66 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 discloses a self-adaptive intelligent teaching content recommendation system, and relates to the technical field of intelligent teaching, and the system comprises a data collection and analysis module which is used for collecting multiple types of data of students in the learning process, including learning behavior data, physiological signals, environment data and learning achievement cognition feedback data of the students, and after collection, sending the data to a database; performing preprocessing of noise reduction, standardization and analysis feature extraction on the multi-type data to generate multi-modal data; according to the method, the three-dimensional knowledge graph of knowledge points, error types and thinking paths is constructed, and the TCN analysis is combined, so that explicit knowledge defects can be identified, implicit knowledge vulnerabilities can be diagnosed, students can accurately know knowledge system vulnerabilities of themselves, and targeted defect checking, leak repairing and intensified training are carried out; learning interest information is extracted from multi-modal data, an explicit and implicit multi-dimensional interest model is established, and teaching content is screened in combination with a knowledge short board diagnosis result.

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 learning achievement prediction method based on learning behavior data mining and a computer device. The method comprises the steps of collecting multi-dimensional learning behavior data of students, performing preprocessing and feature extraction, constructing a structured learning behavior feature set, then constructing a target machine learning model suitable for different prediction tasks, inputting structured learning behavior features of the students to be predicted into the model, and obtaining a continuous cumulative risk prediction curve; converting the cumulative risk prediction curve into discrete classification labels, and generating a classification prediction result with reserved time sequence characteristics; and expanding the structured learning behavior feature set by adopting a pseudo-label semi-supervised learning algorithm, iteratively training a target machine learning model, inputting the structured learning behavior features of a to-be-predicted student into the trained model to obtain a comprehensive prediction result, and generating a visual learning achievement prediction scheme according to a classification prediction result. The method can provide a comprehensive and accurate prediction result.

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 relates to the technical field of education interaction, in particular to a teaching interaction mode switching method and system, and the method comprises the following steps: collecting classroom behavior data, classifying answering time consumption and error distribution, dividing teaching task load levels, recording teaching interaction forms, calling data based on a mapping table, and recognizing task delay and errors. Marking unmatched interaction forms, matching teaching structure types, comparing student speech frequencies, screening group cooperation features, judging teaching task interaction stages, and writing in classroom interaction process instructions. According to the method, more flexible and more personalized adaptation between teaching content and student performance is realized by refining question answering time consumption and error frequency of the students, specific difficulties encountered by the students in the learning process are identified and coped with, teaching interaction forms are adjusted, teaching efficiency and learning results are improved, and when the students are in a knowledge mastering lagging period, the students can learn the knowledge conveniently and quickly. A more suitable teaching structure is recommended, and it is ensured that each student can progress in the most suitable learning environment.

The invention discloses a production and teaching integration practical teaching interaction method and system based on a block chain, and belongs to the technical field of production and teaching integration. The method comprises the following steps: obtaining current data of production and teaching integrated practical teaching, and obtaining a school teaching effect index, an enterprise participation index and a student learning achievement index of current teaching interaction; the practice task completion degree, the knowledge and skill improvement rate and the occupational quality development degree of the current teaching interaction are obtained; and obtaining a teaching quality evaluation index based on the practice task completion degree, the knowledge and skill improvement rate and the occupational quality development degree, and constructing a teaching effect attenuation prediction model. Through real-time monitoring and analysis of multi-dimensional teaching interaction data, the technical effect of comprehensively evaluating the teaching effect of production and teaching integration practice is achieved, and the problems that in the prior art, teaching quality evaluation is single, and real-time feedback is lacked are solved.

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 provides a personalized learning planning analysis method based on beauty teaching, and the method comprises the following steps: collecting multi-dimensional data, and constructing a multi-level comprehensive portrait; constructing a target library and a demand analysis model, obtaining a to-be-improved capability list, and customizing a learning target; designing a path planning algorithm, obtaining and adjusting an optimal path, and generating a personalized learning route map; performing multi-dimensional evaluation on the real-time learning condition data to obtain a learning condition deviation value, and judging whether a dynamic adjustment mechanism is automatically triggered or not; and carrying out acceptance and analysis on the learning achievements to obtain a learning achievement report, and carrying out optimization and adjustment on teacher teaching. According to the invention, the method can deeply analyze the conditions of students, lays a data foundation, achieves the precise customization and planning of learning targets and learning paths, achieves the accurate evaluation of learning conditions, dynamically adjusts teaching strategies, and improves the learning efficiency.

