling enhancement, and the method comprises the following steps: extracting practice records of a plurality of students, and extracting a plurality of knowledge concepts in the practice records; selecting knowledge concept pairs of which the similarity is higher than a threshold value, identifying the relation between knowledge concepts, and constructing an accurate heterogeneous conceptual graph through triple generation and triple evaluation; dividing the heterogeneous concept graph into three sub-graphs according to the relationship type; basic representations of the three sub-graphs are obtained through a sub-graph encoder; the important sub-graph features are dynamically identified through a self-adaptive selector; updating knowledge concepts and practice features through a graphic information aggregator; a diagnostic prediction of the student's knowledge grasp level is generated based on the response records and the graphical information. According to the method, the complex relation between knowledge concepts is mined, the accurate inference on the knowledge mastering level is improved, the basic sub-graph features are adaptively recognized, and graph representation is integrated.

The application belongs to the technical field of personalized learning, and discloses a multi-level intelligent cognitive tracking method, system, storable medium and terminal, the method comprising: introducing Bloom cognitive domain education target classification, constructing test question knowledge cognitive tensor TKC, collecting learning resources and answer data of learners, and generating a sequence of learner time sequence answer pairs; introducing a multi-attribute cognitive diagnosis method, combining a deep neural network, and constructing a cognitive level mining model; sorting and encoding the cognitive level mining results of the learners to obtain deep representation features, combining a self-attention mechanism, constructing a multi-level intelligent cognitive tracking model, and further predicting the answer performance of the learners on the test questions. The application is beneficial to accurately and finely modeling the overall knowledge structure and specific level of the learners, thereby promoting personalized learning of the learners and providing a new idea for mining and tracking the cognitive state and level of the learners in an online learning platform.

The invention provides a method and system for cognitive diagnosis and attribution analysis of teacher teaching, and belongs to the technical field of teaching management. The method specifically comprises the following steps: constructing a hybrid multi-dimensional capability vector for a student agent; acquiring a teaching instruction of a teacher, analyzing the teaching instruction, determining a teaching mode and analyzing teaching content; the intelligent agent responds to the teaching mode and the teaching content, calls an ability probe algorithm to evaluate the performance of the intelligent agent for completing the task, and calculates and updates a multi-dimensional ability vector of the intelligent agent; and comparing state differences of the multi-dimensional capability vector before and after evolution, modifying a teaching instruction based on an anti-fact attribution algorithm, carrying out anti-fact inference, and generating an evaluation report containing diagnosis information. By constructing a hybrid multi-dimensional ability vector, accurate description and dynamic tracking of the learning ability of the student are realized; and evaluating task performance in real time and updating a multi-dimensional capability vector through a capability probe algorithm to form a closed-loop feedback mechanism.

The invention provides a personalized lesson preparation method and system based on multi-modal data fusion and a cognitive map, and relates to the technical field of intelligent education. Extracting a student cognition state from the student cognition map, and performing time sequence evolution analysis on the student cognition map to obtain a change trend of the student cognition state; semantic difference quantization is carried out on the cognitive state and the mastery degree prototype of the student based on the change trend, and a cognitive state residual vector is obtained; performing collaborative reasoning on the cognitive state residual vector to obtain a personalized lesson preparation path and teaching strategy configuration, and performing teaching resource matching and teaching plan assembly based on the personalized lesson preparation path and teaching strategy configuration to obtain a personalized hierarchical teaching plan; the personalized hierarchical teaching plan is dynamically adjusted through the classroom feedback data, and then lesson preparation evaluation data is obtained, so that personalized teaching strategy generation based on dynamic cognitive diagnosis and closed-loop optimization can be realized, and the accuracy of teaching intervention is improved.

