86 results about "Evaluative learning" patented technology

The invention relates to the technical field of online teaching management, and discloses an online teaching optimization method and system based on emotion recognition, and the method comprises the steps: collecting the real-time emotion data of a learner through a multi-modal sensor, carrying out the fusion of a graph convolutional neural network to generate a feature vector, and optimizing a teaching strategy parameter through a meta-reinforcement learning model, the dynamic course generation model combines an improved genetic algorithm and a knowledge graph to optimize a teaching content sequence, and the hierarchical teaching control model realizes knowledge path planning, interactive adjustment and learning state evaluation, and generates a teaching control instruction. According to the method, personalized online teaching optimization is realized, the state of a learner can be accurately grasped, the teaching interaction mode is optimized, the course content is dynamically adjusted, the learning effect is comprehensively and accurately evaluated, the problems that traditional online teaching lacks personalization, the state of the learner is difficult to grasp and the like are effectively solved, the online teaching quality and the learning experience are improved, and the learning experience is improved. And the online education development is promoted.

The invention discloses an online resource adaptive recommendation method based on multi-modal learning behavior analysis, and relates to the technical field of resource recommendation. The method comprises the following steps: firstly, dynamically collecting multi-modal data by using a heterogeneous sensor array, and carrying out noise reduction, probability distribution matching normalization and time-space alignment preprocessing; features are extracted through a hierarchical network, modeling learning behaviors such as a variational auto-encoder are combined, and the learning state is evaluated from multiple dimensions; recommendation decisions are generated based on reinforcement learning, recommendation is optimized in combination with personalized presentation and multi-source feedback analysis, meanwhile, the system has the functions of dynamic strategy adjustment, intelligent resource creation, cross-scene migration recommendation and the like, and accurate self-adaptive recommendation is achieved. According to the method, multi-modal data are comprehensively collected and deeply processed, learning behaviors and evaluation states are accurately analyzed, personalized resource recommendation is provided through intelligent recommendation and dynamic optimization strategies, recommendation accuracy and learning effects can be improved, user experience can be enhanced, and the utilization rate and competitiveness of platform resources can be improved.

The invention relates to the technical field of personalized pushing, in particular to a personalized music teaching content pushing system. The system comprises a user ability evaluation module, a teaching content matching module, a learning path planning module, a push strategy optimization module, a behavior analysis and prediction module, a teaching effect feedback module, a dynamic adjustment and optimization module and a personalized recommendation module. According to the invention, a clustering algorithm, a support vector machine, a random forest algorithm, accurate assessment of learner ability, K-means clustering and an analytic hierarchy process are adopted, teaching content and ability matching, a Dijkstra algorithm and dynamic planning are realized, a personalized learning path, a Q learning and strategy gradient method are designed, and a push strategy, a long and short term memory network and a seasonal decomposition time sequence are optimized. Learning requirements are predicted, the project reaction theory, the Markov decision process and the Bayesian network are utilized, feedback adjustment precision is improved, deep Q network and Monte Carlo tree search are carried out, personalized recommendation is optimized, and teaching efficiency and achievements are enhanced.

The invention discloses a personalized learning system based on multiple agents and retrieval enhancement generation. The system comprises a knowledge extraction agent, a retrieval enhancement generation module, a planning teaching agent, a knowledge consolidation agent and a test evaluation agent. The knowledge extraction agent extracts document information from the original learning materials and converts the document information into semi-structured data; the retrieval enhancement generation module is used for storing the semi-structured data into a vector library in a vector form, retrieving information in the vector library according to a request sent by a user, and inputting enhancement information combined by the retrieval information and the request into a downstream agent; and the planning teaching agent, the knowledge consolidation agent and the test evaluation agent are respectively used for constructing a knowledge graph according to the received information to generate a personalized teaching plan, generating exercise questions to consolidate learned knowledge, evaluating a learning effect and providing feedback suggestions. The learning efficiency, the learning effect and the learning experience of the learner can be remarkably improved, and personalized learning can be met.

The invention relates to the technical field of education, in particular to an English personalized learning recommendation method based on big data, which comprises the following steps: collecting original data of English homework completion speed and answer accuracy of a learner, calculating average completion time and accuracy by using a statistical analysis method, identifying the deviation between learning ability and interest points, and recommending the learning ability to the learner. And obtaining a learning ability evaluation result. According to the invention, through predicting learning content demands, not only is the progress of a learner captured, but also future demands can be predicted, so that education resources are prepared in advance, prospective matching of teaching contents is realized, and through optimizing a teaching material sequence and contents, high matching of teaching materials and personalized learning demands is ensured; the use efficiency of educational resources and the maximization of the learning effect are remarkably improved, the learning path is dynamically adjusted, the learning adaptability is evaluated, the learning process is optimized, the flexible application of course content is enhanced, and more personalized learning experience and higher learning achievement are achieved.