The invention relates to the technical field of educational training management, in particular to a police school educational training management system based on artificial intelligence. The basic exploration module is used for analyzing a basic exploration report of the students on to-be-trained knowledge by utilizing artificial intelligence; when the system is used, a basis is provided for subsequent personalized learning scheme design, so that a targeted learning plan is formulated for a student, and the learning time of the student is reasonably arranged. And the students can gradually improve the learning progress under a proper learning progress. Through continuous middle-term adjustment and stage inspection, the learning content and difficulty are optimized in real time, and the learning effect of students is improved. The learning pertinence and efficiency are improved, the learning achievement of the students is maximized, the knowledge memory depth and learning efficiency of the students are improved, the students are helped to consolidate weak knowledge points, the learning effect is improved, the students always keep high learning participation in the learning process, and the overall training effect is further 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 invention provides a five-axis contour error control method based on variable gain iterative learning. The method comprises the following steps: 1, establishing coordinate systems of a five-axis machine tool; 2, constructing a kinematic model of the machine tool by adopting a D-H parameter method; 3, estimating a contour error by using an arc fitting method, and ensuring the synchronism of a tool nose position contour error and a tool attitude contour error through a pose proportion; step 4, correcting the position and attitude of the cutter based on a variable gain iterative learning control algorithm, and adaptively adjusting control input by optimizing learning gain to reduce contour errors; according to the method, the variable gain iterative learning algorithm is introduced, so that the control precision is optimized, the convergence speed is also improved, and the contour error is more effectively inhibited in a complex and high-precision processing scene.

This application belongs to the field of augmented reality teaching technology, and discloses a method for quickly generating AR teaching materials and related devices. Prepare basic AR teaching materials and extended AR teaching materials in advance, and quantify their data volumes and learning gain scores. According to the natural language feedback of students, evaluate the students' mastery of knowledge points, and generate multiple teaching material generation schemes differently according to the mastery degree. Evaluate the performance of each teaching material generation scheme from two dimensions of bandwidth saving and learning gain improvement. Finally, select the teaching material generation scheme with the best performance, and generate and distribute AR teaching materials for each student accordingly; thus, it is possible to dynamically adjust teaching materials according to student feedback, optimize the utilization of bandwidth resources while ensuring personalized teaching, and achieve the generation and distribution of fast, efficient and high-quality AR teaching materials.

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.

The invention belongs to the technical field of augmented reality teaching, and discloses an AR teaching material rapid generation method and related equipment, basic AR teaching materials and extended AR teaching materials are prepared in advance, data volumes and learning gain scores of the basic AR teaching materials and the extended AR teaching materials are quantified, and according to natural language feedback of students, the mastery degree of the students on knowledge points is evaluated, so that the learning efficiency of the students is improved. The method comprises the following steps: acquiring the mastering degree of students, differentially generating a plurality of teaching material generation schemes according to the mastering degree, evaluating the performance of each teaching material generation scheme from two dimensions of bandwidth saving and learning gain improvement, finally selecting the teaching material generation scheme with the best performance, and generating and distributing AR teaching materials for each student according to the teaching material generation scheme; therefore, the teaching materials can be dynamically adjusted according to the feedback of the students, while personalized teaching is ensured, bandwidth resource utilization is optimized, and rapid, efficient and high-quality AR teaching material generation and distribution are realized.

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 invention discloses a control method for preventing rolling wrinkles of a strip steel plate and related equipment, relates to the field of strip steel rolling, and mainly aims to solve the problem that the strip steel plate is extremely easy to wrinkle in the rolling process at present. The method comprises the steps that the roller convexity of a target rack is adjusted, and the target rack is the rack with the rolling missing rate reaching a preset value; establishing a dynamic roll bending compensation system to determine a segmented compensation strategy; constructing a rolling force-roll bending feedforward control model to determine a rolling force fluctuation compensation gain coefficient; self-learning parameters of the rolling mill are adjusted, so that the roll bending compensation self-learning gain coefficient and the self-learning speed are reduced; the deformation resistance coefficient of the rack is adjusted to restrain middle extension during high-speed rolling; and establishing a roll channeling compensation mechanism after the welding seam passes, so as to execute corresponding roll bending compensation according to the width of the strip steel. The control method is used for preventing rolling wrinkles of the strip steel plate.

The invention relates to the technical field of education, and particularly discloses an AI-based personalized course teaching auxiliary management system, and the system is characterized in that a personalized learning path selection module sets different teaching paths according to different teaching contents, and a student user can select different teaching paths for learning according to needs; the data acquisition module acquires learning data of each student user under different teaching paths, and the AI intelligent analysis module analyzes learning results of each student user under different teaching paths according to the learning data and makes a subsequent learning plan for each student according to the learning results. And meanwhile, the data acquisition module also acquires user parameters of each learning path, and the upgrading resource allocation module allocates upgrading resources to each teaching path according to the user parameters, so that the learning characteristics and requirements of each student can be accurately known, and the learning efficiency and learning experience of the student are greatly 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.