579 results about "Learning behavior" patented technology

The invention relates to the technical field of online network teaching, and discloses an online network teaching practice method based on a virtual technology, and the method comprises the steps: obtaining virtual scene data, and carrying out the integration and building of a dynamic knowledge graph and a multi-modal teaching resource library; learning behavior features are extracted according to the cognitive features of the learner, knowledge difficulty levels are divided, and immersive learning is guided; constructing a cognitive evaluation model, optimizing a learning path through hierarchical reinforcement learning, and generating a self-adaptive teaching instruction; collecting interaction track data to generate an operation thermodynamic diagram, and evaluating the skill mastering degree; complexity parameters of the virtual scene are dynamically adjusted, a virtual-real linkage experiment simulation environment is constructed, and skill training closed-loop feedback is achieved. The invention further relates to specific implementation modes of resource integration, cognitive feature extraction, learning path optimization and the like. According to the method, the personalized and immersive experience and skill training effect of online teaching are improved, and the method is suitable for the field of online network teaching.

The invention discloses a multi-agent collaborative reasoning system for intelligent teaching intervention, and relates to the technical field of artificial intelligence and education, and the system comprises a data collection and convergence module which collects data of learning behaviors, emotional states, academic scores and knowledge point mastering conditions of students by means of a classroom behavior analysis system, a camera, a microphone and a learning management system; according to the intelligent teaching intervention-oriented multi-agent collaborative reasoning system, the precision and individuation of teaching intervention are realized by constructing the intelligent teaching intervention-oriented multi-agent collaborative reasoning system, the system collects multi-dimensional data of students through the data acquisition and convergence module, and through the processing of the data conversion and cleaning module, the accuracy and availability of the data are ensured; the teaching strategy planning module generates a dynamic decision model by means of a large language model, a customized intervention strategy can be generated according to the learning condition and problem root of students, and the multi-agent collaborative reasoning module further improves the intelligent level of the system.

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.

The invention provides a smart home management method and system based on the Internet of Things. The method comprises the steps that indoor and outdoor environment parameters, user physiological data, home equipment operation states and energy consumption data are collected; based on the data, learning behavior preferences of the user in different time and environments, and establishing a personalized behavior prediction model; deploying the model at an edge computing node, combining big data analysis of a cloud server to form a hierarchical intelligent decision-making architecture, and outputting a preliminary decision-making result; the real intention of the user is understood and an intention confidence evaluation mechanism is established; when the consistency of the identification results of the multiple modes is lower than a threshold value, confirmation is actively carried out on the user, and a primary intention is obtained; according to the intention, a control strategy is dynamically generated in combination with the environment state and the equipment capacity; and based on the operation data and the energy consumption data, establishing an equipment health degree evaluation model, and predicting a fault risk and a maintenance demand. Through the scheme of the invention, the intention of the user can be accurately identified, and accurate personalized services are provided, so that the user experience is improved.

The invention relates to the technical field of artificial intelligence, and discloses an AI intelligent auxiliary question answering system for student practice. According to the system, various interactive data such as texts, voices and handwritten formula images of students are collected through a multi-modal data acquisition module, semantic feature vectors of all modals are generated through a multi-dimensional feature analysis module, and then the semantic feature vectors are mapped to a unified semantic space through a cross-modal fusion module. The double-layer graph construction module constructs a domain knowledge graph and a learning behavior graph, and the dynamic reasoning and recommendation module generates a personalized problem solving path recommendation and knowledge point completion strategy based on fusion semantic representation and a graph library. In addition, the system also has the functions of interactive behavior log anomaly detection, path backtracking, knowledge forgetting curve prediction and the like. The system can comprehensively understand questions of students, provides personalized question answering service, and effectively improves the learning efficiency and knowledge mastering degree of the students.

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

The invention discloses an interactive processing method and device based on course management, and relates to the technical field of intelligent education, and the method comprises the steps: collecting student information, carrying out the preprocessing of the student information, and generating an initial learning path according to the preprocessed student information; in the process that the student learns according to the initial learning path, learning behavior data of the student is monitored, and a personalized learning path is generated according to the learning behavior data of the student; monitoring emotion change data, cognitive load data and attention level data of the trainee in the personalized learning path in real time, constructing a learning state evaluation model, and evaluating the current learning state of the trainee; and dynamically generating an immersive learning situation according to the current learning state of the student. According to the invention, the pertinence and utilization efficiency of educational resources are improved, and the enthusiasm and effect of learning are enhanced.