The invention relates to the technical field of computers, and discloses a personalized art learning path generation system and an implementation method thereof. The system comprises a real-time behavior acquisition module, a standard feature library module, a behavior feature extraction module and the like. The method comprises the steps of collecting data such as student creation behaviors and work progress in real time, extracting creation style feature vectors, evaluating learning stage coefficients, generating an initial recommendation path, combining various parameters and feedback information, dynamically adjusting path node weights by utilizing a reinforcement learning algorithm, and generating a personalized learning path. Meanwhile, a media presentation form of a learning path is optimized according to multi-modal interaction data, and path parameters are iteratively updated according to learning feedback information. According to the method, the art learning efficiency can be improved, the personalized learning requirements of students are met, the learning experience is optimized, teaching resources are reasonably utilized, and an efficient and personalized learning path generation scheme is provided for art education.

To provide a learning model evaluation support device and a learning model evaluation support program which allow for easily evaluating a characteristic of a learning model.SOLUTION: A learning model evaluation support device 100 includes a model information display unit 110, a model selection unit 120, and an output display unit 150. The model information display unit 110 selectively displays a plurality of learning models which are stored in a prescribed storage area and share an interface, on a display device 260. The model selection unit 120 selects at least one of the plurality of learning models displayed on the display device 260. The output display unit 150 displays an output result from at least one learning model on the display device 260.SELECTED DRAWING: Figure 17

The invention discloses a 3D holographic image knowledge base interactive playing method for diabetic education, and the method comprises the steps: firstly obtaining patient data of a hospital HIS system and a wearable device, and carrying out the classification, preprocessing and data fusion, thereby obtaining an encrypted standardized data set; and then multi-dimensional feature groups are extracted, knowledge base education contents are matched, and a personalized scheme is generated. And carrying out structural processing on the scheme, constructing a 3D model and a scene resource library, rendering the scheme into a holographic image format, optimizing the scheme, and outputting a material set. And then configuring a calibration material set, scheduling and playing, realizing immersive learning, and generating interaction data and feedback information. And finally, evaluating a learning effect by combining clinical indexes, and dynamically updating and optimizing the knowledge base. By means of 3D holography and multi-mode interaction, intuition, interactivity and individuation of diabetic education are improved, and the self-management ability is enhanced.

The invention discloses a knowledge graph-based learning path analysis method, device and equipment and a medium, and relates to the field of online learning platform analysis, and the method comprises the steps: determining a knowledge behavior event and a learning behavior event according to user operation information and a knowledge graph of an online learning platform, thereby determining structured user operation record data, determining a knowledge point learning sequence and a learning behavior sequence by adopting a timestamp sequence analysis method so as to determine a learning path basic sequence; extracting behavior pattern features according to the learning behavior event; according to the relationship among the nodes in the knowledge graph, a hidden Markov model is adopted to calculate the knowledge correlation degree of the learning path; and determining a learning path representation model according to the behavior pattern characteristics, the learning path knowledge association degree and the learning path basic sequence, so that the learning behavior pattern of the user can be combined with the knowledge point learning sequence, and the association degree between the learning behavior and the knowledge points and the association degree between the knowledge points before and after can be evaluated.

The invention discloses an AI learning method based on learning passwords and application thereof, and relates to the technical field of AI learning methods, the AI learning method comprises the steps of learning password generation analysis, personalized student plan making and learning task execution feedback, the learning password generation analysis generates learning passwords through learner behaviors and academic expressions, and the learning passwords are generated through learning task execution feedback; the evaluation module is used for evaluating the familiarity of learners to different knowledge points and the overall mastering level of a subject; the learning password generation and analysis comprises data collection, learning password generation and learning password analysis. By designing learning password generation analysis, a corresponding generation function of a personalized learning password is realized, the problem that a traditional learning analysis method cannot accurately diagnose strong and weak items of students in a personalized manner is solved, a basis is provided for formulating a targeted learning plan, a comprehensive subject ability index is provided for the students, and the learning efficiency of the students is improved. And the learning experience of students is greatly improved.

