47 results about "Learning gain" patented technology

A transformer-based student performance prediction and reasoning intervention is disclosed. The system comprises a data repository coupled to a transformer-based prediction module that processes student data through multi-head attention mechanisms to generate performance predictions and identify potential learning shortfalls. A reasoning-enhanced large language model algorithmically generates personalized corrective action plans by applying structured decomposition of learning challenges, multi-step reasoning, and hypothesis testing. An algorithmic prompt formulation system optimizes inputs using field-specific, level-specific, and shortfall-specific templates. The system implements a workflow including shortfall detection against educational thresholds, causal factor analysis, intervention generation, and adaptive refinement based on outcomes. This approach enables early identification of academic challenges and timely implementation of personalized interventions to improve student learning outcomes.

The invention provides a student adaptive auxiliary learning method and system based on artificial intelligence, and the method comprises the steps: constructing a subject knowledge graph, and carrying out the correlation and structuralization of knowledge points; the knowledge points are associated with learning resources and test questions in the subject knowledge graph; collecting learning behavior data of students, and constructing student portraits; the student portrait comprises three dimensions of learning style, knowledge level and hobbies and interests; wherein the knowledge level is a mastering probability of each knowledge point acquired according to the subject knowledge graph; personalized learning paths, learning resources and learning strategies are recommended to the students according to the student portraits and the subject knowledge maps; and learning results of the students are automatically evaluated and fed back. According to the characteristics and requirements of each student, a personalized learning scheme is provided, the learning efficiency is improved, the limitation of time and space is broken through, and the students can obtain high-quality learning resources which are more personalized for themselves anytime and anywhere.

A computer-implemented method is disclosed for transforming academic test performance into personalized feedback and learning recommendations. The method involves presenting an academic test to a user via a user interface of an online learning platform and receiving the user's submitted answers. The system accesses input parameters including historical user-performance data, correct answers, and coaching session data. The user's responses are compared with the correct answers to identify incorrect responses. A prompt generator creates a prompt to guide and constrain an AI engine in analyzing the test responses. The AI engine correlates the incorrect responses with historical performance data and coaching session information to detect learning patterns or recurring errors. Based on the identified patterns, the system generates personalized feedback and targeted learning recommendations to address specific learning gaps. The method enables adaptive, AI-assisted post-assessment guidance, improving learning outcomes through individualized support.

The invention discloses a dynamic learning path planning method and system based on a learner portrait. The method comprises the steps of collecting learning behavior data, learning result data and background attribute data of a target scholar based on a plurality of data sources, and obtaining knowledge point mastery degree vector representation, learning style preference and cognitive ability level to construct a multi-dimensional dynamic portrait of the target scholar; generating an initial personalized learning path of the target scholar based on a preset learning target and the multi-dimensional dynamic portrait of the target scholar; learning process data of a target scholar for related learning resources is monitored in real time, a learning effect evaluation report is generated according to the learning process data, a multi-dimensional dynamic portrait is adjusted to trigger a path replanning mechanism to generate a brand new personalized learning path, and the brand new personalized learning path is pushed. The learning path and the current state of the learner are kept optimally matched all the time, and personalized static planning is upgraded to dynamic syndrome guidance.

The invention discloses a teaching evaluation method and system based on artificial intelligence, and relates to the technical field of teaching evaluation, and the method comprises the steps: constructing a student learning analysis time series data set based on an LMS learning management system integration platform; constructing a dynamic student learning behavior evaluation model by using an unsupervised learning algorithm, and generating a student personalized learning portrait; based on the personalized learning portrait of the student, combining the historical score data of the student and the learning progress of the student, utilizing a deep neural network optimization model to realize dynamic real-time prediction and self-adaptive adjustment of the learning state of the student, and generating a continuously updated student learning state evaluation map; and based on the continuously updated student learning state evaluation map, analyzing the relationship between the student learning progress and the teacher teaching effect, dynamically adjusting the student learning strategy and the teaching plan, and generating an artificial intelligence teaching evaluation scheme. The method has the beneficial effects that personalized and scientific teaching evaluation and optimization are realized, and the teaching effect and the learning achievement of students are improved.

The invention discloses a verifiable generation and causal optimization education intelligent system and method oriented to teaching and evaluation integration. The system comprises a data access and learning portrait, a knowledge retrieval and knowledge graph, VGE (Verifiable Generation Environment), consistency / contradictory rate / coverage rate verification, multi-expert agent collaboration and arbitration, causal structure learning and anti-factual optimization, a context protocol and an edge-cloud double-loop scale block. The illusion is reduced through evidence chain binding and thresholding, and the content quality is guaranteed through multi-Agent voting and negative right; optimal intervention is selected based on a causal objective function, and a teaching plan, a calendar and a task list are written back in a closed loop, so that end-to-end optimization of question setting, reading, lecture and teaching intervention is realized, learning gain is improved, and fairness and teacher workload are considered.