The invention provides a learning data accurate mining method based on cognitive calculation driving. The learning data accurate mining method comprises the following steps of S1, performing multi-modal learning behavior data acquisition and heterogeneous integration; s2, a dynamic feature weight optimization step based on calculus; s3, performing cognitive state differential equation modeling; s4, a cognitive diagnosis hybrid model based on statistics; s5, incremental construction of the dynamic knowledge graph is carried out; s6, constructing a federated learning framework for privacy protection; s7, a cognitive intervention strategy is generated; s8, constructing a multi-granularity effect evaluation system; s9, a step of constructing an interpretability enhancement module; s10, a step of carrying out adaptive iterative optimization; the learning data accurate mining method based on cognitive calculation driving has the following advantages that the data utilization rate breaks through the limitation of a traditional method through federated learning and heterogeneous graph fusion; the attention prediction error is reduced through differential equation modeling, and the method is superior to all existing ARIMA / LSTM baseline models.

The invention relates to a multi-mode tumble detection method, device and equipment, and belongs to the technical field of intelligent nursing. The method comprises the steps of obtaining fall detection data; performing motion point extraction on the radar point cloud data based on time sequence analysis, screening out motion points whose positions change in continuous time, and generating motion point cloud data; projecting the moving point cloud data to a coordinate system corresponding to the thermal infrared imaging data to generate a radar point cloud projection matrix; combining the radar point cloud projection matrix with the thermal infrared imaging data, calculating the three-dimensional coordinates of the target, and generating target height change data; and when the target height change data meets a preset tumble condition, inputting the constructed time sequence multi-modal fusion data into a deep learning behavior recognition module, and outputting a classification result of tumble behaviors. According to the method, the radar sensor and the thermal infrared imaging data are fused, and time sequence analysis and deep learning behavior recognition are combined, so that the privacy of the user is protected while high-precision fall detection is realized.

The invention relates to the technical field of behavior portrait construction, and discloses a learner behavior portrait construction system and method based on data analysis, and the system comprises a learning behavior collection module, a learning rhythm mutation detection module, a behavior track stability evaluation module, a knowledge point backtracking index calculation module, and a portrait dynamic adjustment module. According to the method, the learning behavior fluctuation rate is calculated, the knowledge point jumping frequency and the task completion interval fluctuation condition are combined, the sudden change characteristics of the learning behaviors are judged, the recognition capacity for sudden learning behavior changes is enhanced, the behavior path overlapping degree of different time periods is compared, and the page switching frequency and the dwell time change range are calculated; the method comprises the steps of analyzing the stability of learning behaviors, performing hierarchical analysis on a knowledge point access mode of a learner according to a backtracking condition of a learning path, and optimizing the sensitivity of a portrait to learning path adjustment and enhancing the portrait adaptability by dynamically adjusting a portrait updating strategy and self-adapting to real-time behavior changes of the learner.

The invention belongs to the technical field of artificial intelligence education, and particularly relates to a student learning behavior prediction method based on artificial intelligence, and the method comprises the steps: obtaining student learning data and teacher teaching task data; constructing a student agent according to the student learning data, and constructing a teacher agent according to the teacher teaching task data; a dynamic teaching strategy generated by the teacher agent is input into the student agent, and the student agent predicts possible learning behavior change of the student based on a learned behavior mode in combination with a strategy-behavior causal model; the prediction result is linked with a teaching management mechanism, a dynamic learning file is generated, the teacher intelligent agent matches personalized learning resources through an intelligent recommendation algorithm according to the dynamic learning file, and the student intelligent agent receives feedback of students on the learning resources and performs targeted intervention actions in combination with feedback information. Therefore, the problems of insufficient causal reasoning ability, weak calibration ability, insufficient data fusion ability and the like in the prior art are solved.

The invention provides a course reconstruction method and system based on a knowledge graph, and relates to the technical field of intelligent education. The method comprises the following steps: collecting course resource data and student learning behavior data; constructing a knowledge graph fusing multiple courses; generating a personalized learning path based on the atlas and the behavior data; arranging experiment training tasks according to a path and collecting feedback data to update a graph state; and converting the student training result into a technical manuscript in a structured manner and accessing the technical manuscript to a management platform. Closed-loop optimization of teaching resource organization, path recommendation and achievement management is realized.

The invention relates to the technical field of test question generation, in particular to an intelligent test question generation method and system based on learning behavior analysis. The method comprises the following steps: acquiring interactive behavior data and score data of learners in a user online learning platform in real time; inputting the interactive behavior data and the score data into a pre-trained knowledge state analysis model to generate a user knowledge state matrix; based on the knowledge state matrix and in combination with a preset teaching target library, identifying a target knowledge point set which needs to be strengthened currently and a corresponding cognitive training type; according to the target knowledge point set needing to be strengthened and the corresponding cognitive training type, a test question element combination algorithm is called, question stems, interference items and question solving path prompts are dynamically assembled, and personalized test questions are generated. The method has the advantages that full-closed-loop intelligent teaching from behavior analysis of the user to targeted training is achieved, and personalized test questions adaptive to individual cognitive vulnerabilities are dynamically generated.

