113 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 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, 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 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 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 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.

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.

The application belongs to the field of intelligent human-computer interaction, and relates to a kind of intelligent writing and correcting method and system, comprising: utilizing flexible pressure sensor matrix and coordinate positioning system, the multidimensional data of sensing input pen writing, and constructing four-dimensional writing characteristic space;Adopt three-level correcting system, the logical reasoning verification of answer is carried out through depth semantic understanding model;The three-level correcting system successively includes OCR character shape matching, semantic rectification based on large language model and intelligent correction based on process reward model;Cognitive diagnosis model is introduced, and the degree of hesitation of answering is judged by analyzing answering index;Fusion question solving information, the knowledge point of question is identified using large model, and according to the question answering condition, the weak point of knowledge mastery is inferred;With the intelligent writing capture technology based on multi-modal sensor fusion, the writing information is accurately collected, and the intelligent correcting integrated method is constructed combined with cognitive diagnosis model, the efficient identification and accurate correction of writing content are realized.

The invention relates to the technical field of education evaluation, in particular to an English reading understanding ability self-adaptive evaluation method and system. The method aims at solving the technical problems that the traditional fixed difficulty test is easy to cause inaccurate capability estimation, the existing adaptive test is rough in subject description difficulty, the reading skill diagnosis is fuzzy, and the question bank security is insufficiently considered. According to the technical scheme, the method comprises the steps that a pre-training language model is used for conducting word, syntax, chapter and subject multi-dimensional difficulty feature extraction on a reading material; correcting question parameters based on an item reaction theoretical model in combination with answer data; dynamic evaluation is realized by adopting a self-adaptive test engine based on Bayesian capability estimation and maximum information amount topic selection; outputting the fine-grained skill mastering degree through the cognitive diagnosis model with the Q matrix constraint; a question exposure control strategy is integrated to balance measurement precision and question bank safety. And the system finally outputs a capability level, a skill diagnosis radar map and personalized reading recommendation.

The application discloses an education resource retrieval method and device based on cognitive diagnosis and learning path, and the method comprises the following steps: determining a test question-knowledge point matrix and a user-test question matrix according to a knowledge point set, and inputting the matrix into a cognitive diagnosis model, so that the model judges the comprehensive mastery degree of each user on each knowledge point, and obtains weak knowledge points of the user; the test question-knowledge point matrix comprises a video-knowledge point matrix, a practice question-knowledge point matrix and other resource-knowledge point matrix; and the user-test question matrix comprises a user-video matrix, a user-practice question matrix and a user-other resource matrix. The AprioriAll algorithm is used to scan the knowledge point set, the weight value of the weak knowledge points of the user is increased in the learning path planning process, and a recommended learning path is generated; a plurality of member search engines are introduced to perform resource retrieval on the recommended learning path and integration on retrieval results, so that a recommended result is obtained, and the accuracy of the education resource retrieval is improved.

The invention discloses a neural network cognitive diagnosis system based on rule prior, and the system comprises a parameter initialization module which is used for loading and preprocessing student answer records and question related knowledge information, and constructing an embedded layer of a neural network, and the embedded layer corresponds to a student ability vector, a question difficulty vector and a question distinction degree vector; the rule constraint module is used for introducing an interpretable constraint item; comprising the following steps of: ensuring the ability sorting consistency constraint of consistent student ability sorting and actual answering performance, forcing the negative correlation discrimination-variance negative correlation constraint of question discrimination and reply ability variance, and calibrating the difficulty of questions as the difficulty threshold consistency constraint of a decision threshold of student ability; and the diagnosis network module is used for constructing an output layer of a neural network based on the two-parameter item reaction theory and calculating the answer probability. According to the method, the neural network model fusing rule constraints is constructed, so that high-precision answer prediction is realized, and meanwhile, student ability estimation and question parameters are ensured to be consistent with an education theory.

The present invention provides a hierarchical attention cognitive diagnosis method based on a hybrid of knowledge association and abnormal behavior. The method uses graph neural network technology to aggregate heterogeneous test item attributes such as test item text, concepts, and difficulty, and obtains knowledge association through neighbor node update; adopts a short-term attention mechanism based on working memory capacity to aggregate the pre-order knowledge information associated in time series, realizing the first-order coupling of "knowledge-time series"; extracts key features reflecting students' guessing and carelessness from click stream features, sets guessing and carelessness functions to calculate the guessing index and carelessness index, realizing the first-order coupling of "behavior-time series"; based on the knowledge state and abnormal behavior index, sets a guessing gate and a carelessness gate to correct the cognitive state of students and obtain the comprehensive ability state of students, realizing the second-order coupling of "knowledge-time series-behavior"; finally, adopts a multi-task learning method and introduces an abnormal index prediction task to correct the prediction of learners' future answering performance.

The invention belongs to the technical field of online education, and discloses an exercise recommendation system and method based on hierarchical reinforcement learning and multi-objective optimization, and the system comprises a multi-objective optimization index definition module which is used for defining exercise diversity, exercise novelty, recommendation accuracy, learner satisfaction, learner enthusiasm, learner score and system question setting time; a comprehensive objective function is formed through weighted summation; the three-layer reinforcement learning architecture module comprises a high-level strategy, a middle-level strategy and a low-level strategy which are respectively responsible for global optimization target planning, strategy execution and personalized exercise recommendation; the cognitive diagnosis model and Transform prediction model fusion module is used for evaluating the knowledge mastering condition of the learner and optimizing a recommendation strategy; and the improved Pareto optimization method module is used for processing uncertainty in multi-target optimization, solving target conflicts and improving the convergence speed of an algorithm through dynamic parameter adjustment. According to the invention, appropriate exercises can be accurately recommended according to the learning state of each student.

The application discloses a cognitive diagnosis method for student ability representation based on reliability, and steps of the method comprise the following steps: firstly, modeling the student ability representation as a distribution based on a Bayesian method, and using variance in the distribution to represent the reliability of the diagnosed ability representation; secondly, considering potential differences of each student, and using a pre-training model to establish individual prior distribution of latent variables of different ability concepts; thirdly, using uncertainty regularization technology to make the variance in the distribution neither too volatile nor too stable; and finally, using a calibration loss function to ensure the reliability of the diagnosed ability representation. The application integrates the Bayesian method and the calibration loss function, and can perform cognitive diagnosis on the student ability representation and evaluate the reliability of the ability representation.