51 results about "Cognitive diagnosis" patented technology

The invention discloses an online cognitive assessment system and an assessment method, and belongs to the field of assessment systems. The online cognitive assessment system comprises a terminal platform, wherein the terminal platform comprises a processor module, a power supply module, a man-machine interaction module, a communication module, a recording module, an assessment module, a storage module, a calculation module and an output module; the power supply module, the man-machine interaction module, the communication module, the recording module, the assessment module, the storage module, the calculation module and the output module are connected with the processor module respectively. Online cognitive diagnosis is carried out through reading and processing information of each module. By adopting the online cognitive assessment system and the assessment method, rapid and accurate assessment and good accuracy can be realized, and information is updated in real time.

The invention discloses a personalized test question recommendation method based on user learning behavior, the method is as follows: obtaining user history problem data, test questions and knowledgepoint information from an online education platform; constructing a user based on user history data data; R, according to the relationship between the test questions and the knowledge points, constructing the test questions-Knowledge point correlation matrix Q; constructing the user cognitive diagnosis model through the DINA model, obtaining the user knowledge point mastering matrix A; obtainingthe user adjacent test set according to the matrix A, according to Matrix Q the test questions-Adjacent test sets, constructing alternative test sets; non-negative matrix decomposition of matrix R, obtaining the implicit feature matrices W and H of users and test questions, find the estimated values of W and H matrices, and get the score prediction Model; calculating the potential answering situation of the user, and recommending the test questions of the target user's own difficulty range to the target user. The invention can accurately recommend the test questions suitable for the target user to the user. The invention is applicable to the field of online education.

The invention discloses a student modeling and personalized course recommendation method in an online learning system, and belongs to the education data mining field. According to the method, cognitive level modeling and personalized course recommendation of students are mainly studied, firstly, knowledge mastering states of the students are judged based on a cognitive diagnosis model, learning behaviors of the students are analyzed through data on a system platform, and then the cognitive abilities of the students are modeled by integrating course mastering conditions; secondly, an online course is modeled ; and finally, the features of the online course are fused according to the cognitive level of the student to perform personalized recommendation. According to the invention, personalized recommendation is carried out based on the cognitive level of students and in combination with the feature indexes of the online courses, so that the user can be helped to carry out more accurate personalized course recommendation, and the online course recommendation is more interpretable and acceptable.

The invention discloses a student cognitive diagnosis method, which comprises the following steps of obtaining the historical answer information of a student, and extracting a test question text and the predefined knowledge points contained in the test question text; calculating a knowledge point relevancy vector of each test question according to the test question text and the contained predefined knowledge points; taking the set student parameters and the test question parameters containing the knowledge point relevancy vectors of the test questions as the input of a cognitive diagnosis model constructed by a neural network, fitting the answer results, obtaining the knowledge point mastery degree vectors of the students through training, and completing the cognitive diagnosis of the students.

The invention discloses a self-adaptive network security knowledge evaluation method based on a cognitive diagnosis theory. The self-adaptive network security knowledge evaluation method based on thecognitive diagnosis theory comprises the following steps: S1, an evaluation system generates a network security knowledge graph according to the identity background of a user, and tests the user according to a preset sequence and a knowledge structure; S2, the test system generates a personal basic information database according to the personal identity background information uploaded by the userand a specific format; and S3, the test system traverses according to the structural sequence of the knowledge graph for item-by-item test, and accurate positioning of the knowledge level of the useris realized based on test question extraction of the corresponding difficulty standard. According to the method, a potential knowledge state is obtained through real-time feedback of a user in testing, and a novel cognitive diagnosis model PH-is utilized; And the real knowledge, skill level and corresponding short boards of the user are efficiently reasoned by the DINA, so that efficient and accurate education evaluation is realized, and the learning condition of the user is better reflected.