The invention discloses a generative agent cognitive diagnosis framework fusing teacher thinking, and the framework is used for modeling a cognitive judgment process of a teacher in a real teaching scene, systematically constructing task-aware thinking modeling, a behavior track-based observation reflection mechanism, and a causal-driven explanation generation module. Therefore, dynamic modeling and deviation diagnosis of the cognitive state of the student are realized. The method has the advantages that firstly, the method has good cold start capability, and reasonable judgment can be made even if complete historical behavior data is lacked; 2, the method has relatively high interpretability, can output a causal chain of'knowledge state-behavior process-task performance ', and supports accurate teaching intervention; and thirdly, the method has the ability of distinguishing the thinking levels of students, can assist in completing Bloom cognitive classification, and provides effective support for task layering and teaching design.

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 discloses a cognitive diagnosis method based on hierarchical constraint perception. The method comprises the following steps: S1, inputting student information to be processed; s2, performing hierarchical division on historical scores in the student information; s3, performing convolution feature extraction on the student information of the same level, and performing attention network aggregation to obtain knowledge features in the level; s4, aggregating student information of different levels, and forming a level knowledge proficiency degree vector by combining knowledge features in the levels and among the levels; s5, combining the hierarchical knowledge proficiency vector with the personalized knowledge proficiency vector to form a comprehensive proficiency vector, inputting the comprehensive proficiency vector into the cognitive diagnosis model, and predicting a practice question result; according to the method, cognitive diagnosis is carried out according to student information, an attention mechanism of internal and external knowledge states is introduced, the diagnosis precision is improved, the interpretability and reasoning ability of diagnosis are enhanced, and the result is more accurate and can be easily applied to teaching practice.

The invention relates to the technical field of intelligent education, in particular to a cognitive diagnosis method and model based on an emotional state, and the model comprises a multi-modal information feature extraction module, a feature alignment layer, a modal selection layer, a self-adaptive super-modal learning layer and a cross-modal fusion conversion layer which are cascaded. The method comprises the following steps: extracting emotion-related multi-modal features from a teaching video, and obtaining knowledge point information corresponding to the emotion-related multi-modal features; the extracted multi-modal features are projected to a unified low-dimensional space, so that data are in the same dimension, and preparation is made for subsequent fusion; adaptively selecting a main mode and an auxiliary mode; emotional semantic expression is formed through information interaction between the main mode and the auxiliary mode; and fusing the main mode and the super-mode state to generate a unified super-mode state representing the emotion cognitive state of the student. The problem that cognitive diagnosis in the prior art is not comprehensive enough is solved, and the method has the advantages of being high in accuracy and applicability.

The invention belongs to the technical field of releasable cognitive diagnosis, and discloses an ECD-RC releasable cognitive diagnosis method based on relation enhancement and causal reasoning, a relation enhanced feature extraction module is designed, feature information is aggregated through the interaction strength between modeling entities, and the dependency relationship in a complex education scene is effectively captured. Meanwhile, an explainable module based on causal reasoning is introduced, and key factors influencing student performance and an action mechanism thereof are revealed through anti-factual reasoning. A wide range of experiments are carried out on two public education data sets, the effectiveness and interpretability of the method are proved by experimental results, and powerful support is provided for realizing personalized teaching.

The invention discloses a learner cognitive diagnosis method oriented to an intelligent education scene, and belongs to the technical field of cognitive diagnosis and education data analysis. According to the method, concept-level multi-dimensional modeling is carried out on the ability and exercise difficulty of a learner by constructing multi-dimensional representation of concept perception so as to describe mastering characteristics of the learner on different cognitive levels; and meanwhile, distinguishing a pre-correction dependency relationship and a semantic approximation relationship, establishing a relationship-perceived concept dependency model, and deducing a potential exercise-concept association structure according to the relationship-perceived concept dependency model. Further, the annotated exercise-concept incidence matrix and the inference incidence matrix are fused in a unified diagnosis layer, and comprehensive evaluation of the knowledge mastering state of the learner is achieved. According to the method, an end-to-end mode is adopted for optimization, the fineness, knowledge coverage and stability of cognitive diagnosis can be effectively improved, better prediction performance and generalization ability are shown on multiple real education data sets, and the method is suitable for intelligent education application scenes such as learning analysis and personalized teaching.