The invention discloses a multi-working-condition collaborative operation scheduling method for a wheel bucket excavator, a medium and computer equipment. The method comprises the following steps: acquiring operation data of the wheel bucket excavator and transportation equipment in real time, wherein the operation data comprises an operation equipment state, an operation task emergency degree and a material value; and extracting working condition characteristics such as excavation strength of the wheel bucket excavator under stripping and mining working conditions. An evaluation matrix is constructed by using an improved analytic hierarchy process, a digital twinning model is constructed in combination with data and features, and online learning is performed by using a federated learning framework. And dynamically adjusting task priorities by utilizing learning results, calculating a matching degree, determining a scheduling strategy and scheduling equipment until all tasks are completed. By constructing a multi-source sensing and digital twinborn model and combining a dynamic priority evaluation and self-adaptive task allocation technology, intelligent collaborative scheduling of the wheel bucket excavator under multiple working conditions is realized, so that the working efficiency, the equipment utilization rate and the anti-disturbance capability are improved, and the purposes of reducing energy consumption and guaranteeing production continuity are achieved.

The invention belongs to the technical field of intelligent education, and discloses a personalized learning path intelligent planning system, which comprises a data acquisition module used for acquiring learning activity data, user behavior data and learning result data; the data processing module is used for preprocessing the collected learning activity data, user behavior data and learning achievement data to obtain a learning activity feature data set, a user behavior feature data set and a learning achievement feature data set; the learning fitness prediction module is used for fusing the learning activity feature data set, the user behavior feature data set and the learning achievement feature data set to obtain a comprehensive feature data set; a learning fitness prediction model is trained and constructed according to the comprehensive feature data set, and the learning fitness of the user is obtained through prediction of the learning fitness prediction model; each learner provides a personalized learning path and recommendation, so that learning better meets personal requirements, and the learning effect and efficiency of the learner are improved.

The embodiment of the invention provides a course learning data processing method and device and a storage medium, and aims to timely and accurately calculate a course section learning progress of a target course section in a target course and respond to a configuration operation of a course provider on the target course section in a learning progress statistical information configuration interface. And determining a first learning progress statistical object related to the learning duration and a second learning progress statistical object related to the learning achievement corresponding to the target course section under the plurality of course types. And according to the first learning progress statistical object and the second learning progress statistical object, acquiring learning behavior data generated when the target learning party learns the target course section in different course periods of the target course. And if new learning behavior data is collected, according to the collected learning behavior data, combining contents of a target course section repeatedly learned by a target learning party under various course types in different course periods, determining a first statistical value and a second statistical value corresponding to the first learning progress statistical object and the second learning progress statistical object, and calculating the course section learning progress of the target course section.

The application discloses a kind of self-adapting intelligent teaching content recommendation system, it is related to intelligent teaching technical field, including data acquisition analysis module: collection student in the learning process multiple types of data, including student's learning behavior data, physiological signal, environmental data and learning achievement cognitive feedback data, after acquisition, multiple types of data are preprocessed with noise reduction, standardization and analysis feature extraction, generate multimodal data;The application is analyzed by constructing knowledge point, error type, three-dimensional knowledge graph of thinking path and time sequence convolution network TCN, not only can identify dominant knowledge defects, but also can diagnose implicit knowledge leaks, so that students can accurately know their own knowledge system leaks, targeted to check and fill the gap and strengthen training;By extracting learning interest information from multimodal data, an explicit and implicit multidimensional interest model is established, and the teaching content is filtered in combination with the knowledge gap diagnosis result.

The invention relates to the technical field of agents, and discloses a student behavior analysis method and device, electronic equipment and a storage medium. Simulation data and real data are mapped to a common submerged space through an original encoder, and the difference between a simulation domain classroom state and a real domain classroom state is quantified by using an original discriminator; and the alignment loss between the simulation domain and the real domain is obtained, so that the alignment loss is utilized for training, and the migration capability of the model from the simulation environment to the real environment is improved. An original multi-agent model predicts cognitive conflicts and learning gains of students based on a simulation domain classroom state, and then cooperative training is performed on the multi-agent model, an encoder and a discriminator according to the cognitive conflicts, the learning gains and alignment losses, so that the model balances short-term cognitive conflicts and long-term learning gains. Through constructing the student behavior mapping model, the target action of the teacher agent is determined based on the actual cognitive conflict and the actual classroom state, and the interpretability of the teacher strategy is improved.