The invention discloses a learning resource pushing method based on an interactive language model, and the method comprises the steps: obtaining learning interaction data of a learner, carrying out the semantic analysis of the learning interaction data based on a large language model, and extracting key features; performing feature enhancement on the answer text information and the knowledge point text information of the key features, constructing a knowledge state diagnosis model based on the enhanced features, and predicting the knowledge state of the learner through a forgetting attenuation coefficient matrix of the knowledge state diagnosis model; and performing feature alignment and mutual matching on the learner knowledge state and the knowledge point label of the learning interaction data, and extracting learning resource text content according to a matching result and pushing the learning resource text content. The problem that the state of a learner cannot be accurately evaluated in real time and learning resources cannot be individually matched in a traditional education mode can be solved.

To provide a system.SOLUTION: A system includes: means for learning words and actions, action guidelines, a life philosophy, and past books of a specific person; means for generating a video and voice in which the learned specific person is talking with respect to an input question or consultation; means for providing the generated video and voice to inside or outside; means for preprocessing learned data; means for causing a deep learning model to learn using the preprocessed data; means for evaluating the performance of the learned model; and means for generating a response to the question or consultation from a user. The means for generating the response generates a response using a generative AI model on the basis of a prompt sentence in which a feeling state of the user and thinking of the specific person are combined.SELECTED DRAWING: Figure 1

The invention provides a data processing method and device, and relates to the technical field of education. According to the specific implementation scheme, an education strategy is generated based on a two-person game model and teaching task data; the education strategy is input into an education subject model pre-constructed for an education subject, an action sequence output by the education subject model is obtained, and the education subject model is used for representing the corresponding relation between the education strategy and the action sequence; the action sequence and the teaching task data are input into a learning subject model pre-constructed for a learning subject, a learning strategy output by the learning subject model is obtained, and the learning subject model is used for representing the corresponding relation between the action sequence and the learning strategy and between the teaching task data and the learning strategy; inputting the learning strategy into a pre-constructed main body state conversion model to obtain a current main body state output by the main body state conversion model; and based on the current subject state, evaluating the performance of the learning subject model on the teaching task data to obtain performance data.

The invention relates to the technical field of personalized education platforms, in particular to an artificial intelligence-based personalized education platform and method.The artificial intelligence-based personalized education platform comprises a storage module, an information acquisition module, a target generation module, a scheme making module and a display module, and the scheme making module is used for generating an interested knowledge point sequence according to interested knowledge points set by a user and displaying the interested knowledge point sequence; interested knowledge points in the interested knowledge point sequence are divided into a plurality of preset sub-learning points, and an interest value judgment unit is arranged in the scheme making module; the display module is connected with the target generation module, the display module comprises a plurality of small screens, a plurality of preset sub-learning points are displayed through the small screens, the characteristics of each learner can be accurately grasped, and customized learning content and paths are provided; the learning effect can be evaluated in real time, and targeted feedback and guidance can be given in time; the learning progress and content can be dynamically adjusted according to the performance and state of the learner; and humanized learning experience is provided.

To provide a learning material evaluation system, a method, and a program capable of automatically evaluating learning materials.SOLUTION: A learning material evaluation system according to the present invention comprises: acquisition means for acquiring text information indicating learning materials; extraction means for extracting keyword information indicating words and related information relating to the words based on the text information acquired by the acquisition means; and output means for inputting the keyword information and related information extracted by the extraction means into a base model and outputting evaluation information indicating an evaluation of the learning material.SELECTED DRAWING: Figure 3

The invention discloses a user learning quality evaluation method and system based on education platform data analysis, and relates to the technical field of data analysis, and the method comprises the steps: constructing a dynamic knowledge graph sequence based on the overall learning process of a user on an education platform, analyzing the abnormal degree of a user learning path, and calculating a path behavior abnormal value; a path embedding vector is extracted through a neural network graph embedding method, the stability of a learning track is analyzed, and a convergence abnormal value is calculated; and judging whether the user learning quality evaluation result has fraud or not by combining the two. And if the fraud exists, performing exception identification on all paths, distinguishing an abnormal path from a normal path, and re-evaluating the learning quality based on the normal path. According to the method, fraudulent behaviors are effectively recognized and corrected through the dynamic knowledge graph, the deviation of learning quality evaluation is reduced, the judgment accuracy of an education platform on the real learning condition of a user is improved, and misguidance on the teaching intervention effect is reduced.

