50 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 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 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 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

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.

The invention discloses a personalized online learning path recommendation method and system based on an improved dwarf weasel optimization algorithm. The method comprises the steps of obtaining multi-dimensional user portraits of learners and multi-modal features of learning resources; based on the multi-dimensional user portraits and the multi-modal features, a multi-criterion fitness function used for evaluating the matching degree of learners and learning resources is constructed; and carrying out global optimization solution on the multi-criterion fitness function by adopting an improved dwarf weasel optimization algorithm, and outputting a recommended personalized learning path sequence. According to the personalized online learning path recommendation method and system based on the improved dwarf weasel optimization algorithm, multi-dimensional features of learners and learning resources are analyzed, a multi-criterion fitness function used for evaluating the matching degree of the learners and the learning resources is constructed, solving is carried out through the dwarf weasel optimization algorithm, and the learning path recommendation accuracy is improved. And more personalized learning paths and resource recommendation meeting requirements are provided for learners.

The invention discloses an education content personalized recommendation system based on deep learning, and relates to the field of education technology and deep learning, and the system comprises a multi-modal data processing module which collects and processes four types of data, and forms a standardized vector; the target disassembling and mapping module is used for disassembling a macroscopic target into microscopic sub-targets and generating a list and a mapping table; the ability and interest modeling module evaluates ability and constructs an interest model; the resource recombination recommendation module screens and pushes combined resources; the feedback iterative optimization module collects feedback, updates data, optimizes parameters and reversely transmits the parameters; according to the method, a personalized learning path is constructed through a multi-modal data processing and target dynamic disassembly technology, and the requirements of learners are accurately evaluated in combination with multi-dimensional capability quantification and interest modeling; and meanwhile, a resource intelligent recombination recommendation mechanism is adopted, a comprehensive and high-timeliness resource list is generated based on an adaptation degree algorithm, and a feedback closed loop is formed to continuously optimize system parameters, so that the learning experience and effect are remarkably improved.

The invention belongs to the technical field of power system employee training, and provides a learning path planning and learning effect evaluation method and system based on a neural network, electronic equipment and a storage medium. The method comprises the steps of data acquisition, coding feature matrix construction, knowledge graph entity embedding generation, vector splicing, coding processing, matching score matrix calculation, curriculum personalized screening, knowledge level evaluation, learning demand analysis, optimal learning path screening and learning effect evaluation. According to the invention, through a two-stage learning path planning process combining actual demands and employee historical data, a learning demand list and a recommended course sequence are enabled to accurately fit employee knowledge shortages and business demands, and the path can be adjusted in real time according to the employee's own learning progress; in addition, due to the integration of the power grid business data, the training content hysteresis is avoided, and the training quality is improved.

The invention relates to the technical field of intelligent data processing, in particular to a hierarchical operation method and system for a knowledge vulnerability probability map, and the method comprises the following steps: obtaining answer data and a knowledge point network, extracting mastering probability distribution, screening key partitions, extracting weights and thresholds, marking vulnerability levels, and analyzing dependence intensity to construct a topological structure. And matching the original behavior extraction consolidation index, evaluating the mastering stability, calculating the lifting value record change amplitude, and generating the knowledge point mastering dynamic evaluation index. According to the method, by combining the knowledge point prior relation and the mastering probability matrix, the knowledge vulnerability recognition precision is improved, the mastering condition of a learner is accurately evaluated, the assignment and priority of job tasks are optimized, the learning progress is accurately adjusted, the personalized learning experience is enhanced, mastering changes are continuously tracked, stability evaluation is improved, and task dynamic optimization is promoted; knowledge vulnerability analysis and job setting are improved, the knowledge fragmentation risk is reduced, and the learning effect and the path integrating degree are improved.

According to the method for evaluating the English multi-knowledge-point cognition degree of the learner, the English multi-knowledge-point cognition degree of the learner can be evaluated according to the answer scores of the learner for practicing multiple knowledge points in English. According to the method for evaluating the cognition degree of the multiple English knowledge points of the learner, the problem that an existing English knowledge point mastering condition diagnosis method lacks accurate evaluation on the cognition degree of the multiple English knowledge points of the learner is solved.

The invention discloses an electric bicycle intrusion behavior simulation method based on reinforcement learning parameter self-adaption, and the method comprises the following steps: obtaining environment parameters, initializing a reinforcement learning agent, and enabling the motion space of the reinforcement learning agent to comprise the hyper-parameter adjustment amount of a social force model; iteratively training the reinforcement learning agent until a convergence condition is reached to obtain a simulation model, acquiring all vehicle states and updating a state space by the reinforcement learning agent in each iteration, then calculating a corresponding action space, and meanwhile, calculating an electric bicycle intrusion decision probability according to the state space, and the social force model calculates the social force of the electric bicycle according to the intrusion decision probability of the electric bicycle after adjusting hyper-parameters in the action space, and updates the state of the electric bicycle. According to the method, through deep fusion of reinforcement learning and a social force model, a complete simulation-evaluation-learning closed-loop optimization framework is constructed to realize parameter adaptation, decision dynamics and multi-scene adaptation.

The invention relates to the technical field of electric vehicle charging facility intelligent control, and discloses a reinforcement learning-based charging pile cluster load balancing optimization method and system. The system comprises a digital twinborn simulation module, a data sensing and processing module, a state characterization module, a double-track decision module, a baseline strategy unit, a shadow learning agent, a safety verification and execution module, a double-path reward calculation module, a shadow experience learning and verification module and an online deployment and continuous learning module. Through the self-evolution system normal form of'evaluation-learning-verification-online 'and the dual security isolation design, the autonomous management system for the charging pile cluster, which can continuously improve the performance on the premise of ensuring absolute security, is successfully constructed, and a replicable solution is provided for engineering application of reinforcement learning in a complex industrial scene.

This invention discloses a multimodal AI-driven adaptive vocational skills training platform, primarily relating to the fields of vocational skills training and adaptive learning. It includes: a multimodal data acquisition unit for collecting multimodal data from trainees; a skills profile construction unit for generating proficiency profiles of trainees across various skills dimensions based on a multimodal fusion model; an adaptive path generation unit for generating personalized training paths based on the skills profiles using reinforcement learning algorithms; a training interaction unit for executing the training paths and collecting interaction data; and a dynamic evaluation and adjustment unit for evaluating learning effectiveness and dynamically adjusting the training paths. The beneficial effects of this invention are: it achieves accurate profiling of trainees' skill levels and real-time dynamic adjustment of training paths, significantly improving the personalization and intelligence of vocational skills training through multimodal data fusion and reinforcement learning.

The invention discloses a personalized training scheme generation and adjustment method based on student learning ability evaluation, and relates to the field of education. The method comprises a parent communication module, a learning ability parent questionnaire module, a learning ability evaluation module, a personalized training scheme generation module and a home-school module. The method comprises the steps of collecting learning ability data of students, evaluating the learning ability, generating a personalized training scheme, feeding back a training effect, adjusting the training scheme and the like. According to the invention, the learning ability of the student can be comprehensively and accurately evaluated, a personalized training scheme is generated, and the learning efficiency of the student is effectively improved.

The system according to the embodiment aims to fairly and efficiently evaluate the thinking ability and judgment ability of a learner. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, an evaluation unit, and a storage unit. The collection unit collects data on learners' note-taking and behavior. The analysis unit analyzes the data collected by the collection unit and evaluates the learners' thinking ability and judgment ability. The evaluation unit evaluates the learners against goals based on the evaluation results obtained by the analysis unit. The storage unit stores the data collected by the collection unit over the long term.