31 results about "Learning gain" patented technology

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 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 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 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.

The development of information technology makes multimedia devices and smart devices widely used in teaching, and using information technology to promote the improvement and innovation of education and teaching has become a new trend. Classroom teaching condition detection and evaluation is an inevitable requirement to improve teaching quality and learning results. Traditional teaching evaluation methods are highly subjective and inefficient, and in the digital information age, big data and artificial intelligence technology should be fully used for classroom teaching condition detection and evaluation. The interaction between teachers and students, the concentration of teachers' teaching and the classroom atmosphere can be judged by detecting the moving distance of teachers. The invention focuses on classroom teaching video, analyzes the teaching behavior of teachers, selects the moving distance of teachers as an index, and researches from the aspects of teacher target detection and tracking, video moving distance measurement, teacher behavior modeling and action recognition. In the teaching video, the teacher's face is recognized, the centroid point is taken, the teacher's face change is positioned and tracked, and the horizontal moving distance of the teacher in the platform area and the vertical moving distance of the teacher in the aisle area are measured online.

The invention discloses a knowledge graph-based student achievement prediction method, which comprises the following steps of: firstly, automatically constructing a course knowledge graph by utilizing a large model, then obtaining behavior data of a user on an MOOC (Model of Oriented Communication) website, and predicting learning achievements of students on different knowledge points according to the behavior data of the students on the different knowledge points. The system can help a teacher to timely find and predict the mastering condition of students on knowledge points, and timely adjust a teaching scheme and emphasis.

This invention discloses a method for urban traffic signal control based on accelerated iterative learning control, characterized by the following steps: Step 1) Establishing a traffic flow model based on iterative learning control; Step 2) Creating a historical iteration information database to build and train a random forest regression model; Step 3) During the iteration process of the traffic flow model based on iterative learning control, using a particle swarm optimization algorithm to optimize the learning gain of the iterative learning control online; Step 4) Saving the iterative data of the traffic flow model based on iterative learning control that meets the conditions to the historical iteration information dataset, and further improving the historical iteration information dataset. This method solves the problems of slow convergence speed and low utilization of historical data in current iterative learning control algorithms applied to urban traffic signal control.

The application discloses an AI large model-based video knowledge automatic dotting answering and mastering evaluation system and belongs to the technical field of AI large models, and comprises a video knowledge element extraction module, a dynamic knowledge graph construction calculation module, a learning path cognitive navigation module, an intelligent evaluation ability portrait generation module, a graph intelligent answering community distillation module and a learning achievement externalization migration guide module. According to the application, the path branch is dynamically adjusted according to the learning behavior, and the content difficulty and the individual ability are accurately matched. The navigation interface converts the abstract knowledge association into intuitive guidance through multimodal interaction such as a visual path graph, a progress bar color mark prompt and a forward-looking risk early warning, and effectively reduces the learning disorientation and cognitive overload risk. The cognitive load theory is deeply integrated, high-complexity node continuous accumulation is actively avoided in path planning, and a just-right cognitive scaffold is provided, so that the learning process is both challenging and maintains a smooth experience, and the learning autonomy is significantly improved.

The invention belongs to but is not limited to the field of educational artificial intelligence and adaptive learning systems, and discloses an adaptive test question recommendation method and system based on double-layer implicit learning state quantization, and the method comprises the steps: constructing a multi-agent closed-loop architecture comprising an Actor, an Evaluator and a Reflector, by combining a knowledge graph enhanced cognitive diagnosis model and a double-layer (belief / authority) state updating mechanism, accurate positioning of a recent development area (ZPD) of a student, dynamic generation of a cognitive scaffold and self-iteration of a teaching strategy are realized, so that the robustness and interpretability of an adaptive learning system and long-term learning gain of the student are remarkably improved, and the learning efficiency is improved. The method is suitable for personalized path planning of an intelligent tutoring system and a large-scale online education platform.

Approaches for a learning management system and a method for implementing targeted learning, is described. In an example, a learning outcome may be selected. In one example, the learning outcome may correspond to obtaining a learning objective. Further, one or more learning activities may be identified corresponding to achieving the learning outcome. In one example, the learning activities are categorized to obtain a target feature, with extracted feature(s) from traditional and traditional sports by applying the science of embodied cognition. Further, each of the plurality of learning activities may be correlated with each of the target features based on a pre-defined criterion based on the learning activity, which leads to achieving the learning objective.

The invention discloses a dynamic material performance prediction method and system based on incremental learning and knowledge distillation, and belongs to the technical field of material science and engineering.The method comprises the steps that a dynamic material encoder generates embedded representation for a material, and the known material is embedded by searching an embedded table; and the newly added material generates a new embedding through the neural network and updates the embedding table. The system adopts an incremental learning mechanism, the model can be updated step by step when the new material is added, the prediction precision of the new material is ensured, and the learning result of the existing data is kept at the same time. Through a knowledge distillation mechanism, performance prediction of similar materials is used as a teacher signal, prediction of a teacher model and a student model is weighted and fused in combination with physical similarity and structural similarity, and the accuracy of performance prediction of new materials is further improved.

Approaches for a learning management system and a method for implementing targeted learning, is described. In an example, a learning outcome may be selected. In one example, the learning outcome may correspond to obtaining a learning objective. Further, one or more learning activities may be identified corresponding to achieving the learning outcome. In one example, the learning activities are categorized to obtain a target feature, with extracted feature(s) from traditional and traditional sports by applying the science of embodied cognition. Further, each of the plurality of learning activities may be correlated with each of the target features based on a pre-defined criterion based on the learning activity, which leads to achieving the learning objective.

The application relates to the technical field of intelligent education pushing, and particularly discloses an online education training intelligent pushing system based on AI adaptive learning, which comprises the following steps: collecting student historical behavior sequence data and learning achievement evaluation data, performing feature coding on the two types of data, and generating a student exclusive cognitive state vector; inputting the vector into a pre-trained adaptive learning model to output a knowledge point mastery prediction value; and searching for course resources according to the prediction value to construct a pushing candidate set. The system quantitatively calculates a teaching unit difficulty coefficient and a knowledge correlation strength, sets a pushing priority according to the calculation result, and completes the pushing. The design can accurately quantify the cognitive state of students, regularize original learning data, enrich sorting and judging dimensions, optimize course arrangement logic, and improve the matching degree of course pushing and student individualized learning features.