An object of a system according to an embodiment is to provide high-quality learning support in response to individual learning needs of students.SOLUTION: A system according to an embodiment includes a question reception unit, an advice generation unit, an instruction generation unit, an effect measurement unit, a model answer generation unit, and a visual information generation unit. The question reception unit receives a question from a student. The advice generation unit generates advice based on the question received by the question reception unit. The guidance generation unit generates learning guidance based on the advice generated by the advice generation unit. The effect measurement unit measures the effect based on the instruction generated by the instruction generation unit. The model answer generation unit generates a model answer based on the effect measured by the effect measurement unit. The visual information generation unit generates visual information based on the model answer generated by the model answer generation unit.SELECTED DRAWING: Figure 1

To provide an evaluation system, an evaluation method and a control program capable of quantitatively evaluating the effect of learning.SOLUTION: The evaluation system evaluates the effect of learning. At each time point before and after the learning, questions related to the content of the learning are asked to the subject of the learning. The evaluation system includes an acquisition unit, an evaluation unit, and an output unit. The acquisition unit acquires natural language data indicating an answer to a question. The evaluation unit evaluates the answer to the question by performing natural language processing on the natural language data. The output unit outputs an evaluation result of an answer to a question asked at each time point.SELECTED DRAWING: Figure 1

The system according to this embodiment aims to provide employees with incentives for reskilling and acquiring new skills. [Solution] The system according to the embodiment comprises a learning unit, a posting unit, an evaluation unit, and a distribution unit. The learning unit is where employees take AI learning courses. The posting unit is where employees post new content. The evaluation unit evaluates the data provided by the learning unit and the posting unit. The distribution unit distributes rewards based on the evaluation results from the evaluation unit.

To correctly evaluate a learning improvement level of a student in a learning support device, a learning support method, and a program.SOLUTION: A learning support server (100) includes: a past learning state acquisition unit (111, step S1) which acquires a past learning state of a past organization to which a student had belonged to; a current learning evaluation acquisition unit (112, step S2) which acquires current learning evaluation of the student in a current organization to which the student belongs to; and a learning improvement level evaluation unit (113, step S3) which evaluates the learning improvement level on the basis of the past learning state and the current learning evaluation.SELECTED DRAWING: Figure 3

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 invention relates to the technical field of learning and memory ability evaluation, and provides a mouse hippocampus learning and memory ability comprehensive evaluation method and system based on a Morris water maze, the method is different from a traditional method for evaluating learning and memory only through an incubation period, a mouse behavior character stability and swimming skill dual evaluation mechanism is innovatively introduced, and the method is suitable for comprehensive evaluation of learning and memory ability of a mouse hippocampus. And in combination with a training adaptation period, behavior path analysis and weighted scoring of multi-dimensional behavior indexes, precise quantification of the hippocampus dependency space learning and memory ability is realized. The experimental indexes comprise swimming speed, path length, target quadrant residence time and incubation period, and performing standardized calculation after weight assignment to generate a memory ability evaluation score. According to the invention, the scientificity, stability and experimental repeatability of an evaluation result are improved, and the method is suitable for mouse cognitive function evaluation under various research situations such as drug intervention, neurodegenerative disease models and the like.

A system is provided.SOLUTION: A system comprising: means for inputting homework content by a learner; means for analyzing the input homework content and classifying the homework content into a specific learning category; means for generating a game-form question according to the classified learning category and transmitting the game-form question to a terminal; means for displaying the game-form question received by the terminal and allowing the learner to interactively answer the question; means for evaluating the learner's answer and presenting a result; and means for recording the learner's progress and managing a learning history.SELECTED DRAWING: Figure 1

The invention provides a teaching quality continuous improvement system and method, and belongs to the technical field of data driving, and the system comprises a teaching data access module which is used for achieving the breaking of a data island, and achieving the unified integration of multi-source heterogeneous data; the data development treatment module is used for constructing a data warehouse, constructing a comprehensive, scientific and systematic teaching evaluation system and comprehensively evaluating the teaching effect; the teaching data sharing module is used for supporting unified management and publishing of data services; the teaching replay evaluation module is used for identifying problems and improvement points through a four-force model; and the enabling teaching improvement module formulates targeted improvement measures and continuously optimizes teaching strategies. According to the invention, the learning output is intelligently and repeatedly evaluated through a four-force model, the teaching quality is enabled to be continuously improved, and the learning efficiency and effect are improved; by correctly understanding and applying data, it can be ensured that teaching reform proceeds along a correct track, and a practical and effective method is adopted to systematically plan courses and execute teaching management.