The application discloses a multi-dimensional student state knowledge tracking method fusing emotion and forgetting mechanism, relates to the field of intelligent education, and aims at the problems that the existing method ignores the dynamic influence of emotion on forgetting and lacks a learning feedback mechanism.The application proposes a multi-dimensional state collaborative evolution framework.The method firstly constructs an independent forgetting state which is explicitly regulated by emotion and establishes a knowledge state which is inhibited by the forgetting state;secondly, the knowledge learning gain is innovatively introduced into the emotion updating process, realizing deep coupling of the three-dimensional states of knowledge, emotion and forgetting;finally, the states are fused through cross-dimensional attention, and the basic prediction probability is dynamically calibrated by using subjective difficulty and a prediction uncertainty coefficient.The application effectively simulates a real cognitive psychological process, and significantly improves the accuracy, explainability and individualization level of knowledge state tracking.

An object of a system according to an embodiment is to improve the motivation of a learner and respond to individual learning needs.SOLUTION: A system according to an embodiment includes a quest generation unit, a persona generation unit, a feedback provision unit, and an emotion analysis unit. The quest generation unit analyzes the progress status of the learner and generates a quest to be challenged next. The persona generation unit analyzes the learner's profile or learning data based on the quest generated by the quest generation unit and generates an individual persona. The feedback providing unit visualizes the learner's performance based on the persona generated by the persona generation unit and provides feedback in real time. The emotion analysis unit analyzes the emotion of the learner based on the feedback provided by the feedback providing unit and adjusts the learning plan.SELECTED DRAWING: Figure 1

An object of a system according to an embodiment is to efficiently advance learning in an examination study.SOLUTION: A system includes a question setting part, a digitalization part, a plan creation part, a progress management part, and an evaluation part. The question setting unit analyzes the learning progress or the degree of understanding of the user, and sets an optimum question based on the analysis. The digitizing section digitizes the plurality of reference books and makes them accessible to the user when needed. The plan creation unit creates an optimal learning plan based on the user's goal or learning progress. The progress manager manages the user's learning progress in real-time and provides feedback as needed. The evaluation unit evaluates the learning outcome of the user and determines whether to proceed to the next step.SELECTED DRAWING: Figure 1

The present disclosure relates to a system and method for providing a learning curation service using a learning map-linked open badge, wherein a blockchain-based open badge linked to study is simply issued and provided so that a user (learner) can prove the user's qualifications acquired through education, learning, testing, and the like provided by an educational institution (learning institution), thereby ensuring security while eliminating the inconvenience of the user having to visit the educational institution or website thereof to obtain certificates of completion or training certificates and directly submit same to a requesting institution (receiving institution) as is conventionally the case, and thus saving the required time and additional costs.

The application discloses a kind of fusion student learning behavior's score prediction method, system, equipment and storage medium, by analyzing the learning behavior of student, quantification each learning behavior is for the influence of student learning gain, the influence of all learning behavior for learning gain is integrated together, to measure the high-order interaction effect between learning gain under different behavior influences, and update the knowledge state of student, the application can more accurately predict the answer of student Performance, can provide better performance prediction service for online learning system, and then make the online learning system better for student provides personalized learning service, improve the experience of student when using online learning platform.

The invention discloses a multi-modal preference-driven graph convolution combinatorial optimization learning path generation method, which comprises the following steps: acquiring multi-modal learning feature data, and carrying out multi-modal feature data fusion processing to obtain a learning resource initial feature matrix; a multi-relation adjacent matrix is constructed, normalization processing is carried out, and a normalized propagation matrix is constructed; updating the convolution features of the graph, and obtaining a learning gain score; calculating a comprehensive utility based on the learning gain score; integer programming is established under the condition of considering multiple constraints, and optimized candidate resources are obtained through screening; an edge cost function and a learning path objective function are adopted to generate an optimal learning path in the optimized candidate resources; in the optimal learning path generation process, dynamic re-planning is adopted; the dynamic re-planning comprises grasp updating, coverage demand decreasing and duration budget updating. By means of the scheme, the method has the advantages of being simple in logic, reliable in multi-mode fusion and the like.