The application provides a dynamic graph neural cognitive diagnosis method fusing multi-dimensional features, and the method comprises the following steps: collecting multi-modal data of an online learning platform, and obtaining a multi-dimensional feature splicing vector based on the multi-modal data; based on the multi-dimensional feature splicing vector and by using a graph neural network, obtaining an aggregation matrix from an embedding matrix of a current knowledge point, a hidden state matrix of the current knowledge point, an embedding matrix of a neighboring knowledge point, and a hidden state matrix of the neighboring knowledge point; performing a forgetting process on the embedding matrix of the knowledge point by using a forgetting vector, inputting the aggregation matrix into a memory gate structure model, and capturing a time sequence feature embedding matrix of the knowledge point; based on the time sequence feature embedding matrix and by using the graph neural network, constantly updating the hidden state matrix of the knowledge point, and obtaining an updated knowledge mastery degree of the learner at the next moment. The application comprehensively diagnoses the learning effect of the learner and updates the knowledge mastery state of the learner, and greatly improves the accuracy of the diagnosis result.

The application provides a cognitive diagnosis method and system based on causal inference to eliminate problem difficulty bias, relates to the technical field of educational data mining, and specifically includes the following steps: preprocessing the historical record of a student's answer to a problem to obtain a training set composed of a student, a problem, problem difficulty and a score; introducing a problem difficulty variable on the basis of an existing diagnosis model, eliminating bias by using causal inference, constructing a cognitive diagnosis model, and training the cognitive diagnosis model by using the training set; inputting a student to be diagnosed, a problem and problem difficulty into the trained cognitive diagnosis model to obtain a cognitive diagnosis result of the student output by the diagnosis model; the application provides a general cognitive diagnosis framework, creates a cognitive diagnosis model based on an existing diagnosis model, eliminates the adverse effects of problem difficulty bias by using causal inference technology, and performs unbiased training on the model to diagnose the cognitive level of the student, thereby improving the accuracy and efficiency of cognitive diagnosis.

The invention discloses a cognitive diagnosis method based on federated learning, which is used for jointly training a cognitive diagnosis model based on a personalized strategy of parameter decoupling, protecting data privacy and realizing accurate and fair diagnosis of students of each client. A local cognitive diagnosis model is trained for students in each client according to local student learning data, each client uploads part of model parameters to a central server for parameter aggregation according to a designed personalized strategy based on parameter decoupling, and federal learning and cognitive diagnosis are sought to be combined. The method comprises the following steps of: firstly, updating shared parameters through difference perception aggregation and loss perception aggregation to ensure the diagnosis accuracy of a cognitive diagnosis model and protect the privacy of a user, updating the shared parameters through difference perception aggregation and loss perception aggregation on the basis of a personalized strategy of parameter decoupling, and locally storing the parameters of a diagnosis function as private parameters.

This application belongs to the field of intelligent assessment technology, specifically disclosing a matrix optimization method, apparatus, and device based on a cognitive diagnostic model. This application removes the questions to be verified from the correlation matrix to be optimized and the corresponding answer data from the answer matrix; it determines the student's knowledge point mastery vector and question feature vector based on the target cognitive diagnostic model; and it sequentially verifies multiple knowledge points associated with the questions to be verified according to a preset positive testing strategy, optimizing the correlation matrix by correcting the knowledge points to be optimized. By removing the questions to be verified and their corresponding answer data from their respective matrices, the accuracy of the input model's data is improved. The preset positive testing strategy ensures that knowledge points in the correlation matrix to be optimized are corrected only when sufficient and significant statistical evidence is obtained, thereby effectively improving the accuracy and efficiency of the optimization matrix.