An adaptive computer-implemented e-learning system and method able to deliver a learning approach that customizes and adapts to each learners' skills and knowhow. A computer-implemented cognitive diagnosis process for predictions on skills and knowledge of a learner comprising the steps of: building a domain ontology; establishing a items bank; referencing the items bank using elements from the domain ontology elements (semantic-grounding of the items); establishing a training database of learners answers; estimating Cognitive Diagnostic Model (CDM) parameters and determining an appropriate model to be used; running the model to estimate a learner level on non-abstract latent variables; applying a Mastery of domain Knowledge and competency Skill (MKS) approach to estimate the learner level on abstract latent variables, or extracting relevant hierarchies from the ontology and use the CDM appropriate model to estimate those attributes; and building a diagnosis report from the results of steps 6) and 7).

A word mapping method is provided, based on a plurality of connections between a plurality of words as generated by an individual or group, the map being formed based on connections between words provided by an individual or individuals. The resultant word map may be used for various purposes including cognitive diagnoses, sentence structure optimization of documents, and the like.

The invention discloses a cognitive diagnostic method for a cognitive process of a student, comprising the following steps: constructing a multi-granularity representation model of knowledge points and exercises, constructing a learning state representation model of a student node, and analyzing the cognitive diagnosis of the student node. The invention can use the method of knowledge map to express the academic state of the students in multi-granularity, so as to analyze the mastery degree of the corresponding knowledge points of the students according to the answers of the students accordingto the different cognitive processes of the students.

Provided are an apparatus and method for learner diagnosis using reliability of a cognitive diagnostic model, which estimates the reliability of the cognitive diagnostic model that estimates a concept vector (α) of a learner through a Q-matrix regarding a question and an R-matrix regarding a response to a question, the method including assuming a probability (P(X|α)) of a learner response (X) when a concept vector (α) of a learner is given; obtaining a concept pattern-specific probability (P(α|X)) of the learner from the assumed concept vector and learner response of the learner; obtaining an information entropy (H) value of the learner from the concept pattern-specific probability (P(α|X)) of the learner; and obtaining reliability (γ) of an estimated result of a learner-specific concept understanding using the information entropy value of the learner and a number of concepts.

The invention provides a practical effect evaluation and learning path recommendation method based on cognitive diagnosis. The practical effect evaluation and learning path recommendation method comprises an expansion feature preprocessing model, a deep knowledge tracking model, a knowledge network construction model and a path recommendation algorithm based on cognitive ability. The expansion feature preprocessing model performs primary evaluation of cognitive competence according to skill attributes in a learner test process, and introduces personalized difference information into the diagnosis model. And the deep knowledge tracking model predicts the knowledge mastery ability of the learner according to the test sequence and the heterogeneous features of the implicit coding, and the knowledge mastery ability serves as a basis of learning guidance. The exercises and knowledge network construction model provides a global map of scientific thinking, and the cognitive diagnosis is combined to recommend a learning path to a learner, so that the cognitive ability difference in the learning process is considered, and the logic of a knowledge structure is followed.

The invention belongs to the technical field of intelligent education, and discloses a personalized test paper composition method and system fusing cognitive characteristics and test question text information, and the method comprises the following steps: firstly predicting the score of a learner on a specific test question based on the cognitive level through a cognitive diagnosis model; predicting scores of the learners on the specific test questions based on text information by using a recurrent neural network model; constructing a probability matrix decomposition target function on the basis of the obtained learner based on the cognitive level and the prediction score of the text information, and predicting the potential score of the learner on the specific test question; and finally,calculating KL divergence by utilizing the estimated learner knowledge mastering vector and the learner incremental knowledge mastering vector, and selecting test questions with increased learner knowledge mastering trend and proper difficulty to form personalized test paper in combination with the potential score of the learner on the test questions. According to the invention, the test paper forming result can be customized according to the test target and the test question difficulty, and the autonomous learning efficiency of learners is greatly improved.