The invention discloses a foreign Chinese education guidance system based on natural language processing, which relates to the technical field of education and comprises an input module, a natural language processing module, a knowledge database, a learning state analysis module, a teaching strategy generation module, a teaching content pushing module and an output module. The system generates personalized teaching strategies according to specific conditions of learners, provides personalized learning paths and teaching contents according to Chinese basis, learning objectives and learning styles of different learners, integrates rich resources such as Chinese vocabularies, grammar rules, cultural knowledge, example sentences and exercise questions, intelligently recommends according to the conditions of the learners, and improves the learning efficiency. According to the method, the utilization efficiency of teaching resources is improved, the learning behaviors and achievements of learners are tracked in real time, the learning progress and the Chinese language ability are comprehensively evaluated and presented in a visual mode, the interactivity and adaptability of teaching are enhanced, in addition, multi-modal teaching is achieved, and richer and more vivid learning experience is provided for the learners.

The invention discloses an automobile maintenance teaching system combining virtual simulation and an intelligent game. The system comprises a user terminal, a server, a database, a virtual simulation module and an intelligent game module. When a student selects a learning mode, the server receives a learning instruction of the user terminal, calls corresponding learning data from the database, calls the virtual simulation module to generate an automobile maintenance virtual teaching scene, and performs task teaching through the user terminal; when a student selects a game mode, the server receives a game instruction of the user terminal and calls the intelligent game module to generate a corresponding AI opponent, meanwhile, the virtual simulation module is called to generate an automobile maintenance virtual competition scene, competition type teaching is carried out through the user terminal to obtain a single task score, and total points are issued to the student. The virtual simulation technology and the intelligent game are combined, personalized teaching is achieved by enhancing interactivity, the learning enthusiasm and motivation of students are improved, and the learning condition is comprehensively evaluated.

The present disclosure provides a data processing method and apparatus, relating to the field of educational technology. The specific implementation solution is as follows: Based on a two-player game model and teaching task data, an educational strategy is generated; the educational strategy is input into an educational entity model pre-constructed for an educational entity to obtain an action sequence output by the educational entity model, where the educational entity model is used to represent the correspondence between the educational strategy and the action sequence; the action sequence and the teaching task data are input into a learning entity model pre-constructed for a learning entity to obtain a learning strategy output by the learning entity model, where the learning entity model is used to represent the correspondence between the action sequence and the teaching task data and the learning strategy; the learning strategy is input into a pre-constructed entity state transformation model to obtain the current entity state output by the entity state transformation model; based on the current entity state, the performance of the learning entity model on the teaching task data is evaluated to obtain performance data.

Provide a system. 【Solution means】 Means for receiving students' learning data in real time and analyzing the data using a machine learning model for evaluating the learning progress, Means for automatically generating an optimized learning plan based on the students' understanding level, Means for providing an interactive teaching material according to the generated learning plan and giving feedback, Means for visualizing the learning progress to the instructor via a dashboard, Means for providing an online community function for promoting communication between educators and students, Means for providing individualized teaching materials to students in real time using a learning support robot, Means for recognizing students' expressions and voices to infer the understanding level and presenting appropriate teaching materials, A system including the above.

The invention relates to the technical field of computers, in particular to a cross-subject learning fusion method and device, computing equipment and a storage medium, and the method comprises the steps: extracting knowledge points of all subjects to construct an association relationship between the knowledge points of the subjects, calculating an optimal learning sequence according to the association strength between the knowledge points, and obtaining interaction behavior data. And calculating a cognitive state, detecting a learning bottleneck, and recommending associated subjects to assist learning at the corresponding learning bottleneck so as to evaluate a learning effect based on knowledge, thinking and application dimensions. According to the method, knowledge association between subjects is constructed, cognitive evaluation is realized through pure behavior data analysis, a dynamic interdisciplinary knowledge connection mechanism is established on the premise that biological feature collection is not needed, the cross-subject transfer learning ability of learners is improved, and a safer and more universal intelligent learning solution is provided for the education field.

The present invention relates to the field of personalized push technology, specifically a personalized music teaching content push system, the system including a user ability assessment module, a teaching content matching module, a learning path planning module, a push strategy optimization module, a behavior analysis and prediction module, a teaching effect feedback module, a dynamic adjustment and optimization module, and a personalized recommendation module. The present invention adopts clustering algorithms, support vector machines, and random forest algorithms to accurately assess learner abilities, K-means clustering and hierarchical analysis method to achieve matching between teaching content and abilities, Dijkstra algorithm and dynamic programming to design personalized learning paths, Q learning and policy gradient method to optimize push strategies, long short-term memory networks and seasonal decomposition time series to predict learning needs, and utilizes item response theory, Markov decision process and Bayesian network to improve feedback adjustment accuracy. Deep Q network and Monte Carlo tree search are used to optimize personalized recommendations and enhance teaching efficiency and results.