The invention provides a full-cycle intelligent education evaluation system based on a knowledge graph, and relates to the technical field of data processing, and the system is used for carrying out the correlation analysis of a learning behavior data set and a knowledge point data set of a student, calculating the mastering degree of the student on each knowledge point, recognizing the relation between the student and each knowledge point, and carrying out the calculation of the mastering degree. The method comprises the following steps: acquiring knowledge points of students, mapping the knowledge points into a graph structure to obtain a staged knowledge graph, updating the staged knowledge graph, adjusting the relationship between the students and each knowledge point to obtain a dynamic knowledge graph, extracting the real-time mastery degree of each knowledge point according to the dynamic knowledge graph, and periodically analyzing the knowledge points in combination with time dimensions. And obtaining periodic evaluation data, analyzing the mastering degree, the progress trend and the weak knowledge points of the students at each knowledge point, and generating an individual evaluation data set. According to the invention, the knowledge graph can be constructed to periodically evaluate the learning condition of the student.

The invention relates to the technical field of education informatization, in particular to an online learning behavior recognition method and system based on multi-view deep behavior recognition. According to the method, front and side RGB video data and skeleton data during online learning of a student are acquired, after preprocessing, feature vectors of the video and the skeleton data are extracted by using a deep learning technology, feature alignment of the video and the skeleton data is performed by using an inter-modal cyclic generative adversarial network, and a total fusion feature vector is obtained; and inputting the fusion feature vector into a multi-layer perceptron for classification, and finally outputting a learning behavior classification result of the student. According to the method, data of different view angles and modals can be effectively fused, and the accuracy and reliability of online learning behavior recognition are improved. Through combination of multi-view and multi-modal information, not only can behavior states of students be comprehensively captured, but also accurate data support can be provided for personalized learning recommendation, behavior prediction and the like.

The invention relates to a personalized scientific education system based on AI agent driving, and belongs to the technical field of informatics. The education system runs a main agent, a user twinborn agent, a learning planning platform and a graph structure database. The main agent is used for collecting knowledge states and learning behaviors of users, constructing a user model, and training one or more user twin agents based on the user model. And the user twinborn agent is used for simulating learning behaviors and results of the user under different learning paths. And the learning planning platform combines the user model and the knowledge graph stored in the graph structure database to generate a plurality of candidate learning paths for the twin intelligent agent of the user to perform analogue simulation. And the main agent selects an optimal path according to a simulation result and implements the optimal path to the personalized teaching process of the user. According to the technical scheme, high-adaptability learning path recommendation and dynamic adjustment are realized, and the intelligent level of individualized teaching is improved.

The invention discloses a student user portrait construction method based on multi-modal data fusion and a terminal. The method comprises the following steps: acquiring learning behavior data, social behavior data and hobby and interest data of a user, and respectively extracting corresponding feature information; calculating the fusion weight of the feature information of each mode and generating a comprehensive feature vector; and on the basis of the comprehensive feature vector, performing student user portrait construction by using a clustering algorithm, an analytic hierarchy process and knowledge graph analysis. According to the method, data features of learning behaviors, social behaviors and hobbies and interests are extracted, modal information is synthesized by using a fusion weight mechanism, and finally a multi-dimensional and fine-grained student portrait is formed. Compared with a traditional single data source modeling method, the method has the advantages that the comprehensiveness, the accuracy and the personalized adaptive capacity of portraits are remarkably improved, and various application scenes such as personalized teaching recommendation, academic early warning and interest expansion activity matching can be effectively supported.

Embodiments of the present disclosure relate to fine-tuning a neural network model using a weight-decomposed low-rank adaptation (DoRA). DoRA reduces the number of parameters that are fine-tuned, thereby reducing memory and the time needed to fine-tune the parameters. the Pre-trained weights are decomposed into two components, magnitude and direction, which are separately fine-tuned. The magnitude components are fine-tuned while the direction components remain unchanged (frozen). Then low-rank adaptation (LoRA) is used to fine-tune the direction components, efficiently minimizing the number of trainable parameters. Compared with using LoRA to fine-tune the weights directly, using DoRA exhibits a closer resemblance to full fine-tuning's learning behavior and improves upon LoRA in commonsense reasoning and visual instruction tuning tasks. By employing DoRA, both the learning capacity and training stability of LoRA is enhanced. The fine-tuned decomposed magnitude and direction components may be merged into the pre-trained weights to avoid any additional inference overhead.