The invention discloses a cognitive diagnosis method based on a deep project reaction theory. The cognitive diagnosis method comprises the following steps: acquiring historical exercise data of students; preprocessing the historical exercise data, and modeling the students and the exercise data by using a deep learning method and combining a preprocessing result to obtain potential characteristicsof the students, exercise difficulty and distinction degree; and according to the potential characteristics of the students and the difficulty and discrimination of the exercises, predicting the scores of the students on the exercises by utilizing a two-parameter model of the project reaction theory. According to the method, more effective, more detailed and more robust cognitive diagnosis results can be provided for students.

This present system and method provide artificial intelligence systems and methods for automatic identification, localization, recognition, understanding, labelling, analyzing, assessing, deciding and planning related to dento-craniofacial visual assets (‘DCVA’) for creating a report for patient treatment and consultation.

The invention belongs to the technical field of education big data mining, and discloses a multi-modal unified intelligent learning diagnosis modeling method and system, a medium and a terminal. The method comprises the steps: constructing a multi-channel cognitive diagnosis model, performing preliminary diagnosis on learners, and performing parameter estimation on learning resources to obtain a learning resource parameter set and a learner parameter set; performing modeling on the learning resources and learners to obtain depth representation features; introducing a self-attention mechanism to fuse learner features and learning resource features; taking the fusion features as a data basis for predicting the performance condition of the learner, and constructing a learner performance prediction network to obtain a predicted value of the correct answer probability of the learner; and diagnosing the overall knowledge point mastering condition of the learner according to the characteristic information of the learner and the exercises, and acquiring parameter characterization of the exercises. The advantages of the multi-channel cognitive diagnosis model can be fused, the neural network is designed to carry out intelligent learning diagnosis on the learner, and expandability is achieved.

The invention provides a sequential cognitive diagnosis method and apparatus, which relates to the technical field of educational testing. The sequential cognitive diagnostic method first acquires thecurrent diagnostic test data and the preamble diagnostic test data of a learner, and then acquires the current diagnostic test results based on the current diagnostic test data and the preamble diagnostic test data. The sequential cognitive diagnosis method considers the limitation and instability of the cognitive diagnostic results of a single test, screens the preamble diagnostic test data, extracts the effective preamble information, inputs into a sequential diagnosis model based on the effective preamble information and the current diagnostic test data to obtain a final current diagnostictest result, thereby improving the accuracy and comprehensiveness of the cognitive diagnosis.

A personalized test question recommendation method based on student portraits aims at the objective current situation that students are easy to get lost in the current learning process and the learning state is difficult to accurately judge, a campus big data platform is constructed by integrating an existing system and developing a new system, and then the accurate portraits of the students are described. On the basis, a fuzzy cognitive diagnosis model is provided to reasonably judge the learning state attribute of the student, and the question answering condition of the student is predicted in combination with the requirements of the test questions on the learning state of the student. Based on the prediction information, a utility-based test question recommendation method is designed, and test questions with high answering utility are recommended to students. Compared with a traditional DINA method and a DINO method, the FDINA method provided by the invention not only achieves a lower root-mean-square error and a lower mean absolute error in the aspect of test question answering prediction results, but also obtains higher accuracy, a higher recall rate and a higher F1 value in the aspect of recommendation results, and in addition, the FDINA method can also effectively improve the earnings of students in answering test questions.

The embodiment of the invention provides a cognitive diagnosis method and system based on learning behaviors, and the method comprises the steps: determining student numbers and answer numbers to be subjected to cognitive diagnosis, and enabling the student numbers and the answer numbers to be in one-to-one correspondence with student answers and corresponding video records contained in learning courses; inputting the student number to be subjected to cognitive diagnosis and the answer number into a diagnosis model to obtain a student cognitive diagnosis result output by the diagnosis model, wherein the diagnosis model is obtained by training on the basis of test question samples, corresponding knowledge point marks, corresponding video samples and corresponding video marks; the diagnosis model is used for constructing a course diagram based on the test question samples, the corresponding knowledge point marks, the corresponding video samples and the corresponding video marks, and performing corresponding student cognitive diagnosis on the learning course to be subjected to cognitive diagnosis after node information updating is performed on the course diagram through a graph neural network. According to the embodiment of the invention, the knowledge level of the student is effectively predicted.