The invention relates to the technical field of learning behavior data prediction, in particular to an educational resource dynamic recommendation method and system based on multi-dimensional data analysis. Learning behavior data (interest, ability and the like), task completion condition data and auxiliary resource use data of a learner are comprehensively collected, and a multi-dimensional feature system is constructed. The auxiliary resource demand degree of the course is dynamically calculated based on the difference between the behavior data numerical characteristics and the task completion condition data, and the problem of disjunction between recommendation and learning states is solved; furthermore, in combination with the trend difference and numerical value characteristics of the use data of the auxiliary resources and the task completion condition data, the effect contribution degree of the auxiliary resources to the course is quantified, and the supporting ability of the auxiliary resources to learning achievements is objectively reflected. Finally, the demand degree and the effect contribution degree are fused, auxiliary resources with high demands and good effects are preferentially recommended, static recommendation lag is avoided, and the resource utilization efficiency and recommendation accuracy are improved.

The application discloses a science hierarchical learning and external incentive talent selection system, which comprises a hierarchical learning module, a learning collection module, an evaluation module, an incentive triggering module and a reward docking module. The system divides science knowledge points according to difficulty, collects learning task completion conditions and answer quality data, and comprehensively evaluates the data, and issues virtual rewards in existing popular games to users according to learning achievements. The virtual rewards are existing items in the game that can be obtained through tasks or payment, and the issuance qualification is determined by the learning achievements. The application can effectively improve the learning motivation of users, realize process learning evaluation and talent selection, and is suitable for science education auxiliary and ability evaluation scenes.

The invention discloses a motor control system based on self-learning gain, which comprises a super-spiral sliding-mode observer of the self-learning gain, by monitoring current observation errors in real time and dynamically adjusting the sliding-mode gain, the sliding-mode gain is rapidly increased when the system is disturbed so as to enhance robustness, and is automatically reduced when the system tends to be stable so as to suppress buffeting; therefore, high-precision and high-robustness rotor position and rotating speed estimation is realized.

This invention relates to the field of educational informatization and intelligent teaching technology, specifically to a generative AI-enabled method for dynamically generating computer science classroom teaching resources. This method acquires teaching objectives and content to construct a knowledge graph; collects classroom data, generates learning gains and emotional representations within a time window, forms a sequence through confidence assessment and denoising, calculates the emotional core coupling index, and gates the core weights; updates the core and emotional weights based on the learning gains and emotional sequence, and applies stability constraints; generates teaching resources and adjusts their order according to emotional weights; combines learning and resource usage to generate evaluations, updates objectives or parameters, and achieves iterative optimization; updates the graph weights through a dual-channel approach of learning gains / emotions, coupling gating, and stability constraints, suppressing the "emotional injection" pollution of core edge weights by interaction heat; and generates resources based on the graph and adjusts their order according to emotional weights, achieving immediate response without deviating from the main course line.

This invention proposes a deep learning-based method for predicting student learning outcomes. First, it collects student learning behavior data, evaluation data, interaction data, and attribute data through a compliant authorization mechanism, generating standardized data after anonymization and preprocessing. Second, it extracts behavioral feature vectors, evaluation feature vectors, interaction feature vectors, and attribute feature vectors using bidirectional LSTM, fully connected networks, a pre-trained language model (BERT), and an embedding network, respectively. Finally, it generates prediction results through an outcome prediction model, which includes a feature fusion layer, a main feature extraction layer, a memory enhancement layer, an attention decision layer, and an output layer. This invention achieves accurate prediction of student learning outcomes through multi-source data fusion and external memory enhancement mechanisms, while ensuring compliance and privacy security in the data processing process.

The application relates to the technical field of automatic control, in particular to a high-order iterative learning control method for saturation disturbance of a variable-length nonlinear system, which comprises the following steps: for a discrete-time affine nonlinear system simultaneously affected by input saturation, variable iteration length, random initial state offset and external disturbance, Bernoulli random variables are introduced to describe the variable iteration length, an extended control input sequence uniform time scale is constructed, and a modified tracking error is defined; a high-order feedforward and instantaneous feedback compound control law with dynamic time-varying characteristics is constructed: the high-order feedforward synthesizes the saturation extended control input and the modified tracking error of multiple historical iterations, and a saturation residual memory mechanism is introduced for adaptive anti-integral saturation compensation; the feedback term utilizes the current iteration error to suppress local fluctuations; a cooperative convergence condition of a time-varying learning gain matrix is given, the mathematical expectation of a tracking error is proved to converge to a bounded region related to the disturbance amplitude and the initial state offset; and under ideal conditions, the tracking error asymptotically converges to zero.