The invention provides a recommendation method and system fusing big language model reasoning and multi-source trajectory information, and the method comprises the steps: firstly obtaining user historical learning behaviors and static attribute information after receiving a user recommendation request, and constructing a static interest vector; in combination with the initial feature vector of the learned knowledge point and the map enhancement vector of the first-order neighbor node of the knowledge map, generating explicit and map extension interest vectors, and fusing to obtain a user interest vector; screening N unlearned knowledge points to form a candidate set through similarity analysis of user interest vectors and unlearned knowledge point vectors and / or reasoning of a large language model on user association information; and screening the target knowledge points through mastery degree verification of the pre-modified knowledge points, and outputting a recommendation result after sorting. According to the method, recommendation accuracy and suitability are improved, and personalized learning requirements are met.

The invention provides a personalized learning path planning system and method, and belongs to the technical field of emerging software and emerging technical services, and the system comprises a data acquisition module which is used for collecting learning behavior data of a terminal user, obtaining a learner portrait package based on the learning behavior data, and sending the learner portrait package to a server; the path planning module is used for carrying out node matching on the learning ability feature vector and a pre-constructed knowledge and skill map, outputting a to-be-learned content node set, dividing learning advanced levels and generating learning path description containing to-be-learned nodes and the advanced levels, and the path generation module is used for generating a primary path planning scheme, the scheme feasibility verification module is used for carrying out scheme feasibility verification in combination with the learning advanced level and the path adaptation parameters, and then outputting a final executable path scheme, and the scheme execution module is used for executing the final executable path scheme and controlling learning content pushing and progress adjustment. The problems that in the prior art, personalized learning path planning is not high in precision, not high in adaptability, lack of dynamic optimization and the like are solved.

The invention discloses an intelligent agent-based large model education risk problem generation method, which is characterized in that a dynamic student intelligent agent is constructed to simulate a real learning behavior, and a potential risk problem in an education scene is automatically identified and generated in combination with the generation capability of a large language model; the method specifically comprises the steps of constructing a multi-dimensional student agent, constructing a risk problem generation rule base, generating potential risk problems and risk grade classification, optimizing risk problem generation quality, establishing an educational risk problem database, continuously updating and iterating and the like. Compared with the prior art, the method has the advantages that the safety and reliability of an AI education product are remarkably improved, the risk identification accuracy is remarkably improved along with the use time, an original solution is provided for safe development of the education science and technology field, healthy development of artificial intelligence in education application is guaranteed from the technical source, and the method is worthy of popularization and application. The method is widely applied to the fields of education content security auditing, self-adaptive learning system risk prevention and control and the like, and has good application prospects.

The invention relates to the technical field of data processing, in particular to a learning behavior data analysis system and method based on an agent, and the method comprises the steps: determining knowledge points in learning content, generating a visual knowledge point graph, testing the mastering degree of a learner for the knowledge points, generating a visual knowledge point mastering graph, and analyzing the knowledge points. The method comprises the steps of generating an optimal learning task which is most adaptive to the state of a learner based on the mastery degree of the learner and a learning behavior library, collecting real-time learning feature data of the learner during task execution, sensing the emotion and attention state of the learner in real time, and generating a personalized learning path in combination with a knowledge point graph, learning behavior analysis and the state of the learner. Learning tasks are dynamically adjusted according to real-time data, and learning strategies are optimized according to feedback of learners. Through dynamic perception and adjustment capability, the problem that a learning behavior analysis system in the prior art lacks real-time perception and dynamic adjustment of the state of a learner can be effectively solved.

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 reflection method and system based on intelligent classroom teaching, and relates to the technical field of intelligent classroom teaching. The method comprises the following steps: acquiring teaching outline information and teaching material structure information, and constructing a teaching knowledge graph; testing the learning style of the student to generate a learning style label; obtaining teaching activity content, calculating the adaptation degree between the teaching activity content and the learning style of the student, and generating a first teaching reflection result; acquiring learning behavior data and physiological performance data of the student, associating the learning behavior data and the physiological performance data with teaching knowledge graph nodes, evaluating the mastering level of the student on each knowledge node in the teaching knowledge graph, and generating a second teaching reflection result; and constructing a cognitive load prediction model, predicting and evaluating the cognitive load of the student, and generating a third teaching reflection result. According to the invention, a multi-dimensional, personalized and real-time teaching reflection scheme is constructed, and the effect and response capability of intelligent classroom teaching are remarkably improved.