The invention discloses a learning feature data processing method and a device, and electronic equipment. The method comprises the steps of obtaining initial learning feature data of a plurality of target objects in a historical learning process, and performing grouping based on feature levels of the initial learning feature data to obtain learning feature grouping attributes; determining an attribute weight of each target object based on a priority ranking result of the learning feature grouping attributes; matching the initial learning feature data of each target object with the learning feature grouping attributes to obtain a matching result; calculating an attribute score of the learning feature grouping attribute of each target object based on the attribute weight of the target objectand the matching result; grouping the plurality of target objects based on the attribute scores of the learning feature grouping attributes to obtain a plurality of object sets; and initializing testquestion parameters of the target object in each object set, and inputting the test question parameters into the cognitive diagnosis model to estimate learning parameters of each object set.

The invention discloses a cognitive diagnosis method based on meta-knowledge dictionary learning. The cognitive diagnosis method includes the steps of constructing an over-complete meta-knowledge dictionary is constructed, and expressing the answering situation of students on the dictionary; supposing that knowledge points mastered by students and knowledge points involved in questions are linearly composed of meta-knowledge points, and sparsely representing knowledge structure states of the students through a meta-knowledge dictionary; under the condition that expert knowledge points are known, learning and obtaining a relation matrix of the expert knowledge points and meta-knowledge points; and obtaining the mastering condition of each student for the expert knowledge points by utilizingthe product of the sparse representation matrix of the students and the meta-knowledge points and the relation matrix of the expert knowledge points and the meta-knowledge points. Besides, the students with the same knowledge structure are clustered into one class by using the sparse representation matrix of the students so that the students can be better grouped and the student clustering effectcan be enhanced.

The invention discloses a student score prediction method and device based on a fuzzy cloud cognitive diagnosis model. The method comprises the following steps: establishing a student cognitive cloud model; according to a solving result of the student cognitive cloud model, obtaining a mastery degree interval number of students on knowledge points; according to the mastering degree interval number of the knowledge points, obtaining the mastering degree interval number of the students on test questions; and obtaining a prediction score of the test questions according to a target model parameter obtained by iterative training and the mastering degree interval number of the students on the test questions. According to the fuzzy cloud cognitive diagnosis model, fuzzy interval numbers obtained through student cognitive cloud conversion are used for depicting fuzziness and uncertainty of knowledge point mastering degrees of students, and more comprehensive representation of student cognitive states is achieved; besides, the fuzzy cloud cognitive diagnosis model simplifies model parameters, shortens model execution time, and effectively improves the prediction accuracy and calculation efficiency of student scores in a large-scale online learning scene under the support of the model.

The invention discloses a computerized adaptive test method based on cognitive diagnosis. The method comprises the following steps: S1, establishing a question selection model which requires a large amount of answering data and test question examination knowledge point data; S2, logging in the system by a tester; S3, selecting a first test question, and selecting a test question for testing; S4, after each question is finished, calculating a current knowledge mastering state of the tester; S5, judging whether answering is finished or not through conditions; and S6, ending the test, and outputting a result. The technology provided by the invention is very novel, and compared with the current online test adopting a fixed test paper mode, the technology provided by the invention has the advantages that the test length can be shortened, the time of a testee is saved, and the knowledge mastering level of the testee can be accurately measured; the length of the test can be reduced and testedwith different abilities can be diagnosed by using the self-adaptive test; compared with the traditional test, the self-adaptive test can be used to greatly reduce the test length; and by adopting aShannon entropy question selection method and a GDINA model, the measurement precision can be improved.

The invention discloses a cognitive diagnosis method based on a learner cognitive response model. According to the invention, the method comprises the steps: evaluating and / or predicting the cognition condition of a learner through a cognition reaction model, wherein the cognitive response model comprises a learner ability level, a learner effort degree, a learner knowledge level, a knowledge characteristic matrix, a test question characteristic matrix, knowledge weak item parameters, an inspection matrix and a result matrix, and a mutual compensation relationship exists between the ability level and the effort degree. Compared with the prior art, the cognitive diagnosis method provided by the invention has higher accuracy.