This invention relates to the field of course management technology, and more particularly to a smart course management system and method, comprising: a data acquisition module for real-time acquisition of multimodal learning behavior data during the learning process; a teaching resource management module for storing and managing multi-source teaching resources and constructing a unified index map of teaching resources; a cognitive state triggering module for generating cognitive state modeling instructions; a cognitive state modeling module for constructing a dynamic knowledge graph and sending recommendation update instructions; an adaptive learning recommendation module for generating candidate knowledge point sequences based on the dynamic knowledge graph, filtering and matching resource types to generate recommendation results; and a negative feedback adaptive module for calculating the deviation rate between actual and expected learning gains, determining the source of deviation, and generating adjustment instructions. This invention improves the accuracy and adaptability of learning recommendations, and enhances course management efficiency and learning effectiveness.

The application belongs to the technical field of numerical control, and discloses a double-target learning type thrust fluctuation suppression method for a permanent magnet synchronous linear motor, which comprises the following steps: determining an initial range of iterative learning gain according to a system model and a convergence condition; taking the average of multiple peak values of position tracking error and the standard deviation of speed fluctuation as double targets, and optimizing to obtain an optimal gain matrix; constructing a speed and acceleration feedforward controller to reduce the initial error; collecting the tracking error of the current iteration period, inputting the learning law containing a forgetting factor and a zero-phase filter after data alignment, calculating and storing the compensation signal of the next period; and finally implementing online compensation in the form of current feedforward. The application improves the iterative convergence efficiency through feedforward pre-compensation, suppresses the noise influence through a zero-phase filter, and simultaneously suppresses the position tracking error and speed fluctuation through double-target optimization, thereby significantly improving the dynamic accuracy and running stability of the linear motor feeding system.

The invention provides a learning assistance system based on artificial intelligence. The learning assistance system comprises a client and a server, the client is used for inputting personalized data; the data input mode comprises voice input, video shooting and manual typing; the server comprises an artificial intelligence analysis unit and a learning suggestion and plan output unit; analyzing the collected data by using a machine learning algorithm, cleaning the data, describing the data, checking the distribution of the data, comparing the relationship between the data, cultivating the intuition of the data, summarizing the data, and deducing the learning habit and the understanding degree of some courses; on the basis of the knowledge graph, personalized learning resources such as courses, videos and articles are intelligently recommended according to learning characteristics and requirements of students; the system is superior to manual tutoring, and does not have the problems of level difference, emotion difference, standing prejudice and the like like manual tutoring, and does not have a single function or a basic function like existing common learning tools; the system can deeply supervise the learning process, formulate a personalized learning plan and the like, and comprehensively manage the final learning result.

A system is provided.SOLUTION: A system comprising: means for optimizing generated learning content based on individual learning progress and comprehension; means for delivering the learning content to a terminal for display; means for displaying the learning content; means for collecting learning outcomes from the terminal; means for analyzing the learning outcomes and generating new learning content; means for collecting health data and optimizing a learning plan; means for providing learning content based on the health data; and means for providing a dashboard that visually displays learning progress and comprehension for a teacher.SELECTED DRAWING: Figure 1

The invention relates to the technical field of online education, and discloses an online course intelligent auxiliary platform based on data analysis, and the platform comprises a user data collection unit which is used for obtaining login data, content interaction data and evaluation exercise data of a user; the user state analysis unit is used for judging the learning state of the user according to the login data and the content interaction data; the learning content analysis unit is used for judging the learning completion degree of the user according to the learning state of the user and the content interaction data; and the learning assisting module is used for pushing supplementary learning content according to the learning completion degree of the user and the evaluation exercise data. The login data and the content interaction data of the user are obtained to judge the learning state and the learning completion degree of the user, the learning result of the user can be judged more accurately and comprehensively, meanwhile, the defects existing in the learning process of the user can be overcome in the mode of pushing and supplementing the learning content, and the learning efficiency of the user is improved. And the learning effect of the user is improved on the whole.

The present application relates to the technical field of learning behavior data prediction, in particular to an education resource dynamic recommendation method and system based on multi-dimensional data analysis. Learning behavior data (interest, ability, etc.), task completion data and auxiliary resource usage data of learners are comprehensively collected to construct a multi-dimensional feature system. Based on the difference between the numerical characteristics of the behavior data and the task completion data, the auxiliary resource demand degree of the course is dynamically calculated to solve the problem of disconnection between recommendation and learning state. Further, by combining the trend difference and numerical characteristics of the auxiliary resource usage data and the task completion data, the effect contribution degree of the auxiliary resource to the course is quantified to objectively reflect its support ability to the learning achievement. Finally, by fusing the demand degree and the effect contribution degree, the auxiliary resource with high demand and good effect is preferentially recommended to avoid static recommendation lag and improve resource utilization efficiency and recommendation accuracy.