The invention relates to the technical field of data processing, and provides an AI fool-proof algorithm and digital model optimization method and system, and the method comprises the steps: collecting multi-modal process data, carrying out the time alignment and event segmentation, obtaining a standard behavior sequence and an abnormal behavior sequence, inputting a preset deep learning behavior prediction model according to the standard behavior sequence, and carrying out the prediction of an abnormal behavior. And carrying out structure matching on the behavior prediction result and the abnormal behavior sequence to obtain an optimized instruction set and semantic prompt information, and then optimizing a preset deep learning behavior prediction model to obtain an optimized fool-proof intelligent prediction model. By optimizing an original deep learning behavior prediction model, the adaptive recognition capability and risk avoidance capability of the model for abnormal behaviors are improved, and the problem that in a complex practical application environment, high-frequency operation environment changes cannot be flexibly processed, so that part of high-risk behaviors cannot be recognized or corrected in time, and the risk of abnormal behaviors is reduced is solved. And the problems of low accuracy and poor adaptability exist.

The invention provides an intelligent agent generation method, a learning machine and electronic equipment. The generation method comprises the following steps: acquiring user input data and learning behavior data; processing the user input data to obtain a first feature vector, and processing the learning behavior data to obtain a second feature vector; fusing the first feature vector and the second feature vector to obtain a fused feature vector; inputting the fusion feature vector into a generator of a generative adversarial network to obtain a virtual image; and endowing the virtual image with an interaction behavior by adopting a machine learning algorithm according to the user input data and the learning behavior data to obtain an intelligent agent. According to the method, the fusion feature vector is obtained based on the input data and the learning behavior data, the virtual image is generated through the adversarial network generator, the virtual image meeting the emotional requirements of children can be generated in combination with the interests and characters of the children, and the interactive behaviors are generated based on the user input data and the learning behavior data, so that the actual requirements of the children can be better met.

The invention relates to the technical field of artificial intelligence education, and discloses an AI intelligent education tutoring system based on a thinking chain and a retrieval enhancement generation technology. Transparent generation and dynamic verification of a problem solving logic chain are realized through a CoT-RAG fusion architecture, and the interdisciplinary confusion rate is reduced in combination with a knowledge graph technology of subject boundary isolation; the non-question-bank-dependent generation engine creates questions in real time based on semantic association, and the coverage rate is increased; federated learning and differential privacy technologies are integrated, and the safety of user data is ensured while the generalization ability of the model is improved; the learning behavior analysis unit dynamically optimizes a knowledge recommendation path through time sequence modeling, and cooperates with the interactive logic correction module to form a self-evolution closed loop. Finally, an intelligent education system which is high in interpretability, high in professional precision and compliant in privacy is constructed, and the learning efficiency and the interdisciplinary problem solving capability are remarkably improved.

The invention relates to the technical field of online education, and discloses an online course learning management method based on a knowledge graph. The method comprises the following steps: acquiring multi-modal learning behavior data of a learner, and extracting a deep learning state vector reflecting knowledge understanding depth, learning input degree and cognitive confusion through semantic fusion; and dynamically calculating and updating the logical relationship strength among the knowledge points in the course knowledge graph by using the vector, so that the knowledge structure can adaptively evolve along with the actual cognitive state of the learning group. And generating a real-time personalized learning path based on the updated knowledge graph and the current state vector of the learner. Meanwhile, according to cognitive confusion features in the state vector, intervention measures such as pushing of remedial resources, adjusting of content sequence or starting of self-adaptive testing are triggered in real time. According to the method, the dynamic optimization of the knowledge graph and the accurate and immediate response of learning intervention are realized, and the adaptability and management efficiency of online learning are improved.

The invention provides a learning progress monitoring method and system for an online education platform, and is applied to the field of online data processing. Through multi-dimensional data acquisition and intelligent analysis, the learning behavior monitoring accuracy is improved, the learning achievement degree of the user is accurately judged, the evaluation objectivity is ensured, the learning behaviors of the user are comprehensively tracked in combination with multiple types of learning data, the limitation of single-dimensional evaluation is avoided, and the user experience is improved. Meanwhile, through monitoring of active learning, passive learning and non-learning behaviors, the learning state of the user is further refined, the authenticity of data is improved, personalized learning habit data of the user is collected, an Ebbinghaus forgetting curve is dynamically constructed, accurate review time recommendation is achieved, the review time period can be adaptively optimized in combination with the change trend of test results, and the review efficiency is improved. The user can review at the best time, and the knowledge retention rate is improved.