This present system and method provide artificial intelligence systems and methods for automatic identification, localization, recognition, understanding, labelling, analyzing, assessing, deciding and planning related to dento-craniofacial visual assets (‘DCVA’) for creating a report for patient treatment and consultation.

The invention discloses a model-independent adaptive test method which is characterized by comprising the following steps: estimating the cognitive state of an examinee according to the historical answer record of the examinee so as to predict the answer probability of the examinee to each question in an untested question set, and through a model-independent information amount evaluation function,quantifying the information amount of each question in the untested question set, and selecting the top KC questions according to the information amount to form a high-quality candidate set; and quantifying the diversity of a tested question set through a model-independent question set diversity evaluation function in combination with importance weights of knowledge points in questions, and selecting the question with the maximum boundary gain of the diversity from a high-quality candidate set to serve as a final result of question selection this time. The method is advantaged in that dependence of the algorithm on model underlying details is stripped, the method is suitable for all existing cognitive diagnosis models, coupling of the adaptive test system is reduced, and flexibility of the adaptive test system is improved.

The invention provides a cognitive diagnosis method and system. The method comprises the following steps of: obtaining cognitive attribute information and reaction information of a subject; determining a project attribute relationship Q matrix according to the cognitive attribute information and determining a subject reaction X matrix according to the reaction information of the subject; constructing an OR-DINA model, wherein the model comprises an error parameter and a guess parameter; estimating the error parameter and the guess parameter by utilizing an expectation maximization EM algorithm; and calculating a knowledge state of the subject according to the estimated error parameter and guess parameter. According to the cognitive diagnosis method and system provided by the invention, anoptimized cognitive diagnosis model is disclosed, so that the probability of correctly answering projects can be increased along with the increase of attribute quantity tested by projects grasped by the subject, and weak knowledge points and skill point of the subject can be accurately diagnosed.

The invention provides a self-adaptive learning resource recommendation method and system based on a knowledge graph, and the method comprises the steps: building a user cognition diagnosis model and a test question score prediction model, predicting the mastering condition of a user for knowledge points and the scoring condition for uncompleted test questions, and then selecting a first candidate learning resource; a knowledge graph is constructed, knowledge points which are well mastered by the user and weak mastered by the user are respectively positioned in the knowledge graph according to the diagnosis result of the user cognition diagnosis model, and second candidate learning resources are selected; and screening out the optimal learning resource from the candidate learning resources, and recommending the optimal learning resource to the user. According to the method, the cognitive level of the user and the prediction condition of the score of the uncompleted test question by the user are considered, the semantic relation between the knowledge points is considered, the corresponding knowledge graph is constructed, the knowledge points which are well mastered and poorly mastered by the user are positioned in the knowledge graph in combination with the cognitive diagnosis result of the user, and the user experience is improved. And learning resources most suitable for the user are selected and recommended to the user.

The invention relates to a teacher teaching ability manifestation evaluation method based on a knowledge processing mechanism, and belongs to the field of teaching informatization. The method comprises the following steps: S1, constructing an evaluation index system of an acquisition stage, constructing an evaluation index system of a processing stage and constructing an evaluation index system based on an activation stage on the basis of investigating and interviewing a plurality of people; S2, performing weighting on the index system by adopting an index weighting method, and constructing a theoretical framework for student cognitive diagnosis based on a Q matrix theory; S3, compiling a script, operating the script by using multi-surface Rasch model Facets calculation software, revising a scorer, an index system and a sample, and performing cognition judgment on students through an ideal mastering mode; and S4, evaluating the teaching ability of the teacher by using an index system, processing by using computer software spss, performing diagnosis through the cognition of the student, and analyzing the cognition of the student by using flexCDMs.